diff --git a/README.md b/README.md index d1f1276..5d60438 100644 --- a/README.md +++ b/README.md @@ -1,6 +1,6 @@ # Qwen3.8 Flash-Next on RTX 6000D -2026-09-18 已完成部署和验收。保留文字、图片、视频能力；采用 NVIDIA NVFP4 权重、原生 FP8 PLE 主机内存卸载、FP8 KV、128K 上下文及 FULL CUDA Graph。单个活动请求，MTP 暂不启用。 +2026-09-18 已完成部署和验收。保留文字、图片、视频能力；采用 NVIDIA NVFP4 权重、原生 FP8 PLE 主机内存卸载、FP8 KV、128K 上下文及 FULL CUDA Graph。单个活动请求，**MTP 2 已通过扩展配对验证并启用**，GPU 预算为 0.985。 社区代码经过审计后仅作为参考，没有执行社区启动脚本或使用社区镜像。审计范围和取舍见 [源码审计](audit/source-review.md)，实测数据见 [验收结果](audit/results.md)。 @@ -8,7 +8,8 @@ - `compose.yaml`、`Dockerfile`：已验收部署配置与固定镜像。 - `patches/`：最小补丁、精确源码保护及许可证。 -- [MTP 实测](docs/mtp-test.md)：约 1.93 倍短输出吞吐，需要更高显存预算，暂不设为默认。 +- [MTP 完整验证](docs/mtp-qualification.md)：长解码提升约 58%–67%，98 个负载请求及两组各 7 个质量任务通过；包含缓存延迟取舍。 +- [MTP 初测记录](docs/mtp-test.md)：历史短测试和当时的暂缓决定，已由完整验证更新。 - [部署心得](docs/lessons.md)：取舍、踩坑、优化方向及升级方法。 - `scripts/`：功能、上下文、速度和资源验证工具。 - `audit/runtime/`：2026-09-18 历史原始回执，不代表实时状态。 @@ -30,7 +31,7 @@ cd /data/flash-next/project python3 - <<'PY' import json, urllib.request from pathlib import Path -body = {'model': 'qwen3.8-flash-next', 'messages': [{'role': 'user', 'content': '你好，请介绍一下自己。'}], 'max_tokens': 1024, 'reasoning_effort': 'low'} +body = {'model': 'qwen3.8-flash-next', 'messages': [{'role': 'user', 'content': '你好，请介绍一下自己。'}], 'max_tokens': 1024, 'reasoning_effort': 'medium'} req = urllib.request.Request('http://127.0.0.1:8000/v1/chat/completions', data=json.dumps(body).encode(), headers={'Content-Type': 'application/json', 'Authorization': 'Bearer '+Path('secrets/api-key').read_text().strip()}) print(json.load(urllib.request.urlopen(req, timeout=180))) PY @@ -73,4 +74,8 @@ python3 scripts/long_context.py --tokens 128000 --output /data/flash-next/audit/ python3 scripts/benchmark.py --output /data/flash-next/audit/benchmark-new.jsonl ``` -脚本拒绝覆盖同名结果。长上下文验证为固定位置检索及前缀复用；多模态验证为合成图片和短视频；不代表综合质量评测或长期压力测试。当前基线优先单请求和多模态能力，额外并发、MTP、更长视频应另行测试。 +脚本拒绝覆盖同名结果。长上下文验证为固定位置检索及前缀复用；多模态验证为合成图片和短视频；不代表综合质量评测或长期压力测试。当前基线优先单请求和多模态能力，额外并发、更大或更长的视频应另行测试。 + +## 默认配置与回退 + +当前 `compose.yaml` 为 MTP 2，实际运行器使用 FULL_DECODE_ONLY Graph。`compose.baseline.yaml` 保留无 MTP 的 0.96 GPU 预算配置。长生成优先使用 MTP；大上下文反复短问答可能更适合无 MTP，因为可复用 KV 容量更大。切换方法和完整证据见 [MTP 完整验证](docs/mtp-qualification.md)。 diff --git a/audit/qualification/baseline-invalid-effort.jsonl b/audit/qualification/baseline-invalid-effort.jsonl new file mode 100644 index 0000000..688eaeb --- /dev/null +++ b/audit/qualification/baseline-invalid-effort.jsonl @@ -0,0 +1,33 @@ +{"name": "arithmetic-0", "passed": true, "finish": "stop", "usage": {"prompt_tokens": 59, "total_tokens": 141, "completion_tokens": 82, "completion_tokens_details": {"reasoning_tokens": 75}}, "ttft_s": 0.07772354000007908, "elapsed_s": 1.149622212000395, "decode_tps": 75.5668442510918, "max_stream_gap_s": 0.03973271099948761, "events": 79, "content": "\n\n3973", "reasoning": "I need to compute 137 * 29 + 0.\n\n137 * 29 = 137 * 30 - 137 = 4110 - 137 = 3973\n\nAdding 0 doesn't change anything, so the answer is 3973.\n", "sha256": "d929fd317464b07ece811a135b5937af8eb36ea0b1e88ec82bc5137102d0a62c"} +{"name": "arithmetic-1", "passed": true, "finish": "stop", "usage": {"prompt_tokens": 59, "total_tokens": 96, "completion_tokens": 37, "completion_tokens_details": {"reasoning_tokens": 30}}, "ttft_s": 0.07883534699976735, "elapsed_s": 0.5551211570000305, "decode_tps": 75.58486783383303, "max_stream_gap_s": 0.03967835599996761, "events": 34, "content": "\n\n5761", "reasoning": "160 * 36 = 5760. 5760 + 1 = 5761.\n", "sha256": "5cd050422245af84010bf1c71082001dcd6d880bdd6dd7002e69a8402ab6c73c"} +{"name": "arithmetic-2", "passed": true, "finish": "stop", "usage": {"prompt_tokens": 59, "total_tokens": 125, "completion_tokens": 66, "completion_tokens_details": {"reasoning_tokens": 59}}, "ttft_s": 0.07831667499976902, "elapsed_s": 0.9377429689993733, "decode_tps": 75.63184935557712, "max_stream_gap_s": 0.039701828999568534, "events": 63, "content": "\n\n7871", "reasoning": "183 * 43 = 183 * 40 + 183 * 3 = 7320 + 549 = 7869. Then 7869 + 2 = 7871.\n", "sha256": "c6dabe0673abae92ebdac3d245deb7c9ca89c874d316c46b8ce09fde923a752f"} +{"name": "arithmetic-3", "passed": true, "finish": "stop", "usage": {"prompt_tokens": 59, "total_tokens": 101, "completion_tokens": 42, "completion_tokens_details": {"reasoning_tokens": 34}}, "ttft_s": 0.07822468900030799, "elapsed_s": 0.6203575899999123, "decode_tps": 75.62721230237588, "max_stream_gap_s": 0.03962699600015185, "events": 39, "content": "\n\n10303", "reasoning": "206 * 50 = 10300, then 10300 + 3 = 10303.\n", "sha256": "faf8742035a51aed0549e27e042a16ddce400e4a9f6cb41b47540844a9fde0a7"} +{"name": "arithmetic-4", "passed": true, "finish": "stop", "usage": {"prompt_tokens": 59, "total_tokens": 131, "completion_tokens": 72, "completion_tokens_details": {"reasoning_tokens": 64}}, "ttft_s": 0.07851739099987753, "elapsed_s": 1.017388297999787, "decode_tps": 75.62275012533416, "max_stream_gap_s": 0.039650787999562453, "events": 69, "content": "\n\n13057", "reasoning": "229 * 57 = 229 * 50 + 229 * 7 = 11450 + 1603 = 13053. Then 13053 + 4 = 13057.\n", "sha256": "5c8a761b69d32970d9e4021e9cddda2caf44a6201a9bf34e503844cd2ce1dc0c"} +{"name": "arithmetic-5", "passed": true, "finish": "stop", "usage": {"prompt_tokens": 59, "total_tokens": 100, "completion_tokens": 41, "completion_tokens_details": {"reasoning_tokens": 33}}, "ttft_s": 0.07850254000004497, "elapsed_s": 0.6072512140008257, "decode_tps": 75.6503079191475, "max_stream_gap_s": 0.039656347000345704, "events": 38, "content": "\n\n16133", "reasoning": "252 * 64 = 16128. 16128 + 5 = 16133.\n", "sha256": "d7ffeebe2bcd01af72cd619ced3e33606c067dbbcee20babfbe1aded61293b92"} +{"name": "arithmetic-6", "passed": true, "finish": "stop", "usage": {"prompt_tokens": 59, "total_tokens": 130, "completion_tokens": 71, "completion_tokens_details": {"reasoning_tokens": 63}}, "ttft_s": 0.07873150400064333, "elapsed_s": 1.0037764430007883, "decode_tps": 75.67199932541767, "max_stream_gap_s": 0.03964858600011212, "events": 68, "content": "\n\n19531", "reasoning": "275 * 71 = 275 * 70 + 275 * 1 = 19250 + 275 = 19525. Then 19525 + 6 = 19531.\n", "sha256": "7ac565b6322b904ff447bc9ef3ed069ebc4e48b8a51e72f9e18919bd8e3000d1"} +{"name": "arithmetic-7", "passed": true, "finish": "stop", "usage": {"prompt_tokens": 59, "total_tokens": 153, "completion_tokens": 94, "completion_tokens_details": {"reasoning_tokens": 86}}, "ttft_s": 0.0784115259993996, "elapsed_s": 1.3077415259995178, "decode_tps": 75.65096434642534, "max_stream_gap_s": 0.03962907900040591, "events": 91, "content": "\n\n23251", "reasoning": "298 * 78 = 298 * 78. Let me compute this step by step.\n\n298 * 78 = 298 * 80 - 298 * 2 = 23840 - 596 = 23244\n\n23244 + 7 = 23251\n", "sha256": "e7b73eb708d2c4c7d2fc5dca2aed40a571c8b0445cd1867d916b47f57ec4cac0"} +{"name": "arithmetic-8", "passed": true, "finish": "stop", "usage": {"prompt_tokens": 59, "total_tokens": 144, "completion_tokens": 85, "completion_tokens_details": {"reasoning_tokens": 77}}, "ttft_s": 0.07822253599988471, "elapsed_s": 1.1883135919997585, "decode_tps": 75.6694683251367, "max_stream_gap_s": 0.03964279800038639, "events": 82, "content": "\n\n27293", "reasoning": "Let me compute 321 * 85 + 8.\n\n321 * 85 = 321 * 80 + 321 * 5 = 25680 + 1605 = 27285\n\n27285 + 8 = 27293\n", "sha256": "9d93671c15eac2005a27c4fe38e5d4285b3feca77fa1e90f138c71a2b5164db8"} +{"name": "arithmetic-9", "passed": true, "finish": "stop", "usage": {"prompt_tokens": 59, "total_tokens": 143, "completion_tokens": 84, "completion_tokens_details": {"reasoning_tokens": 76}}, "ttft_s": 0.0801680779995877, "elapsed_s": 1.1772930710003493, "decode_tps": 75.65227346884657, "max_stream_gap_s": 0.03965692699966894, "events": 81, "content": "\n\n31657", "reasoning": "Let me compute 344 * 92 + 9.\n\n344 * 92 = 344 * 90 + 344 * 2 = 30960 + 688 = 31648\n\n31648 + 9 = 31657\n", "sha256": "6b95a8b82a4ba5a8335b5f10899663aeebe71fed7e5673836b82ec74cfa1cbee"} +{"name": "arithmetic-10", "passed": true, "finish": "stop", "usage": {"prompt_tokens": 60, "total_tokens": 143, "completion_tokens": 83, "completion_tokens_details": {"reasoning_tokens": 75}}, "ttft_s": 0.07851070200013055, "elapsed_s": 1.1620557369997186, "decode_tps": 75.67751902442262, "max_stream_gap_s": 0.039518157000202336, "events": 80, "content": "\n\n36343", "reasoning": "Let me compute 367 * 99 + 10.\n\n367 * 99 = 367 * (100 - 1) = 36700 - 367 = 36333\n\n36333 + 10 = 36343\n", "sha256": "662f32768d742ea67e45b8109190fc2ba55c429346f4be9b9492ff457cdacc48"} +{"name": "arithmetic-11", "passed": true, "finish": "stop", "usage": {"prompt_tokens": 61, "total_tokens": 151, "completion_tokens": 90, "completion_tokens_details": {"reasoning_tokens": 82}}, "ttft_s": 0.07955242300067766, "elapsed_s": 1.2561235120001584, "decode_tps": 75.64353810162275, "max_stream_gap_s": 0.03971186299986584, "events": 87, "content": "\n\n41351", "reasoning": "Let me compute 390 * 106 + 11.\n\n390 * 106 = 390 * 100 + 390 * 6 = 39000 + 2340 = 41340\n\n41340 + 11 = 41351\n", "sha256": "cdbf70494a3484ed891137af4bad12b7417009befd921930fc1fa5e5e3907e27"} +{"name": "json", "passed": true, "finish": "stop", "usage": {"prompt_tokens": 66, "total_tokens": 109, "completion_tokens": 43, "completion_tokens_details": {"reasoning_tokens": 26}}, "ttft_s": 0.09644218499943236, "elapsed_s": 0.651459989999239, "decode_tps": 75.6732479960974, "max_stream_gap_s": 0.03967342000032659, "events": 40, "content": "\n\n{\"name\":\"设备A\",\"count\":7,\"enabled\":true}", "reasoning": "The user wants me to return exactly a specific JSON object with no markdown formatting. Let me provide exactly what they asked for.\n", "sha256": "2acba5868b21fa90ff7d281b66898b4ed66b6ff2f42a3d5a1d5bd11569806949"} +{"name": "multiturn", "passed": true, "finish": "stop", "usage": {"prompt_tokens": 82, "total_tokens": 146, "completion_tokens": 64, "completion_tokens_details": {"reasoning_tokens": 52}}, "ttft_s": 0.07780361999994057, "elapsed_s": 0.9104519810007332, "decode_tps": 75.66219180961076, "max_stream_gap_s": 0.039605606999430165, "events": 61, "content": "\n\nAQUA-58321", "reasoning": "The user is asking me to recall the code they previously told me to remember. They said \"Remember the code AQUA-58321.\" and now they're asking what the code was. They want me to reply only with the code.\n", "sha256": "5a11c72c78b0b7d9d0ec352a11aa21a4fa1ea34ff915277ee160bbb83bbb1dd6"} +{"name": "image-ocr", "passed": true, "finish": "stop", "usage": {"prompt_tokens": 3135, "total_tokens": 3315, "completion_tokens": 180, "completion_tokens_details": {"reasoning_tokens": 138}}, "ttft_s": 0.7455413950001457, "elapsed_s": 3.1262464499995986, "decode_tps": 75.18781027666661, "max_stream_gap_s": 0.03989871899921127, "events": 177, "content": "\n\nVerification code: **SPARK-82741**\n\nSum of amounts: 125 + 478 + 765 = **1368**", "reasoning": "The user wants the verification code and the sum of three amounts from the invoice image. Reading the image: Verification: SPARK-82741. Amount A: 125, Amount B: 478, Amount C: 765. Sum: 125 + 478 = 603; 603 + 765 = 1368. I need to provide a self-contained reply with the code and the sum. Reasoning should show entropy reduction—confirming each value and the arithmetic. Final answer: SPARK-82741 and 1368.\n", "sha256": "64d438bdfb6d81cbaa352b6ee35b7a538b4e46438117f85e1d529ff210e9a90d"} +{"name": "video-120s", "passed": true, "finish": "stop", "usage": {"prompt_tokens": 12069, "total_tokens": 12183, "completion_tokens": 114, "completion_tokens_details": {"reasoning_tokens": 100}}, "ttft_s": 2.4627599759996883, "elapsed_s": 3.9740706529992167, "decode_tps": 74.76953727630898, "max_stream_gap_s": 0.040032412999607914, "events": 111, "content": "\n\nRed, Green, Blue, Yellow, Purple, White", "reasoning": "The user wants the six background colors in chronological order from the video frames. Looking at the timestamps: 0-19s RED, 20-39s GREEN, 40-59s BLUE, 60-79s YELLOW, 80-99s PURPLE, 100-119s WHITE. That's six colors. I need to reply only with the English color names, in order. No extra text.\n", "sha256": "53ddddc25a28dbc73b210512f71d35f5b4daee20506cee8470192ce5d98d945d"} +{"name": "multi-image", "passed": true, "finish": "stop", "usage": {"prompt_tokens": 12356, "total_tokens": 12427, "completion_tokens": 71, "completion_tokens_details": {"reasoning_tokens": 60}}, "ttft_s": 1.4047450600000957, "elapsed_s": 2.3313891259995216, "decode_tps": 75.54141074059753, "max_stream_gap_s": 0.03995001000021148, "events": 68, "content": "\n\nSPARK-82741", "reasoning": "The user wants the verification code from the invoice. All four copies show the same line: \"Verification: SPARK-82741\". The instruction is to reply only with the code. So the answer is SPARK-82741. No need for elaboration.\n", "sha256": "c4d4ac93f59330deca1f3c5fe9842a60a2ac31170fdbc40f311385c571ed9024"} +{"name": "warmup-code", "passed": false, "error": "HTTP Error 400: Bad Request", "elapsed_s": 0.005225435999818728} +{"name": "perf-code-0", "passed": false, "error": "HTTP Error 400: Bad Request", "elapsed_s": 0.0015374110007542185} +{"name": "perf-code-1", "passed": false, "error": "HTTP Error 400: Bad Request", "elapsed_s": 0.0014719609998792293} +{"name": "perf-code-2", "passed": false, "error": "HTTP Error 400: Bad Request", "elapsed_s": 0.0015831439995963592} +{"name": "warmup-chinese", "passed": false, "error": "HTTP Error 400: Bad Request", "elapsed_s": 0.0012888200008092099} +{"name": "perf-chinese-0", "passed": false, "error": "HTTP Error 400: Bad Request", "elapsed_s": 0.0015359890003310284} +{"name": "perf-chinese-1", "passed": false, "error": "HTTP Error 400: Bad Request", "elapsed_s": 0.0013069549995634588} +{"name": "perf-chinese-2", "passed": false, "error": "HTTP Error 400: Bad Request", "elapsed_s": 0.0015163429998210631} +{"name": "warmup-reasoning", "passed": false, "error": "HTTP Error 400: Bad Request", "elapsed_s": 0.0012579570002344553} +{"name": "perf-reasoning-0", "passed": false, "error": "HTTP Error 400: Bad Request", "elapsed_s": 0.001492990000770078} +{"name": "perf-reasoning-1", "passed": false, "error": "HTTP Error 400: Bad Request", "elapsed_s": 0.0012681409998549498} +{"name": "perf-reasoning-2", "passed": false, "error": "HTTP Error 400: Bad Request", "elapsed_s": 0.0014675349993922282} +{"name": "long-output-16384", "passed": false, "error": "HTTP Error 400: Bad Request", "elapsed_s": 0.0012407340000208933} +{"name": "long-context-0", "passed": true, "finish": "stop", "usage": {"prompt_tokens": 125995, "total_tokens": 126124, "completion_tokens": 129, "completion_tokens_details": {"reasoning_tokens": 109}}, "ttft_s": 13.898595239000315, "elapsed_s": 15.572495153999625, "decode_tps": 76.46813220613176, "max_stream_gap_s": 0.03968665800039162, "events": 126, "content": "\n\nNORTH-49273\nSOUTH-18362", "reasoning": "The user is asking me to provide the northern and southern access codes that are embedded in the data. Let me find them:\n\n1. \"The northern access code is NORTH-49273.\" - found between Item 000019 and Item 000021\n2. \"The southern access code is SOUTH-18362.\" - found between Item 002499 and Item 002501\n\nThe user wants only these two codes.\n", "sha256": "120d58d463e19b76220422b4b9e9ae6449cf6f89f796335bd1cbe86ec55dcc05"} +{"name": "long-context-1", "passed": true, "finish": "stop", "usage": {"prompt_tokens": 125995, "total_tokens": 126112, "completion_tokens": 117, "completion_tokens_details": {"reasoning_tokens": 97}}, "ttft_s": 0.24017905600067024, "elapsed_s": 1.7552992750006524, "decode_tps": 76.56158141468335, "max_stream_gap_s": 0.03971610900043743, "events": 114, "content": "\n\nNORTH-49273\nSOUTH-18362", "reasoning": "The user is asking me to provide both the northern and southern access codes from the data provided.\n\nLooking through the data, I can find:\n- \"The northern access code is NORTH-49273.\" (appears after Item 000019)\n- \"The southern access code is SOUTH-18362.\" (appears after Item 002499)\n\nThe user wants only these two codes.\n", "sha256": "7f8768048dbe7257ce2b8a8b9aa144900f50a7a3a2eae9d79f0120f2323e42d7"} +{"name": "long-context-2", "passed": true, "finish": "stop", "usage": {"prompt_tokens": 125995, "total_tokens": 126117, "completion_tokens": 122, "completion_tokens_details": {"reasoning_tokens": 102}}, "ttft_s": 0.22724735799965856, "elapsed_s": 1.8088994879999518, "decode_tps": 76.50228372276625, "max_stream_gap_s": 0.03969127400068828, "events": 119, "content": "\n\nNORTH-49273\nSOUTH-18362", "reasoning": "The user is asking me to provide both the northern and southern access codes that are embedded in the data.\n\nLooking through the data:\n- \"The northern access code is NORTH-49273.\" (found between Item 000019 and Item 000021)\n- \"The southern access code is SOUTH-18362.\" (found between Item 002499 and Item 002501)\n", "sha256": "27006fc14e3dcb44599f4eaf64b2b32126e28e70c7dbca114c947205a75d4094"} diff --git a/audit/qualification/baseline-merge-recheck.jsonl b/audit/qualification/baseline-merge-recheck.jsonl new file mode 100644 index 0000000..09e0e83 --- /dev/null +++ b/audit/qualification/baseline-merge-recheck.jsonl @@ -0,0 +1 @@ +{"name": "merge-intervals", "passed": false, "error": "the JSON object must be str, bytes or bytearray, not NoneType", "response": {"id": "chatcmpl-a7553ed17f1da7e1", "object": "chat.completion", "created": 1789740677, "model": "qwen3.8-flash-next", "choices": [{"index": 0, "message": {"role": "assistant", "content": null, "refusal": null, "annotations": null, "audio": null, "function_call": null, "reasoning": "We need answer user's request. Need produce final ONLY JSON object with a code string. Need satisfy constraints: Write Python function merge_intervals. Merge overlapping integer intervals, including touching endpoints. Return sorted merged intervals as lists. Reply ONLY a JSON object with a code string. Use no imports, type annotations, decorators, top-level statements or helper functions. Allowed builtins: sorted,len,range,min,max,list,enumerate,sum. Allowed methods: append,sort,copy.\n\nNeed output JSON object likely {\"code\": \"def merge_intervals(...): ...\"}? They say code string. Need only JSON object. Need ensure no imports, type annotations, decorators, top-level statements or helper functions. Function only. Allowed builtins only sorted,len,range,min,max,list,enumerate,sum. Allowed methods append,sort,copy. Need no other builtins? Avoid bool? if statements okay? Need maybe use sorted. Need merge intervals input likely list of intervals [start,end]. Touching endpoints means [1,2] and [2,3] merge. Return sorted merged intervals as lists. Need handle empty input. Need no type annotations. Could use sorted(intervals, key=lambda x: x[0])? lambda is not builtin? It's allowed? They didn't list lambda. They said allowed builtins: sorted,len,range,min,max,list,enumerate,sum. Allowed methods: append,sort,copy. Lambda maybe not allowed? Need avoid lambda? sorted can sort list of lists lexicographically by default, which sorts by start then end. That's okay. Use sorted(intervals). But if intervals are tuples? Return lists. sorted returns list of original elements. If input tuples, need convert to list? Could use list(interval) maybe allowed builtin list. But if sorted returns tuples, we can append list(interval). Need no helper.\n\nPotential implementation:\ndef merge_intervals(intervals):\n if not intervals:\n return []\n sorted_intervals = sorted(intervals)\n merged = []\n current = list(sorted_intervals[0])\n for i in range(1, len(sorted_intervals)):\n interval = list(sorted_intervals[i])\n if interval[0] <= current[1]:\n if interval[1] > current[1]:\n current[1] = interval[1]\n else:\n merged.append(current)\n current = interval\n merged.append(current)\n return merged\n\nUses if, not, <=, >. Builtins sorted,len,range,list. Methods append. No type annotations. No imports. Top-level only function. Is `not` allowed? It's operator. Fine.\n\nNeed JSON string escaping. Could output {\"code\":\"def merge_intervals(intervals):\\n if not intervals:\\n return []\\n sorted_intervals = sorted(intervals)\\n merged = []\\n current = list(sorted_intervals[0])\\n for i in range(1, len(sorted_intervals)):\\n interval = list(sorted_intervals[i])\\n if interval[0] <= current[1]:\\n if interval[1] > current[1]:\\n current[1] = interval[1]\\n else:\\n merged.append(current)\\n current = interval\\n merged.append(current)\\n return merged\"}\n\nNeed ensure no top-level statements. JSON only. Could include code string with newline escapes. Need valid JSON. Need maybe no trailing newline? okay.\n\nCheck constraints: Allowed builtins: sorted,len,range,min,max,list,enumerate,sum. We use sorted, len, range, list. Good. Methods append. Good. No helper functions. No type annotations. No imports. No decorators. No top-level statements outside function.\n\nPotential issue: sorted(intervals) if intervals contains lists of different lengths? Intervals are integer intervals likely [start,end]. If empty? handled. If intervals are tuples, list conversion. If intervals are lists, list copies. Good.\n\nTouching endpoints: condition interval[0] <= current[1]. Good.\n\nReturn sorted merged intervals as lists. Since sorted by start, merged appended in order. Good.\n\nNeed maybe if input intervals unsorted and negative? sorted works.\n\nCould use min/max? not needed.\n\nNeed final only JSON object. Ensure no markdown. Need maybe code string includes function only. JSON key? They didn't specify key. \"code string\" likely {\"code\": \"...\"}. Use that.\n\nDouble-check JSON escaping: backslashes for newlines. Need quotes inside code? none. Good.\n\nCould there be issue with `if not intervals:` uses truthiness, no builtin. okay.\n\nNeed maybe avoid `sorted_intervals[0]` if intervals empty handled. Good.\n\nNeed maybe if intervals is generator? sorted consumes. len works on sorted list. Good.\n\nNeed maybe if intervals contains [2,1]? Not specified. Could assume valid. If not, maybe normalize? Could use min/max allowed. But not necessary. Could robustly normalize: start = min(interval[0], interval[1]); end = max(...). But then sorted? If input invalid, sorted by first may not sort by min. Could normalize before sorting? Need no helper. Could do:\n normalized = []\n for interval in sorted(intervals):\n start = min(interval[0], interval[1])\n end = max(...)\n normalized.append([start,end])\n if not normalized: return []\n current = normalized[0]\n for i in range(1, len(normalized)): ...\nBut uses sorted before empty? sorted([]) okay. But if intervals empty, normalized empty. Need no top-level. This uses min,max allowed. But sorted(intervals) with invalid intervals [2,1] and [0,5] sorted lexicographically [0,5], [2,1] okay. But if [2,1] and [1,0]? sorted [1,0], [2,1], normalized [0,1], [1,2] merge. If [2,1] and [0,0] sorted [0,0], [2,1] normalized [0,0], [1,2] okay. If [2,1] and [1,3] sorted [1,3], [2,1] normalized [1,3], [1,2] okay. But if [2,1] and [0,10] sorted [0,10], [2,1] okay. If [2,1] and [1,0] sorted [1,0], [2,1] normalized [0,1], [1,2]. Good. But if [2,1] and [0,0]? sorted [0,0], [2,1]. Good. If [2,1] and [1,1]? sorted [1,1], [2,1] normalized [1,1], [1,2]. Good. If [2,1] and [0,2]? sorted [0,2], [2,1]. Good. If [2,1] and [1,2]? sorted [1,2], [2,1] normalized [1,2], [1,2]. Good. But if [2,1] and [0,1]? sorted [0,1], [2,1]. Good.\n\nHowever if invalid intervals with start > end, sorting by original first may not sort by normalized start. Example [10,0] and [1,2]. sorted -> [1,2], [10,0]; normalized -> [1,2], [0,10]. Then merge loop sees [0,10] after [1,2], interval[0]=0 <= current[1]=2, extends current[1]=10, merged remains [1,10] but sorted merged should [0,10]. So not robust. Could normalize first then sort. Need no helper. Use list comprehension? Not listed? Could use for loop. But allowed builtins include list, enumerate, sum, min, max. Could do:\ndef merge_intervals(intervals):\n normalized = []\n for interval in intervals:\n start = min(interval[0], interval[1])\n end = max(interval[0], interval[1])\n normalized.append([start, end])\n if not normalized:\n return []\n normalized.sort()\n merged = []\n current = normalized[0]\n for i in range(1, len(normalized)):\n interval = normalized[i]\n if interval[0] <= current[1]:\n if interval[1] > current[1]:\n current[1] = interval[1]\n else:\n merged.append(current)\n current = interval\n merged.append(current)\n return merged\n\nUses sort method allowed. No sorted builtin needed. But if intervals is generator, for interval in intervals okay. If intervals is list, okay. If intervals is None? not specified. If intervals empty, normalized empty. Good. This uses min,max. Allowed. It mutates current which is normalized[0]; okay. It returns lists. It sorts normalized in place. It handles invalid intervals. It uses no lambda. It uses `if not normalized`. Good.\n\nBut if input intervals are tuples, interval[0] works. normalized append lists. Good.\n\nNeed consider if intervals contains empty list? interval[0] error. Not specified.\n\nCould use sorted(normalized) instead of normalized.sort. Both allowed. sort method allowed. Good.\n\nNeed maybe if intervals is not iterable? not specified.\n\nWhich implementation better? The normalized robust one uses min/max allowed. It uses for interval in intervals (no builtin). Good. It uses `normalized.sort()` method allowed. It uses append. Good.\n\nNeed ensure no type annotations. Good.\n\nPotential issue: If intervals is a list of lists, normalized.sort sorts by start then end. Good.\n\nPotential issue: If intervals is a tuple of tuples, for interval in intervals okay. If intervals is a set, okay. If intervals is generator, okay. If intervals is None, error. okay.\n\nNeed maybe if intervals contains [1,2] and [2,3], normalized sorted, current [1,2], interval [2,3], interval[0] <= current[1] true, interval[1] > current[1] true, current[1]=3. Good.\n\nNeed maybe if interval contained [1,5] and [2,3], current[1] remains 5. Good.\n\nNeed maybe if interval contained [1,2] and [3,4] not touching? 3 <= 2 false, separate. Good.\n\nNeed maybe if interval contained [1,2] and [2,2] touching, merge. Good.\n\nNeed maybe if interval contained [1,1] and [1,1], merge. Good.\n\nNeed maybe if intervals empty returns []. Good.\n\nNeed output JSON only. Could include code string with normalized robust. Need ensure JSON valid. Let's craft:\n{\n \"code\": \"def merge_intervals(intervals):\\n normalized = []\\n for interval in intervals:\\n start = min(interval[0], interval[1])\\n end = max(interval[0], interval[1])\\n normalized.append([start, end])\\n if not normalized:\\n return []\\n normalized.sort()\\n merged = []\\n current = normalized[0]\\n for i in range(1, len(normalized)):\\n interval = normalized[i]\\n if interval[0] <= current[1]:\\n if interval[1] > current[1]:\\n current[1] = interval[1]\\n else:\\n merged.append(current)\\n current = interval\\n merged.append(current)\\n return merged\"\n}\n\nNeed maybe no spaces? JSON okay. They said Reply ONLY a JSON object. Could be compact. Use compact to avoid pretty? Pretty is JSON object. But only JSON. Fine.\n\nNeed ensure no top-level statements in code string. It's function only. Good.\n\nNeed maybe allowed builtins: min,max,list? We don't use list builtin except append? We use [] literal. Is [] literal allowed? They didn't forbid. Allowed builtins list maybe for conversion. Using [] literal okay? They said allowed builtins: sorted,len,range,min,max,list,enumerate,sum. Does that mean only those builtins can be used, but literals okay. Yes.\n\nCould there be issue with `min` and `max` if interval is not length 2? Not specified.\n\nNeed maybe if intervals is empty but not iterable? no.\n\nNeed maybe if intervals is a list of lists and we normalize, we create new lists. Good.\n\nNeed maybe if intervals is a list of lists and we mutate current which is normalized[0], then later merged.append(current) appends reference. No further mutation after append? current reassigned. Good.\n\nNeed maybe if intervals is a list of lists and normalized.sort sorts lists in place. Good.\n\nNeed maybe if intervals is a list of tuples and normalized contains lists. Good.\n\nNeed maybe if intervals is a list of lists and we want return sorted merged intervals as lists. Good.\n\nNeed maybe if intervals is a list of lists and input intervals are [1,2], [2,3], output [[1,3]]. Good.\n\nNeed maybe if intervals is a list of lists and input intervals are [[1,2], [2,3]]? Then interval[0] is [1,2], min([1,2], [2,3])? Python min compares lists, returns [1,2], max returns [2,3], then interval[0] later? Actually if nested list, not valid. Not worry.\n\nNeed maybe if intervals is a list of lists and we use min(interval[0], interval[1]) where interval[0] and interval[1] ints. Good.\n\nNeed maybe if intervals is a list of lists and interval[0] > interval[1], normalized. Good.\n\nNeed maybe if intervals is a list of lists and interval[0] == interval[1]. Good.\n\nNeed maybe if intervals is a list of lists and interval[0] or interval[1] not int? Not specified.\n\nNeed maybe if intervals is a list of lists and interval[0] <= current[1] with ints. Good.\n\nNeed maybe if intervals is a list of lists and current[1] updated. Good.\n\nNeed maybe if intervals is a list of lists and normalized.sort uses list comparison. Good.\n\nNeed maybe if intervals is a list of lists and normalized empty. Good.\n\nNeed maybe if intervals is a list of lists and normalized has one. merged append current. Good.\n\nNeed maybe if intervals is a list of lists and normalized has multiple. Good.\n\nNeed maybe if intervals is a list of lists and normalized has [0,10] and [1,2] after sort? sort puts [0,10] first. Good.\n\nNeed maybe if intervals is a list of lists and normalized has [0,10] and [10,20], merge. Good.\n\nNeed maybe if intervals is a list of lists and normalized has [0,10] and [11,20], separate. Good.\n\nNeed maybe if intervals is a list of lists and normalized has [0,10] and [9,10], merge. Good.\n\nNeed maybe if intervals is a list of lists and normalized has [0,10] and [10,10], merge. Good.\n\nNeed maybe if intervals is a list of lists and normalized has [0,10] and [10,9] normalized [9,10], merge. Good.\n\nNeed maybe if intervals is a list of lists and normalized has [0,10] and [10,11], merge. Good.\n\nNeed maybe if intervals is a list of lists and normalized has [0,10] and [11,10] normalized [10,11], merge. Good.\n\nNeed maybe if intervals is a list of lists and normalized has [0,10] and [12,11] normalized [11,12], separate. Good.\n\nNeed maybe if intervals is a list of lists and normalized has [0,10] and [10,12] merge. Good.\n\nNeed maybe if intervals is a list of lists and normalized has [0,10] and [10,10] merge. Good.\n\nNeed maybe if intervals is a list of lists and normalized has [0,10] and [10,9] normalized [9,10] merge. Good.\n\nNeed maybe if intervals is a list of lists and normalized has [0,10] and [10,11] merge. Good.\n\nNeed maybe if intervals is a list of lists and normalized has [0,10] and [11,10] normalized [10,11] merge. Good.\n\nNeed maybe if intervals is a list of lists and normalized has [0,10] and [12,11] normalized [11,12] separate. Good.\n\nNeed maybe if intervals is a list of lists and normalized has [0,10] and [10,12] merge. Good.\n\nNeed maybe if intervals is a list of lists and normalized has [0,10] and [10,10] merge. Good.\n\nNeed maybe if intervals is a list of lists and normalized has [0,10] and [10,9] normalized [9,10] merge. Good.\n\nNeed maybe if intervals is a list of lists and normalized has [0,10] and [10,11] merge. Good.\n\nNeed maybe if intervals is a list of lists and normalized has [0,10] and [11,10] normalized [10,11] merge. Good.\n\nNeed maybe if intervals is a list of lists and normalized has [0,10] and [12,11] normalized [11,12] separate. Good.\n\nNeed maybe if intervals is a list of lists and normalized has [0,10] and [10,12] merge. Good.\n\nNeed maybe if intervals is a list of lists and normalized has [0,10] and [10,10] merge. Good.\n\nNeed maybe if intervals is a list of lists and normalized has [0,10] and [10,9] normalized [9,10] merge. Good.\n\nNeed maybe if intervals is a list of lists and normalized has [0,10] and [10,11] merge. Good.\n\nNeed maybe if intervals is a list of lists and normalized has [0,10] and [11,10] normalized [10,11] merge. Good.\n\nNeed maybe if intervals is a list of lists and normalized has [0,10] and [12,11] normalized [11,12] separate. Good.\n\nNeed maybe if intervals is a list of lists and normalized has [0,10] and [10,12] merge. Good.\n\nNeed maybe if intervals is a list of lists and normalized has [0,10] and [10,10] merge. Good.\n\nNeed maybe if intervals is a list of lists and normalized has [0,10] and [10,9] normalized [9,10] merge. Good.\n\nNeed maybe if intervals is a list of lists and normalized has [0,10] and [10,11] merge. Good.\n\nNeed maybe if intervals is a list of lists and normalized has [0,10] and [11,10] normalized [10,11] merge. Good.\n\nNeed maybe if intervals is a list of lists and normalized has [0,10] and [12,11] normalized [11,12] separate. Good.\n\nNeed maybe if intervals is a list of lists and normalized has [0,10] and [10,12] merge. Good.\n\nNeed maybe if intervals is a list of lists and normalized has [0,10] and [10,10] merge. Good.\n\nNeed maybe if intervals is a list of lists and normalized has [0,10] and [10,9] normalized [9,10] merge. Good.\n\nNeed maybe if intervals is a list of lists and normalized has [0,10] and [10,11] merge. Good.\n\nNeed maybe if intervals is a list of lists and normalized has [0,10] and [11,10] normalized [10,11] merge. Good.\n\nNeed maybe if intervals is a list of lists and normalized has [0,10] and [12,11] normalized [11,12] separate. Good.\n\nNeed maybe if intervals is a list of lists and normalized has [0,10] and [10,12] merge. Good.\n\nNeed maybe if intervals is a list of lists and normalized has [0,10] and [10,10] merge. Good.\n\nNeed maybe if intervals is a list of lists and normalized has [0,10] and [10,9] normalized [9,10] merge. Good.\n\nNeed maybe if intervals is a list of lists and normalized has [0,10] and [10,11] merge. Good.\n\nNeed maybe if intervals is a list of lists and normalized has [0,10] and [11,10] normalized [10,11] merge. Good.\n\nNeed maybe if intervals is a list of lists and normalized has [0,10] and [12,11] normalized [11,12] separate. Good.\n\nNeed maybe if intervals is a list of lists and normalized has [0,10] and [10,12] merge. Good.\n\nNeed maybe if intervals is a list of lists and normalized has [0,10] and [10,10] merge. Good.\n\nNeed maybe if intervals is a list of lists and normalized has [0,10] and [10,9] normalized [9,10] merge. Good.\n\nNeed maybe if intervals is a list of lists and normalized has [0,10] and [10,11] merge. Good.\n\nNeed maybe if intervals is a list of lists and normalized has [0,10] and [11,10] normalized [10,11] merge. Good.\n\nNeed maybe if intervals is a list of lists and normalized has [0,10] and [12,11] normalized [11,12] separate. Good.\n\nNeed maybe if intervals is a list of lists and normalized has [0,10] and [10,12] merge. Good.\n\nNeed maybe if intervals is a list of lists and normalized has [0,10] and [10,10] merge. Good.\n\nNeed maybe if intervals is a list of lists and normalized has [0,10] and [10,9] normalized [9,10] merge. Good.\n\nNeed maybe if intervals is a list of lists and normalized has [0,10] and [10,11] merge. Good.\n\nNeed maybe if intervals is a list of lists and normalized has [0,10] and [11,10] normalized [10,11] merge. Good.\n\nNeed maybe if intervals is a list of lists and normalized has [0,10] and [12,11] normalized [11,12] separate. Good.\n\nNeed maybe if intervals is a list of lists and normalized has [0,10] and [10,12] merge. Good.\n\nNeed maybe if intervals is a list of lists and normalized has [0,10] and [10,10] merge. Good.\n\nNeed maybe if intervals is a list of lists and normalized has [0,10] and [10,9] normalized [9,10] merge. Good.\n\nNeed maybe if intervals is a list of lists and normalized has [0,10] and [10,11] merge. Good.\n\nNeed maybe if intervals is a list of lists and normalized has [0,10] and [11,10] normalized [10,11] merge. Good.\n\nNeed maybe if intervals is a list of lists and normalized has [0,10] and [12,11] normalized [11,12] separate. Good.\n\nNeed maybe if intervals is a list of lists and normalized has [0,10] and [10,12] merge. Good.\n\nNeed maybe if intervals is a list of lists and normalized has [0,10] and [10,10] merge. Good.\n\nNeed maybe if intervals is a list of lists and normalized has [0,10] and [10,9] normalized [9,10] merge. Good.\n\nNeed maybe if intervals is a list of lists and normalized has [0,10] and [10,11] merge. Good.\n\nNeed maybe if intervals is a list of lists and normalized has [0,10] and [11,10] normalized [10,11] merge. Good.\n\nNeed maybe if intervals is a list of lists and normalized has [0,10] and [12,11] normalized [11,12] separate. Good.\n\nNeed maybe if intervals is a list of lists and normalized has [0,10] and [10,12] merge. Good.\n\nNeed maybe if intervals is a list of lists and normalized has [0,10] and [10,10] merge. Good.\n\nNeed maybe if intervals is a list of lists and normalized has [0,10] and [10,9] normalized [9,10] merge. Good.\n\nNeed maybe if intervals is a list of lists and normalized has [0,10] and [10,11] merge. Good.\n\nNeed maybe if intervals is a list of lists and normalized has [0,10] and [11,10] normalized [10,11] merge. Good.\n\nNeed maybe if intervals is a list of lists and normalized has [0,10] and [12,11] normalized [11,12] separate. Good.\n\nNeed maybe if intervals is a list of lists and normalized has [0,10] and [10,12] merge. Good.\n\nNeed maybe if intervals is a list of lists and normalized has [0,10] and [10,10] merge. Good.\n\nNeed maybe if intervals is a list of lists and normalized has [0,10] and [10,9] normalized [9,10] merge. Good.\n\nNeed maybe if intervals is a list of lists and normalized has [0,10] and [10,11] merge. Good.\n\nNeed maybe if intervals is a list of lists and normalized has [0,10] and [11,10] normalized [10,11] merge. Good.\n\nNeed maybe if intervals is a list of lists and normalized has [0,10] and [12,11] normalized [11,12] separate. Good.\n\nNeed maybe if intervals is a list of lists and normalized has [0,10] and [10,12] merge. Good.\n\nNeed maybe if intervals is a list of lists and normalized has [0,10] and [10,10] merge. Good.\n\nNeed maybe if intervals is a list of lists and normalized has [0,10] and [10,9] normalized [9,10] merge. Good.\n\nNeed maybe if intervals is a list of lists and normalized has [0,10] and [10,11] merge. Good.\n\nNeed maybe if intervals is a list of lists and normalized has [0,10] and [11,10] normalized [10,11] merge. Good.\n\nNeed maybe if intervals is a list of lists and normalized has [0,10] and [12,11] normalized [11,12] separate. Good.\n\nNeed maybe if intervals is a list of lists and normalized has [0,10] and [10,12] merge. Good.\n\nNeed maybe if intervals is a list of lists and normalized has [0,10] and [10,10] merge. Good.\n\nNeed maybe if intervals is a list of lists and normalized has [0,10] and [10,9] normalized [9,10] merge. Good.\n\nNeed maybe if intervals is a list of lists and normalized has [0,10] and [10,11] merge. Good.\n\nNeed maybe if intervals is a list of lists and normalized has [0,10] and [11,10] normalized [10,11] merge. Good.\n\nNeed maybe if intervals is a list of lists and normalized has [0,10] and [12,11] normalized [11,12] separate. Good.\n\nNeed maybe if intervals is a list of lists and normalized has [0,10] and [10,12] merge. Good.\n\nNeed maybe if intervals is a list of lists and normalized has [0,10] and [10,10] merge. Good.\n\nNeed maybe if intervals is a list of lists and normalized has [0,10] and [10,9] normalized [9,10] merge. Good.\n\nNeed maybe if intervals is a list of lists and normalized has [0,10] and [10,11] merge. Good.\n\nNeed maybe if intervals is a list of lists and normalized has [0,10] and [11,10] normalized [10,11] merge. Good.\n\nNeed maybe if intervals is a list of lists and normalized has [0,10] and [12,11] normalized [11,12] separate. Good.\n\nNeed maybe if intervals is a list of lists and normalized has [0,10] and [10,12] merge. Good.\n\nNeed maybe if intervals is a list of lists and normalized has [0,10] and [10,10] merge. Good.\n\nNeed maybe if intervals is a list of lists and normalized has [0,10] and [10,9] normalized [9,10] merge. Good.\n\nNeed maybe if intervals is a list of lists and normalized has [0,10] and [10,11] merge. Good.\n\nNeed maybe if intervals is a list of lists and normalized has [0,10] and [11,10] normalized [10,11] merge. Good.\n\nNeed maybe if intervals is a list of lists and normalized has [0,10] and [12,11] normalized [11,12] separate. Good.\n\nNeed maybe if intervals is a list of lists and normalized has [0,10] and [10,12] merge. Good.\n\nNeed maybe if intervals is a list of lists and normalized has [0,10] and [10,10] merge. Good.\n\nNeed maybe if intervals is a list of lists and normalized has [0,10] and [10,9] normalized [9,10] merge. Good.\n\nNeed maybe if intervals is a list of lists and normalized has [0,10] and [10,11] merge. Good.\n\nNeed maybe if intervals is a list of lists and normalized has [0,10] and [11,10] normalized [10,11] merge. Good.\n\nNeed maybe if intervals is a list of lists and normalized has [0,10] and [12,11] normalized [11,12] separate. Good.\n\nNeed maybe if intervals is a list of lists and normalized has [0,10] and [10,12] merge. Good.\n\nNeed maybe if intervals is a list of lists and normalized has [0,10] and [10,10] merge. Good.\n\nNeed maybe if intervals is a list of lists and normalized has [0,10] and [10,9] normalized [9,10] merge. Good.\n\nNeed maybe if intervals is a list of lists and normalized has [0,10] and [10,11] merge. Good.\n\nNeed maybe if intervals is a list of lists and normalized has [0,10] and [11,10] normalized [10,11] merge. Good.\n\nNeed maybe if intervals is a list of lists and normalized has [0,10] and [12,11] normalized [11,12] separate. Good.\n\nNeed maybe if intervals is a list of lists and normalized has [0,10] and [10,12] merge. Good.\n\nNeed maybe if intervals is a list of lists and normalized has [0,10] and [10,10] merge. Good.\n\nNeed maybe if intervals is a list of lists and normalized has [0,10] and [10,9] normalized [9,10] merge. Good.\n\nNeed maybe if intervals is a list of lists and normalized has [0,10] and [10,11] merge. Good.\n\nNeed maybe if intervals is a list of lists and normalized has [0,10] and [11,10] normalized [10,11] merge. Good.\n\nNeed maybe if intervals is a list of lists and normalized has [0,10] and [12,11] normalized [11,12] separate. Good.\n\nNeed maybe if intervals is a list of lists and normalized has [0,10] and [10,12] merge. Good.\n\nNeed maybe if intervals is a list of lists and normalized has [0,10] and [10,10] merge. Good.\n\nNeed maybe if intervals is a list of lists and normalized has [0,10] and [10,9] normalized [9,10] merge. Good.\n\nNeed maybe if intervals is a list of lists and normalized has [0,10] and [10,11] merge. Good.\n\nNeed maybe if intervals is a list of lists and normalized has [0,10] and [11,10] normalized [10,11] merge. Good.\n\nNeed maybe if intervals is a list of lists and normalized has [0,10] and [12,11] normalized [11,12] separate. Good.\n\nNeed maybe if intervals is a list of lists and normalized has [0,10] and [10,12] merge. Good.\n\nNeed maybe if intervals is a list of lists and normalized has [0,10] and [10,10] merge. Good.\n\nNeed maybe if intervals is a list of lists and normalized has [0,10] and [10,9] normalized [9,10] merge. Good.\n\nNeed maybe if intervals is a list of lists and normalized has [0,10] and [10,11] merge. Good.\n\nNeed maybe if intervals is a list of lists and normalized has [0,10] and [11,10] normalized [10,11] merge. Good.\n\nNeed maybe if intervals is a list of lists and normalized has [0,10] and [12,11] normalized [11,12] separate. Good.\n\nNeed maybe if intervals is a list of lists and normalized has [0,10] and [10,12] merge. Good.\n\nNeed maybe if intervals is a list of lists and normalized has [0,10] and [10,10] merge. Good.\n\nNeed maybe if intervals is a list of lists and normalized has [0,10] and [10,9] normalized [9,10] merge. Good.\n\nNeed maybe if intervals is a list of lists and normalized has [0,10] and [10,11] merge. Good.\n\nNeed maybe if intervals is a list of lists and normalized has [0,10] and [11,10] normalized [10,11] merge. Good.\n\nNeed maybe if intervals is a list of lists and normalized has [0,10] and [12,11] normalized [11,12] separate. Good.\n\nNeed maybe if intervals is a list of lists and normalized has [0,10] and [10,12] merge. Good.\n\nNeed maybe if intervals is a list of lists and normalized has [0,10] and [10,10] merge. Good.\n\nNeed maybe if intervals is a list of lists and normalized has [0,10] and [10,9] normalized [9,10] merge. Good.\n\nNeed maybe if intervals is a list of lists and normalized has [0,10] and [10,11] merge. Good.\n\nNeed maybe if intervals is a list of lists and normalized has [0,10"}, "logprobs": null, "finish_reason": "length", "stop_reason": null, "token_ids": null, "routed_experts": null}], "service_tier": null, "system_fingerprint": "vllm-0.3.1.dev3+g0bfc7a15d-9c4a3436", "usage": {"prompt_tokens": 123, "total_tokens": 8315, "completion_tokens": 8192, "prompt_tokens_details": null, "completion_tokens_details": {"reasoning_tokens": 8192}}, "prompt_logprobs": null, "prompt_token_ids": null, "prompt_text": null, "kv_transfer_params": null, "ec_transfer_params": null, "metrics": null}} diff --git a/audit/qualification/baseline-quality-medium.jsonl b/audit/qualification/baseline-quality-medium.jsonl new file mode 100644 index 0000000..d791081 --- /dev/null +++ b/audit/qualification/baseline-quality-medium.jsonl @@ -0,0 +1,7 @@ +{"name": "invoice-extraction", "passed": true, "elapsed_s": 2.4416399859983358, "response": {"id": "chatcmpl-9ef1e6844fbb710d", "object": "chat.completion", "created": 1789740786, "model": "qwen3.8-flash-next", "choices": [{"index": 0, "message": {"role": "assistant", "content": "\n\n{\"supplier\":\"North Star Ltd\",\"currency\":\"CNY\",\"total\":476,\"due_date\":\"2026-10-03\"}", "refusal": null, "annotations": null, "audio": null, "function_call": null, "reasoning": "The user wants me to extract invoice data into JSON format. Let me parse the information:\n\n- Supplier: North Star Ltd\n- Currency: CNY\n- Rows: 3 items at 125 each = 375, 2 items at 48 each = 96\n- Shipping: 20\n- Discount: 15\n- Total: 375 + 96 + 20 - 15 = 476\n- Due date: 2026-10-03\n\nThe total must be an integer: 476\n\nLet me format as JSON with keys: supplier, currency, total, due_date.\n"}, "logprobs": null, "finish_reason": "stop", "stop_reason": null, "token_ids": null, "routed_experts": null}], "service_tier": null, "system_fingerprint": "vllm-0.3.1.dev3+g0bfc7a15d-9c4a3436", "usage": {"prompt_tokens": 90, "total_tokens": 273, "completion_tokens": 183, "prompt_tokens_details": null, "completion_tokens_details": {"reasoning_tokens": 148}}, "prompt_logprobs": null, "prompt_token_ids": null, "prompt_text": null, "kv_transfer_params": null, "ec_transfer_params": null, "metrics": null}} +{"name": "deduplicate", "passed": true, "elapsed_s": 3.6378728710042196, "response": {"id": "chatcmpl-aebe6528b7dfdb4a", "object": "chat.completion", "created": 1789740789, "model": "qwen3.8-flash-next", "choices": [{"index": 0, "message": {"role": "assistant", "content": "\n\n{\"ids\":[\"A\",\"B\",\"D\"],\"total\":48}", "refusal": null, "annotations": null, "audio": null, "function_call": null, "reasoning": "Let me process the orders step by step.\n\nInput orders:\n1. A pending 12\n2. B paid 20\n3. A paid 15\n4. C cancelled 99\n5. D paid 8\n6. B paid 25\n\nStep 1: Keep the last occurrence of each ID.\n- A: last occurrence is \"A paid 15\" (order 3)\n- B: last occurrence is \"B paid 25\" (order 6)\n- C: last occurrence is \"C cancelled 99\" (order 4)\n- D: last occurrence is \"D paid 8\" (order 5)\n\nStep 2: Discard cancelled orders.\n- C is cancelled, so discard it.\n- Remaining: A paid 15, B paid 25, D paid 8\n\nStep 3: Sort IDs alphabetically.\n- A, B, D\n\nStep 4: Calculate total.\n- 15 + 25 + 8 = 48\n\nOutput: {\"ids\":[\"A\",\"B\",\"D\"],\"total\":48}\n"}, "logprobs": null, "finish_reason": "stop", "stop_reason": null, "token_ids": null, "routed_experts": null}], "service_tier": null, "system_fingerprint": "vllm-0.3.1.dev3+g0bfc7a15d-9c4a3436", "usage": {"prompt_tokens": 78, "total_tokens": 342, "completion_tokens": 264, "prompt_tokens_details": null, "completion_tokens_details": {"reasoning_tokens": 247}}, "prompt_logprobs": null, "prompt_token_ids": null, "prompt_text": null, "kv_transfer_params": null, "ec_transfer_params": null, "metrics": null}} +{"name": "schedule", "passed": true, "elapsed_s": 4.258343130997673, "response": {"id": "chatcmpl-80e60d150fade4f6", "object": "chat.completion", "created": 1789740792, "model": "qwen3.8-flash-next", "choices": [{"index": 0, "message": {"role": "assistant", "content": "\n\n{\"earliest_finish\": 10, \"critical_path\": [\"A\", \"B\", \"D\"]}", "refusal": null, "annotations": null, "audio": null, "function_call": null, "reasoning": "Let me analyze this scheduling problem carefully.\n\nJobs and their durations:\n- A: 3 hours (starts at hour 0)\n- B: 5 hours, starts after A (so starts at hour 3, finishes at hour 8)\n- C: 4 hours, starts after A (so starts at hour 3, finishes at hour 7)\n- D: 2 hours, starts after both B and C (so starts at max(8, 7) = 8, finishes at hour 10)\n\nLet me trace the timeline:\n- Hour 0: A starts\n- Hour 3: A finishes. B and C can start.\n- Hour 3: B starts, C starts\n- Hour 7: C finishes\n- Hour 8: B finishes. D can start (both B and C are done).\n- Hour 8: D starts\n- Hour 10: D finishes\n\nEarliest finish: 10 hours\n\nCritical path: The longest path through the dependency graph.\n- A → B → D: 3 + 5 + 2 = 10 hours\n- A → C → D: 3 + 4 + 2 = 9 hours\n\nThe critical path is A → B → D, which is [\"A\", \"B\", \"D\"].\n\nThe output should be JSON with earliest_finish and critical_path.\n"}, "logprobs": null, "finish_reason": "stop", "stop_reason": null, "token_ids": null, "routed_experts": null}], "service_tier": null, "system_fingerprint": "vllm-0.3.1.dev3+g0bfc7a15d-9c4a3436", "usage": {"prompt_tokens": 68, "total_tokens": 391, "completion_tokens": 323, "prompt_tokens_details": null, "completion_tokens_details": {"reasoning_tokens": 298}}, "prompt_logprobs": null, "prompt_token_ids": null, "prompt_text": null, "kv_transfer_params": null, "ec_transfer_params": null, "metrics": null}} +{"name": "chinese-policy", "passed": true, "elapsed_s": 2.5289165139984107, "response": {"id": "chatcmpl-a468fc20ce7b6692", "object": "chat.completion", "created": 1789740797, "model": "qwen3.8-flash-next", "choices": [{"index": 0, "message": {"role": "assistant", "content": "\n\n{\"甲\":\"主管\",\"乙\":\"经理\",\"丙\":\"经理\",\"丁\":\"总监\"}", "refusal": null, "annotations": null, "audio": null, "function_call": null, "reasoning": "让我仔细分析这个审批制度：\n\n- 金额不超过5000元：主管审批\n- 超过5000且不超过20000：经理审批\n- 超过20000：总监审批\n- 加急不改变审批人\n\n现在分析每个申请：\n\n甲：5000元 → 不超过5000 → 主管\n乙：5001元加急 → 超过5000且不超过20000 → 经理（加急不改变审批人）\n丙：20000元 → 超过5000且不超过20000 → 经理\n丁：20001元 → 超过20000 → 总监\n\n输出JSON格式。\n"}, "logprobs": null, "finish_reason": "stop", "stop_reason": null, "token_ids": null, "routed_experts": null}], "service_tier": null, "system_fingerprint": "vllm-0.3.1.dev3+g0bfc7a15d-9c4a3436", "usage": {"prompt_tokens": 106, "total_tokens": 296, "completion_tokens": 190, "prompt_tokens_details": null, "completion_tokens_details": {"reasoning_tokens": 170}}, "prompt_logprobs": null, "prompt_token_ids": null, "prompt_text": null, "kv_transfer_params": null, "ec_transfer_params": null, "metrics": null}} +{"name": "merge-intervals", "passed": true, "validation_stdout": "passed\n", "validation_stderr": "", "response": {"id": "chatcmpl-981b9ed8509e7638", "object": "chat.completion", "created": 1789740799, "model": "qwen3.8-flash-next", "choices": [{"index": 0, "message": {"role": "assistant", "content": "\n\n{\"code\": \"def merge_intervals(intervals):\\n if len(intervals) == 0:\\n return []\\n sorted_intervals = sorted(intervals)\\n merged = [list(sorted_intervals[0])]\\n for i in range(1, len(sorted_intervals)):\\n current = sorted_intervals[i]\\n last = merged[len(merged) - 1]\\n if current[0] <= last[1]:\\n last[1] = max(last[1], current[1])\\n else:\\n merged.append(list(current))\\n return merged\"}", "refusal": null, "annotations": null, "audio": null, "function_call": null, "reasoning": "The user wants a Python function `merge_intervals` that merges overlapping integer intervals, including touching endpoints. Let me think about the requirements:\n\n1. Merge overlapping integer intervals, including touching endpoints (e.g., [1,3] and [3,5] should merge to [1,5])\n2. Return sorted merged intervals as lists\n3. Reply ONLY a JSON object with a code string\n4. No imports, type annotations, decorators, top-level statements or helper functions\n5. Allowed builtins: sorted, len, range, min, max, list, enumerate, sum\n6. Allowed methods: append, sort, copy\n\nLet me write the function:\n\n```python\ndef merge_intervals(intervals):\n if len(intervals) == 0:\n return []\n sorted_intervals = sorted(intervals, key=lambda x: x[0])\n merged = [list(sorted_intervals[0])]\n for i in range(1, len(sorted_intervals)):\n current = sorted_intervals[i]\n last = merged[len(merged) - 1]\n if current[0] <= last[1]: # touching or overlapping\n last[1] = max(last[1], current[1])\n else:\n merged.append(list(current))\n return merged\n```\n\nWait, I need to check: can I use `lambda`? The constraint says no imports, type annotations, decorators, top-level statements or helper functions. Lambda isn't explicitly forbidden. But let me check if `sorted` with `key` is allowed - `sorted` is in the allowed builtins.\n\nActually, let me reconsider. The constraint says \"Allowed builtins: sorted,len,range,min,max,list,enumerate,sum\". This means I can only use these builtins. `lambda` is not a builtin, it's a keyword. Let me think if I can use it...\n\nActually, `lambda` is a keyword expression, not a builtin function. The restriction is on builtins. But to be safe, let me avoid lambda and use a different approach.\n\nI can sort by using `sorted(intervals)` which sorts by first element then second element by default for lists. That should work for intervals.\n\nLet me also check: `list()` is allowed as a builtin. `append` and `sort` are allowed methods.\n\nLet me also make sure I'm not using any helper functions. The function itself is the only function.\n\n```python\ndef merge_intervals(intervals):\n if len(intervals) == 0:\n return []\n sorted_intervals = sorted(intervals)\n merged = [list(sorted_intervals[0])]\n for i in range(1, len(sorted_intervals)):\n current = sorted_intervals[i]\n last = merged[len(merged) - 1]\n if current[0] <= last[1]:\n last[1] = max(last[1], current[1])\n else:\n merged.append(list(current))\n return merged\n```\n\nThis should work. `sorted(intervals)` will sort lists lexicographically, which means by first element, then by second element. That's exactly what we need.\n\nNow I need to return this as a JSON object with a \"code\" string. Let me format it properly.\n\nThe JSON should be: {\"code\": \"def merge_intervals(intervals):\\n if len(intervals) == 0:\\n return []\\n sorted_intervals = sorted(intervals)\\n merged = [list(sorted_intervals[0])]\\n for i in range(1, len(sorted_intervals)):\\n current = sorted_intervals[i]\\n last = merged[len(merged) - 1]\\n if current[0] <= last[1]:\\n last[1] = max(last[1], current[1])\\n else:\\n merged.append(list(current))\\n return merged\"}\n\nLet me double-check: no imports, no type annotations, no decorators, no top-level statements, no helper functions. Only uses sorted, len, range, max, list, append. All allowed. Good.\n"}, "logprobs": null, "finish_reason": "stop", "stop_reason": null, "token_ids": null, "routed_experts": null}], "service_tier": null, "system_fingerprint": "vllm-0.3.1.dev3+g0bfc7a15d-9c4a3436", "usage": {"prompt_tokens": 81, "total_tokens": 1090, "completion_tokens": 1009, "prompt_tokens_details": null, "completion_tokens_details": {"reasoning_tokens": 877}}, "prompt_logprobs": null, "prompt_token_ids": null, "prompt_text": null, "kv_transfer_params": null, "ec_transfer_params": null, "metrics": null}} +{"name": "binary-search", "passed": true, "validation_stdout": "passed\n", "validation_stderr": "", "response": {"id": "chatcmpl-aeb2774a80c34bf9", "object": "chat.completion", "created": 1789740812, "model": "qwen3.8-flash-next", "choices": [{"index": 0, "message": {"role": "assistant", "content": "\n\n{\"code\":\"def lower_bound(nums, target):\\n lo = 0\\n hi = len(nums)\\n while lo < hi:\\n mid = (lo + hi) // 2\\n if nums[mid] < target:\\n lo = mid + 1\\n else:\\n hi = mid\\n return lo\"}", "refusal": null, "annotations": null, "audio": null, "function_call": null, "reasoning": "The user wants a Python function `lower_bound` that returns the index of the first element >= target in a sorted list `nums`, or `len(nums)` if none exists.\n\nConstraints:\n- Return ONLY a JSON object with a code string\n- No imports, type annotations, decorators, top-level statements, or helper functions\n- Allowed builtins: sorted, len, range, min, max, list, enumerate, sum\n- Allowed methods: append, sort, copy\n\nLet me write a binary search implementation for lower_bound:\n\n```python\ndef lower_bound(nums, target):\n lo = 0\n hi = len(nums)\n while lo < hi:\n mid = (lo + hi) // 2\n if nums[mid] < target:\n lo = mid + 1\n else:\n hi = mid\n return lo\n```\n\nThis is a standard binary search for lower bound. Let me check constraints:\n- No imports ✓\n- No type annotations ✓\n- No decorators ✓\n- No top-level statements (just the function definition) ✓\n- No helper functions ✓\n- Uses `len` which is allowed ✓\n- No other builtins needed ✓\n\nNow I need to wrap this in a JSON object with a \"code\" string field.\n"}, "logprobs": null, "finish_reason": "stop", "stop_reason": null, "token_ids": null, "routed_experts": null}], "service_tier": null, "system_fingerprint": "vllm-0.3.1.dev3+g0bfc7a15d-9c4a3436", "usage": {"prompt_tokens": 92, "total_tokens": 442, "completion_tokens": 350, "prompt_tokens_details": null, "completion_tokens_details": {"reasoning_tokens": 274}}, "prompt_logprobs": null, "prompt_token_ids": null, "prompt_text": null, "kv_transfer_params": null, "ec_transfer_params": null, "metrics": null}} +{"name": "tool-arguments", "passed": true, "response": {"id": "chatcmpl-b686b7cb17c289f3", "object": "chat.completion", "created": 1789740817, "model": "qwen3.8-flash-next", "choices": [{"index": 0, "message": {"role": "assistant", "content": null, "refusal": null, "annotations": null, "audio": null, "function_call": null, "tool_calls": [{"id": "chatcmpl-tool-8b3933eec1e344ce", "type": "function", "function": {"name": "create_ticket", "arguments": "{\"title\": \"GPU cooling failure\", \"priority\": \"urgent\", \"asset\": \"GPU-092\"}"}}], "reasoning": "The user wants me to create a ticket with specific parameters:\n- title: \"GPU cooling failure\" (exactly)\n- priority: \"urgent\"\n- asset: \"GPU-092\" (exactly)\n\nThis is straightforward - I'll call the create_ticket function with these exact values.\n"}, "logprobs": null, "finish_reason": "tool_calls", "stop_reason": null, "token_ids": null, "routed_experts": null}], "service_tier": null, "system_fingerprint": "vllm-0.3.1.dev3+g0bfc7a15d-9c4a3436", "usage": {"prompt_tokens": 325, "total_tokens": 447, "completion_tokens": 122, "prompt_tokens_details": null, "completion_tokens_details": {"reasoning_tokens": 65}}, "prompt_logprobs": null, "prompt_token_ids": null, "prompt_text": null, "kv_transfer_params": null, "ec_transfer_params": null, "metrics": null}} diff --git a/audit/qualification/baseline-quality.jsonl b/audit/qualification/baseline-quality.jsonl new file mode 100644 index 0000000..3abe81f --- /dev/null +++ b/audit/qualification/baseline-quality.jsonl @@ -0,0 +1,7 @@ +{"name": "invoice-extraction", "passed": true, "elapsed_s": 1.853186526999707, "response": {"id": "chatcmpl-9004d24804c21cdc", "object": "chat.completion", "created": 1789702200, "model": "qwen3.8-flash-next", "choices": [{"index": 0, "message": {"role": "assistant", "content": "\n\n{\n \"supplier\": \"North Star Ltd\",\n \"currency\": \"CNY\",\n \"total\": 476,\n \"due_date\": \"2026-10-03\"\n}", "refusal": null, "annotations": null, "audio": null, "function_call": null, "reasoning": "We need answer user's request. Need produce final only JSON. Need compute total: 3*125=375, 2*48=96, shipping 20, discount 15. total = 375+96+20-15=476. Keys supplier, currency, total, due_date. JSON only. Ensure integer total.\n"}, "logprobs": null, "finish_reason": "stop", "stop_reason": null, "token_ids": null, "routed_experts": null}], "service_tier": null, "system_fingerprint": "vllm-0.3.1.dev3+g0bfc7a15d-9c4a3436", "usage": {"prompt_tokens": 132, "total_tokens": 270, "completion_tokens": 138, "prompt_tokens_details": null, "completion_tokens_details": {"reasoning_tokens": 85}}, "prompt_logprobs": null, "prompt_token_ids": null, "prompt_text": null, "kv_transfer_params": null, "ec_transfer_params": null, "metrics": null}} +{"name": "deduplicate", "passed": true, "elapsed_s": 3.305690247001621, "response": {"id": "chatcmpl-8c78159521c60876", "object": "chat.completion", "created": 1789702202, "model": "qwen3.8-flash-next", "choices": [{"index": 0, "message": {"role": "assistant", "content": "\n\n{\"ids\":[\"A\",\"B\",\"D\"],\"total\":48}", "refusal": null, "annotations": null, "audio": null, "function_call": null, "reasoning": "We need answer user's request. Need parse input orders: \"A pending 12; B paid 20; A paid 15; C cancelled 99; D paid 8; B paid 25. Keep the last occurrence of each ID, discard cancelled orders, and output ONLY JSON {\"ids\":[...],\"total\":...}. Sort IDs alphabetically.\"\n\nNeed produce final only JSON. Let's compute.\nOrders in sequence:\nA pending 12\nB paid 20\nA paid 15\nC cancelled 99\nD paid 8\nB paid 25\nKeep last occurrence of each ID: A last paid 15, B last paid 25, C last cancelled 99, D paid 8. Discard cancelled orders: remove C. Remaining A 15, B 25, D 8. Sort IDs alphabetically: A, B, D. Total = 15+25+8=48. Output {\"ids\":[\"A\",\"B\",\"D\"],\"total\":48}. Ensure ONLY JSON no markdown.\n"}, "logprobs": null, "finish_reason": "stop", "stop_reason": null, "token_ids": null, "routed_experts": null}], "service_tier": null, "system_fingerprint": "vllm-0.3.1.dev3+g0bfc7a15d-9c4a3436", "usage": {"prompt_tokens": 120, "total_tokens": 370, "completion_tokens": 250, "prompt_tokens_details": null, "completion_tokens_details": {"reasoning_tokens": 233}}, "prompt_logprobs": null, "prompt_token_ids": null, "prompt_text": null, "kv_transfer_params": null, "ec_transfer_params": null, "metrics": null}} +{"name": "schedule", "passed": true, "elapsed_s": 3.0858910660008405, "response": {"id": "chatcmpl-b6ceeff440eaa531", "object": "chat.completion", "created": 1789702205, "model": "qwen3.8-flash-next", "choices": [{"index": 0, "message": {"role": "assistant", "content": "\n\n{\n \"earliest_finish\": 10,\n \"critical_path\": [\"A\", \"B\", \"D\"]\n}", "refusal": null, "annotations": null, "audio": null, "function_call": null, "reasoning": "We need answer user's request. Need parse problem. \"Jobs A takes 3 hours; B takes 5 hours after A; C takes 4 hours after A; D takes 2 hours after both B and C. Unlimited workers, start at hour 0. Output only JSON with earliest_finish and critical_path (array of job letters).\"\n\nWe need compute earliest finish times. A duration 3, starts 0 finishes 3. B after A duration 5 => starts 3 finishes 8. C after A duration 4 => starts 3 finishes 7. D after both B and C duration 2 => starts max(8,7)=8 finishes 10. Earliest finish overall 10. Critical path longest path: A->B->D (3+5+2=10). Output only JSON. Need ensure valid JSON. {\"earliest_finish\":10,\"critical_path\":[\"A\",\"B\",\"D\"]}. No extra text.\n"}, "logprobs": null, "finish_reason": "stop", "stop_reason": null, "token_ids": null, "routed_experts": null}], "service_tier": null, "system_fingerprint": "vllm-0.3.1.dev3+g0bfc7a15d-9c4a3436", "usage": {"prompt_tokens": 110, "total_tokens": 343, "completion_tokens": 233, "prompt_tokens_details": null, "completion_tokens_details": {"reasoning_tokens": 201}}, "prompt_logprobs": null, "prompt_token_ids": null, "prompt_text": null, "kv_transfer_params": null, "ec_transfer_params": null, "metrics": null}} +{"name": "chinese-policy", "passed": true, "elapsed_s": 3.033975599999394, "response": {"id": "chatcmpl-9c030a36836653bb", "object": "chat.completion", "created": 1789702208, "model": "qwen3.8-flash-next", "choices": [{"index": 0, "message": {"role": "assistant", "content": "\n\n{\n \"甲\": \"主管\",\n \"乙\": \"经理\",\n \"丙\": \"经理\",\n \"丁\": \"总监\"\n}", "refusal": null, "annotations": null, "audio": null, "function_call": null, "reasoning": "We need answer user's request. Need produce final only JSON. Need parse Chinese:\nRule: amount <=5000 supervisor (主管); >5000 and <=20000 manager (经理); >20000 director (总监). Urgent doesn't change approver.\nApplications: 甲 5000 -> <=5000 => 主管. 乙 5001 加急 -> >5000 <=20000 => 经理. 丙 20000 -> <=20000 and >5000 => 经理. 丁 20001 -> >20000 => 总监.\nOutput only JSON keys 甲乙丙丁 values. Ensure valid JSON. Could be {\"甲\":\"主管\",\"乙\":\"经理\",\"丙\":\"经理\",\"丁\":\"总监\"}.\n"}, "logprobs": null, "finish_reason": "stop", "stop_reason": null, "token_ids": null, "routed_experts": null}], "service_tier": null, "system_fingerprint": "vllm-0.3.1.dev3+g0bfc7a15d-9c4a3436", "usage": {"prompt_tokens": 148, "total_tokens": 377, "completion_tokens": 229, "prompt_tokens_details": null, "completion_tokens_details": {"reasoning_tokens": 191}}, "prompt_logprobs": null, "prompt_token_ids": null, "prompt_text": null, "kv_transfer_params": null, "ec_transfer_params": null, "metrics": null}} +{"name": "merge-intervals", "passed": false, "error": "the JSON object must be str, bytes or bytearray, not NoneType"} +{"name": "binary-search", "passed": true, "validation_stdout": "passed\n", "validation_stderr": "", "response": {"id": "chatcmpl-89efbe814cd5878e", "object": "chat.completion", "created": 1789702318, "model": "qwen3.8-flash-next", "choices": [{"index": 0, "message": {"role": "assistant", "content": "\n\n{\"code\":\"def lower_bound(nums, target):\\n lo = 0\\n hi = len(nums)\\n while lo < hi:\\n mid = (lo + hi) // 2\\n if nums[mid] < target:\\n lo = mid + 1\\n else:\\n hi = mid\\n return lo\"}", "refusal": null, "annotations": null, "audio": null, "function_call": null, "reasoning": "We need answer user's request. Need produce final ONLY JSON object with a code string. Need satisfy constraints: Write Python function lower_bound. Return index first element >= target in sorted list nums, or len(nums) if none. Parameters nums, target. Reply ONLY JSON object with a code string. Use no imports, type annotations, decorators, top-level statements or helper functions. Allowed builtins: sorted,len,range,min,max,list,enumerate,sum. Allowed methods: append,sort,copy.\n\nNeed output JSON object likely {\"code\": \"def lower_bound(nums, target):\\n ...\"}. No imports etc. Function only. Need maybe binary search using len, range? No type annotations. Could implement:\ndef lower_bound(nums, target):\n lo = 0\n hi = len(nums)\n while lo < hi:\n mid = (lo + hi) // 2\n if nums[mid] < target:\n lo = mid + 1\n else:\n hi = mid\n return lo\n\nThis uses no disallowed builtins. JSON string must escape newlines. Ensure only JSON object. Could include code string. Need no top-level statements. Good.\n\nDouble-check allowed builtins: len allowed. // operator okay. No helper. No annotations.\n\nNeed final only JSON. Ensure valid JSON. Could be:\n{\"code\":\"def lower_bound(nums, target):\\n lo = 0\\n hi = len(nums)\\n while lo < hi:\\n mid = (lo + hi) // 2\\n if nums[mid] < target:\\n lo = mid + 1\\n else:\\n hi = mid\\n return lo\"}\n\nNo trailing newline? fine.\n"}, "logprobs": null, "finish_reason": "stop", "stop_reason": null, "token_ids": null, "routed_experts": null}], "service_tier": null, "system_fingerprint": "vllm-0.3.1.dev3+g0bfc7a15d-9c4a3436", "usage": {"prompt_tokens": 134, "total_tokens": 572, "completion_tokens": 438, "prompt_tokens_details": null, "completion_tokens_details": {"reasoning_tokens": 362}}, "prompt_logprobs": null, "prompt_token_ids": null, "prompt_text": null, "kv_transfer_params": null, "ec_transfer_params": null, "metrics": null}} +{"name": "tool-arguments", "passed": true, "response": {"id": "chatcmpl-817dda016167c20b", "object": "chat.completion", "created": 1789702324, "model": "qwen3.8-flash-next", "choices": [{"index": 0, "message": {"role": "assistant", "content": null, "refusal": null, "annotations": null, "audio": null, "function_call": null, "tool_calls": [{"id": "chatcmpl-tool-8dbfec4209335069", "type": "function", "function": {"name": "create_ticket", "arguments": "{\"title\": \"GPU cooling failure\", \"priority\": \"urgent\", \"asset\": \"GPU-092\"}"}}], "reasoning": "We need to respond to user: \"Call create_ticket with title exactly GPU cooling failure, priority urgent, and asset exactly GPU-092.\" Need call tool. Need final maybe brief. Ensure exact params.\n"}, "logprobs": null, "finish_reason": "tool_calls", "stop_reason": null, "token_ids": null, "routed_experts": null}], "service_tier": null, "system_fingerprint": "vllm-0.3.1.dev3+g0bfc7a15d-9c4a3436", "usage": {"prompt_tokens": 363, "total_tokens": 464, "completion_tokens": 101, "prompt_tokens_details": null, "completion_tokens_details": {"reasoning_tokens": 44}}, "prompt_logprobs": null, "prompt_token_ids": null, "prompt_text": null, "kv_transfer_params": null, "ec_transfer_params": null, "metrics": null}} diff --git a/audit/qualification/baseline-server.log b/audit/qualification/baseline-server.log new file mode 100644 index 0000000..98da51b --- /dev/null +++ b/audit/qualification/baseline-server.log @@ -0,0 +1,540 @@ +(APIServer pid=1) INFO 09-18 02:59:48 [api_utils.py:347] +(APIServer pid=1) INFO 09-18 02:59:48 [api_utils.py:347] █ █ █▄ ▄█ +(APIServer pid=1) INFO 09-18 02:59:48 [api_utils.py:347] ▄▄ ▄█ █ █ █ ▀▄▀ █ version 0.3.1.dev3+g0bfc7a15d +(APIServer pid=1) INFO 09-18 02:59:48 [api_utils.py:347] █▄█▀ █ █ █ █ model /model +(APIServer pid=1) INFO 09-18 02:59:48 [api_utils.py:347] ▀▀ ▀▀▀▀▀ ▀▀▀▀▀ ▀ ▀ +(APIServer pid=1) INFO 09-18 02:59:48 [api_utils.py:347] +(APIServer pid=1) INFO 09-18 02:59:48 [api_utils.py:286] non-default args: {'model_tag': '/model', 'enable_auto_tool_choice': True, 'tool_call_parser': 'qwen3_xml', 'host': '0.0.0.0', 'api_key': '***', 'model': '/model', 'dtype': 'bfloat16', 'max_model_len': 131072, 'served_model_name': ['qwen3.8-flash-next'], 'load_format': 'safetensors', 'reasoning_parser': 'qwen3', 'gpu_memory_utilization': 0.96, 'kv_cache_dtype': 'fp8', 'enable_prefix_caching': True, 'max_num_batched_tokens': 2048, 'max_num_seqs': 1, 'enable_chunked_prefill': True, 'enable_flashinfer_autotune': False, 'compilation_config': {'mode': , 'debug_dump_path': None, 'cache_dir': '', 'compile_cache_save_format': 'binary', 'backend': 'inductor', 'custom_ops': [], 'ir_enable_torch_wrap': None, 'splitting_ops': None, 'compile_mm_encoder': False, 'cudagraph_mm_encoder': False, 'encoder_cudagraph_token_budgets': [], 'encoder_cudagraph_max_vision_items_per_batch': 0, 'encoder_cudagraph_max_frames_per_batch': None, 'compile_sizes': None, 'compile_ranges_endpoints': None, 'inductor_compile_config': {'enable_auto_functionalized_v2': False, 'combo_kernels': True, 'benchmark_combo_kernel': True}, 'inductor_passes': {}, 'cudagraph_mode': , 'cudagraph_num_of_warmups': 0, 'cudagraph_capture_sizes': [1], 'cudagraph_copy_inputs': False, 'cudagraph_specialize_lora': True, 'use_inductor_graph_partition': None, 'pass_config': {}, 'max_cudagraph_capture_size': None, 'dynamic_shapes_config': {'type': , 'evaluate_guards': False, 'assume_32_bit_indexing': False}, 'local_cache_dir': None, 'fast_moe_cold_start': None, 'static_all_moe_layers': []}, 'engram_config': EngramConfig(cpu_offload=True, embedding_across_dp=False, dp_shared_memory=False)} +(APIServer pid=1) [transformers] Unrecognized keys in `rope_parameters` for 'rope_type'='default': {'mrope_section', 'mrope_interleaved'} +(APIServer pid=1) [transformers] Unrecognized keys in `rope_parameters` for 'rope_type'='default': {'mrope_section', 'mrope_interleaved'} +(APIServer pid=1) [transformers] Unrecognized keys in `rope_parameters` for 'rope_type'='default': {'mrope_section', 'mrope_interleaved'} +(APIServer pid=1) [transformers] Unrecognized keys in `rope_parameters` for 'rope_type'='default': {'mrope_section', 'mrope_interleaved'} +(APIServer pid=1) [transformers] Unrecognized keys in `rope_parameters` for 'rope_type'='default': {'mrope_section', 'mrope_interleaved'} +(APIServer pid=1) INFO 09-18 02:59:48 [model.py:691] Resolved architecture: Qwen4ExpForConditionalGeneration +(APIServer pid=1) INFO 09-18 02:59:48 [model.py:2024] Using max model len 131072 +(APIServer pid=1) [transformers] Unrecognized keys in `rope_parameters` for 'rope_type'='default': {'mrope_section', 'mrope_interleaved'} +(APIServer pid=1) [transformers] Unrecognized keys in `rope_parameters` for 'rope_type'='default': {'mrope_section', 'mrope_interleaved'} +(APIServer pid=1) [transformers] Unrecognized keys in `rope_parameters` for 'rope_type'='default': {'mrope_section', 'mrope_interleaved'} +(APIServer pid=1) [transformers] Unrecognized keys in `rope_parameters` for 'rope_type'='default': {'mrope_section', 'mrope_interleaved'} +(APIServer pid=1) [transformers] Unrecognized keys in `rope_parameters` for 'rope_type'='default': {'mrope_section', 'mrope_interleaved'} +(APIServer pid=1) INFO 09-18 02:59:52 [cache.py:345] Using fp8 data type to store kv cache. It reduces the GPU memory footprint and boosts the performance. Meanwhile, it may cause accuracy drop without a proper scaling factor +(APIServer pid=1) INFO 09-18 02:59:52 [config.py:625] Mamba cache mode is set to 'align' for Qwen4ExpForConditionalGeneration by default when prefix caching is enabled +(APIServer pid=1) INFO 09-18 02:59:52 [vllm.py:1271] Resolved Engram configuration: EngramConfig(cpu_offload=True, embedding_across_dp=False, dp_shared_memory=False) +(APIServer pid=1) INFO 09-18 02:59:52 [vllm.py:781] Auto-enabling VLLM_USE_BREAKABLE_CUDAGRAPH=1. Set VLLM_USE_BREAKABLE_CUDAGRAPH=0 to opt out. +(APIServer pid=1) INFO 09-18 02:59:52 [kernel.py:408] Final IR op priority after setting platform defaults: IrOpPriorityConfig(rms_norm=['vllm_c', 'native'], fused_add_rms_norm=['vllm_c', 'native'], gelu_and_mul_sparse=['triton', 'native']) +(APIServer pid=1) INFO 09-18 02:59:52 [compilation.py:331] Enabled custom fusions: norm_quant, act_quant +(APIServer pid=1) [transformers] Unrecognized keys in `rope_parameters` for 'rope_type'='default': {'mrope_section', 'mrope_interleaved'} +(APIServer pid=1) [transformers] Unrecognized keys in `rope_parameters` for 'rope_type'='default': {'mrope_section', 'mrope_interleaved'} +(APIServer pid=1) [transformers] The `use_fast` parameter is deprecated and will be removed in a future version. Use `backend="torchvision"` instead of `use_fast=True`, or `backend="pil"` instead of `use_fast=False`. +(APIServer pid=1) [transformers] Unrecognized keys in `rope_parameters` for 'rope_type'='default': {'mrope_section', 'mrope_interleaved'} +(APIServer pid=1) [transformers] Unrecognized keys in `rope_parameters` for 'rope_type'='default': {'mrope_section', 'mrope_interleaved'} +(EngineCore pid=113) INFO 09-18 03:00:04 [core.py:123] Initializing a V1 LLM engine (v0.3.1.dev3+g0bfc7a15d) with config: model='/model', speculative_config=None, tokenizer='/model', skip_tokenizer_init=False, tokenizer_mode=auto, revision=None, tokenizer_revision=None, trust_remote_code=False, dtype=torch.bfloat16, max_seq_len=131072, download_dir=None, load_format=safetensors, tensor_parallel_size=1, pipeline_parallel_size=1, data_parallel_size=1, decode_context_parallel_size=1, dcp_comm_backend=ag_rs, disable_custom_all_reduce=False, quantization=modelopt_mixed, quantization_config=None, enforce_eager=False, enable_return_routed_experts=False, kv_cache_dtype=fp8, device_config=cuda, structured_outputs_config=StructuredOutputsConfig(backend='auto', disable_any_whitespace=False, disable_additional_properties=False, reasoning_parser='qwen3', reasoning_parser_plugin='', enable_in_reasoning=False), observability_config=ObservabilityConfig(show_hidden_metrics_for_version=None, otlp_traces_endpoint=None, collect_detailed_traces=None, per_request_spec_decode_metrics='none', kv_cache_metrics=False, kv_cache_metrics_sample=0.01, cudagraph_metrics=False, enable_layerwise_nvtx_tracing=False, enable_mfu_metrics=False, enable_mm_processor_stats=False, enable_logging_iteration_details=False, jit_monitor_mode='warn', jit_monitor_verbose=False), seed=0, served_model_name=qwen3.8-flash-next, enable_prefix_caching=True, enable_chunked_prefill=True, pooler_config=None, compilation_config={'mode': , 'debug_dump_path': None, 'cache_dir': '', 'compile_cache_save_format': 'binary', 'backend': 'inductor', 'custom_ops': ['+quant_fp8', 'all', '+quant_fp8'], 'ir_enable_torch_wrap': False, 'splitting_ops': [], 'compile_mm_encoder': False, 'cudagraph_mm_encoder': False, 'encoder_cudagraph_token_budgets': [], 'encoder_cudagraph_max_vision_items_per_batch': 0, 'encoder_cudagraph_max_frames_per_batch': None, 'compile_sizes': [], 'compile_ranges_endpoints': [2048], 'inductor_compile_config': {'enable_auto_functionalized_v2': False, 'combo_kernels': True, 'benchmark_combo_kernel': True}, 'inductor_passes': {}, 'cudagraph_mode': , 'cudagraph_num_of_warmups': 1, 'cudagraph_capture_sizes': [1], 'cudagraph_copy_inputs': False, 'cudagraph_specialize_lora': True, 'use_inductor_graph_partition': False, 'pass_config': {'fuse_norm_quant': True, 'fuse_act_quant': True, 'fuse_attn_quant': False, 'enable_sp': False, 'fuse_gemm_comms': False, 'fuse_allreduce_rms': False, 'enable_qk_norm_rope_fusion': False, 'fuse_rope_kvcache_cat_mla': False, 'fuse_act_padding': False, 'fuse_qk_norm_rope_kvcache': False}, 'max_cudagraph_capture_size': 1, 'dynamic_shapes_config': {'type': , 'evaluate_guards': False, 'assume_32_bit_indexing': False}, 'local_cache_dir': None, 'fast_moe_cold_start': False, 'static_all_moe_layers': []}, kernel_config=KernelConfig(ir_op_priority=IrOpPriorityConfig(rms_norm=['vllm_c', 'native'], fused_add_rms_norm=['vllm_c', 'native'], gelu_and_mul_sparse=['triton', 'native']), enable_flashinfer_autotune=False, enable_cutedsl_warmup=True, enable_jit_warmup=True, moe_backend='auto', sparse_indexer_topk_backend='auto', linear_backend='auto', linear_backend_per_quant=None) +(EngineCore pid=113) [transformers] Unrecognized keys in `rope_parameters` for 'rope_type'='default': {'mrope_section', 'mrope_interleaved'} +(EngineCore pid=113) [transformers] Unrecognized keys in `rope_parameters` for 'rope_type'='default': {'mrope_section', 'mrope_interleaved'} +(EngineCore pid=113) [transformers] Unrecognized keys in `rope_parameters` for 'rope_type'='default': {'mrope_section', 'mrope_interleaved'} +(EngineCore pid=113) [transformers] Unrecognized keys in `rope_parameters` for 'rope_type'='default': {'mrope_section', 'mrope_interleaved'} +(EngineCore pid=113) [transformers] Unrecognized keys in `rope_parameters` for 'rope_type'='default': {'mrope_section', 'mrope_interleaved'} +(APIServer pid=1) [transformers] Unrecognized keys in `rope_parameters` for 'rope_type'='default': {'mrope_section', 'mrope_interleaved'} +(APIServer pid=1) [transformers] Unrecognized keys in `rope_parameters` for 'rope_type'='default': {'mrope_section', 'mrope_interleaved'} +(EngineCore pid=113) INFO 09-18 03:00:05 [parallel_state.py:1825] world_size=1 rank=0 local_rank=0 distributed_init_method=file:///tmp/vllm_dist_1ab23a515b4248119daf657c8672946f backend=nccl +(EngineCore pid=113) INFO 09-18 03:00:05 [parallel_state.py:2269] rank 0 in world size 1 is assigned as DP rank 0, PP rank 0, PCP rank 0, TP rank 0, ETP rank 0, EP rank 0, EPLB rank N/A +(EngineCore pid=113) INFO 09-18 03:00:05 [gpu_worker.py:441] Using V2 Model Runner +(EngineCore pid=113) INFO 09-18 03:00:06 [model_runner.py:387] Loading model from scratch... +(EngineCore pid=113) INFO 09-18 03:00:06 [cuda.py:595] Using backend AttentionBackendEnum.FLASH_ATTN for vit attention +(EngineCore pid=113) INFO 09-18 03:00:06 [mm_encoder_attention.py:372] Using AttentionBackendEnum.FLASH_ATTN for MMEncoderAttention. +(EngineCore pid=113) INFO 09-18 03:00:06 [qwen_gdn_linear_attn.py:176] Using FlashInfer GDN prefill kernel (requested=auto, head_k_dim=128). +(EngineCore pid=113) INFO 09-18 03:00:06 [qwen_gdn_linear_attn.py:528] GDN decode kernel: cuda +(EngineCore pid=113) INFO 09-18 03:00:08 [nvfp4.py:302] Using 'FLASHINFER_CUTLASS' NvFp4 MoE backend out of potential backends: ['FLASHINFER_TRTLLM', 'FLASHINFER_CUTEDSL', 'FLASHINFER_CUTEDSL_BATCHED', 'FLASHINFER_CUTLASS', 'VLLM_CUTLASS', 'MARLIN', 'HUMMING', 'EMULATION']. +(APIServer pid=1) [transformers] Qwen3VL video processing does not apply the per-frame pixel cap the reference implementation (qwen-vl-utils) applies, so some videos cost far more tokens than they would there. In v5.22 the capped behavior will become the default and `cap_pixels_per_frame` will be removed. Pass `cap_pixels_per_frame=True` to adopt the reference behavior now, or `False` to keep the current behavior and silence this warning. +(APIServer pid=1) INFO 09-18 03:00:10 [base.py:261] Multi-modal warmup completed in 12.284s +(APIServer pid=1) INFO 09-18 03:00:12 [base.py:261] Readonly multi-modal warmup completed in 1.318s +(EngineCore pid=113) INFO 09-18 03:00:42 [ngram_embedding.py:720] Initialized PLE embedding language_model.model.layers.1.ple.ple_embedding.ngram_embedding: quantization_method=Qwen4ExpPLEFp8EmbeddingMethod, weight_dtype=torch.float8_e4m3fn, weight_device=cpu, pinned=True +(EngineCore pid=113) INFO 09-18 03:00:42 [flash_attn.py:1115] Using FlashAttention version 2 +(EngineCore pid=113) WARNING 09-18 03:00:42 [compilation.py:1350] Op 'quant_fp8' not present in model, enabling with '+quant_fp8' has no effect +(EngineCore pid=113) INFO 09-18 03:00:42 [weight_utils.py:900] Filesystem type for checkpoints: EXT4. Checkpoint size: 123.57 GiB. Available RAM: 142.30 GiB. +(EngineCore pid=113) INFO 09-18 03:00:42 [weight_utils.py:923] Auto-prefetch is disabled because the filesystem (EXT4) is not a recognized network FS (NFS/Lustre). If you want to force prefetching, start vLLM with --safetensors-load-strategy=prefetch. +(EngineCore pid=113) +Loading safetensors checkpoint shards: 0% Completed | 0/11 [00:00= mamba page size. +(EngineCore pid=113) INFO 09-18 03:01:26 [interface.py:942] Padding mamba page size by 0.13% to ensure that mamba page size and attention page size are exactly equal. +(EngineCore pid=113) INFO 09-18 03:01:26 [utils.py:320] Using BLNHC KV cache layout. +(EngineCore pid=113) [transformers] Unrecognized keys in `rope_parameters` for 'rope_type'='default': {'mrope_section', 'mrope_interleaved'} +(EngineCore pid=113) [transformers] Unrecognized keys in `rope_parameters` for 'rope_type'='default': {'mrope_section', 'mrope_interleaved'} +(EngineCore pid=113) [transformers] The `use_fast` parameter is deprecated and will be removed in a future version. Use `backend="torchvision"` instead of `use_fast=True`, or `backend="pil"` instead of `use_fast=False`. +(EngineCore pid=113) [transformers] Unrecognized keys in `rope_parameters` for 'rope_type'='default': {'mrope_section', 'mrope_interleaved'} +(EngineCore pid=113) [transformers] Unrecognized keys in `rope_parameters` for 'rope_type'='default': {'mrope_section', 'mrope_interleaved'} +(EngineCore pid=113) INFO 09-18 03:01:30 [encoder_runner.py:131] Encoder cache will be initialized with a budget of 16384 tokens, and profiled with 1 image items of the maximum feature size. +(EngineCore pid=113) WARNING 09-18 03:01:52 [compilation.py:1415] CUDAGraphMode.FULL is not supported with GDNAttentionBackend backend (support: AttentionCGSupport.UNIFORM_BATCH); setting cudagraph_mode=FULL_DECODE_ONLY +(EngineCore pid=113) +Capturing CUDA graphs (FULL): 0%| | 0/1 [00:00 262144). Running this sequence through the model will result in indexing errors +(APIServer pid=1) INFO: 172.21.0.1:47142 - "POST /tokenize HTTP/1.1" 200 OK +(APIServer pid=1) INFO: 172.21.0.1:47154 - "POST /tokenize HTTP/1.1" 200 OK +(APIServer pid=1) INFO: 172.21.0.1:47168 - "POST /tokenize HTTP/1.1" 200 OK +(APIServer pid=1) INFO: 172.21.0.1:47176 - "POST /tokenize HTTP/1.1" 200 OK +(APIServer pid=1) INFO: 172.21.0.1:47184 - "POST /tokenize HTTP/1.1" 200 OK +(APIServer pid=1) INFO: 172.21.0.1:47196 - "POST /tokenize HTTP/1.1" 200 OK +(APIServer pid=1) INFO: 172.21.0.1:47206 - "POST /tokenize HTTP/1.1" 200 OK +(APIServer pid=1) INFO: 172.21.0.1:47214 - "POST /tokenize HTTP/1.1" 200 OK +(APIServer pid=1) INFO: 172.21.0.1:47226 - "POST /tokenize HTTP/1.1" 200 OK +(APIServer pid=1) INFO: 127.0.0.1:59622 - "GET /health HTTP/1.1" 200 OK +(APIServer pid=1) INFO: 172.21.0.1:47232 - "POST /tokenize HTTP/1.1" 200 OK +(APIServer pid=1) INFO: 172.21.0.1:47244 - "POST /tokenize HTTP/1.1" 200 OK +(APIServer pid=1) INFO: 172.21.0.1:47252 - "POST /tokenize HTTP/1.1" 200 OK +(APIServer pid=1) INFO: 172.21.0.1:47266 - "POST /tokenize HTTP/1.1" 200 OK +(APIServer pid=1) INFO: 172.21.0.1:47276 - "POST /tokenize HTTP/1.1" 200 OK +(APIServer pid=1) INFO: 172.21.0.1:44820 - "POST /v1/chat/completions HTTP/1.1" 200 OK +(APIServer pid=1) INFO 09-18 03:10:19 [loggers.py:323] Engine 000: Avg prompt throughput: 1235.6 tokens/s, Avg generation throughput: 14.7 tokens/s, Running: 1 reqs, Waiting: 0 reqs, GPU KV cache usage: 28.6%, Prefix cache hit rate: 0.0%, MM cache hit rate: 66.7% +(APIServer pid=1) INFO 09-18 03:10:29 [loggers.py:323] Engine 000: Avg prompt throughput: 12598.2 tokens/s, Avg generation throughput: 9.0 tokens/s, Running: 1 reqs, Waiting: 0 reqs, GPU KV cache usage: 54.8%, Prefix cache hit rate: 0.0%, MM cache hit rate: 66.7% +(APIServer pid=1) INFO: 172.21.0.1:54018 - "POST /v1/chat/completions HTTP/1.1" 200 OK +(APIServer pid=1) INFO: 172.21.0.1:54020 - "POST /v1/chat/completions HTTP/1.1" 200 OK +(APIServer pid=1) INFO: 172.21.0.1:54032 - "POST /v1/chat/completions HTTP/1.1" 200 OK +(APIServer pid=1) INFO 09-18 03:10:39 [loggers.py:323] Engine 000: Avg prompt throughput: 111.0 tokens/s, Avg generation throughput: 27.8 tokens/s, Running: 1 reqs, Waiting: 0 reqs, GPU KV cache usage: 31.0%, Prefix cache hit rate: 48.4%, MM cache hit rate: 71.4% +(APIServer pid=1) INFO: 127.0.0.1:57240 - "GET /health HTTP/1.1" 200 OK +(APIServer pid=1) WARNING 09-18 03:10:45 [hf.py:879] Chat template rejected the request: Unexpected reasoning effort high. Supported types are xhigh (default), medium, and low. +(APIServer pid=1) INFO: 172.21.0.1:38526 - "POST /v1/chat/completions HTTP/1.1" 400 Bad Request +(APIServer pid=1) INFO 09-18 03:10:49 [loggers.py:323] Engine 000: Avg prompt throughput: 0.0 tokens/s, Avg generation throughput: 0.0 tokens/s, Running: 0 reqs, Waiting: 0 reqs, GPU KV cache usage: 0.0%, Prefix cache hit rate: 48.4%, MM cache hit rate: 71.4% +(APIServer pid=1) INFO: 172.21.0.1:35840 - "POST /v1/chat/completions HTTP/1.1" 200 OK +(APIServer pid=1) INFO 09-18 03:11:09 [loggers.py:323] Engine 000: Avg prompt throughput: 5.9 tokens/s, Avg generation throughput: 3.0 tokens/s, Running: 1 reqs, Waiting: 0 reqs, GPU KV cache usage: 7.1%, Prefix cache hit rate: 48.4%, MM cache hit rate: 71.4% +(APIServer pid=1) INFO: 172.21.0.1:35848 - "POST /v1/chat/completions HTTP/1.1" 200 OK +(APIServer pid=1) INFO: 172.21.0.1:35864 - "POST /v1/chat/completions HTTP/1.1" 200 OK +(APIServer pid=1) INFO: 172.21.0.1:35872 - "POST /v1/chat/completions HTTP/1.1" 200 OK +(APIServer pid=1) INFO: 172.21.0.1:35888 - "POST /v1/chat/completions HTTP/1.1" 200 OK +(APIServer pid=1) INFO: 172.21.0.1:35896 - "POST /v1/chat/completions HTTP/1.1" 200 OK +(APIServer pid=1) INFO: 172.21.0.1:35910 - "POST /v1/chat/completions HTTP/1.1" 200 OK +(APIServer pid=1) INFO: 127.0.0.1:53726 - "GET /health HTTP/1.1" 200 OK +(APIServer pid=1) INFO: 172.21.0.1:45488 - "POST /v1/chat/completions HTTP/1.1" 200 OK +(APIServer pid=1) INFO: 172.21.0.1:45496 - "POST /v1/chat/completions HTTP/1.1" 200 OK +(APIServer pid=1) INFO: 172.21.0.1:45510 - "POST /v1/chat/completions HTTP/1.1" 200 OK +(APIServer pid=1) INFO: 172.21.0.1:45520 - "POST /v1/chat/completions HTTP/1.1" 200 OK +(APIServer pid=1) INFO 09-18 03:11:19 [loggers.py:323] Engine 000: Avg prompt throughput: 59.1 tokens/s, Avg generation throughput: 71.4 tokens/s, Running: 1 reqs, Waiting: 0 reqs, GPU KV cache usage: 7.1%, Prefix cache hit rate: 48.3%, MM cache hit rate: 71.4% +(APIServer pid=1) INFO: 172.21.0.1:45536 - "POST /v1/chat/completions HTTP/1.1" 200 OK +(APIServer pid=1) INFO: 172.21.0.1:45552 - "POST /v1/chat/completions HTTP/1.1" 200 OK +(APIServer pid=1) INFO: 172.21.0.1:45566 - "POST /v1/chat/completions HTTP/1.1" 200 OK +(APIServer pid=1) INFO: 172.21.0.1:45576 - "POST /v1/chat/completions HTTP/1.1" 200 OK +(APIServer pid=1) INFO: 172.21.0.1:52076 - "POST /v1/chat/completions HTTP/1.1" 200 OK +(APIServer pid=1) INFO: 172.21.0.1:52080 - "POST /v1/chat/completions HTTP/1.1" 200 OK +(APIServer pid=1) INFO 09-18 03:11:29 [loggers.py:323] Engine 000: Avg prompt throughput: 2776.9 tokens/s, Avg generation throughput: 50.7 tokens/s, Running: 1 reqs, Waiting: 0 reqs, GPU KV cache usage: 10.7%, Prefix cache hit rate: 45.9%, MM cache hit rate: 84.6% +(APIServer pid=1) INFO: 172.21.0.1:52090 - "POST /v1/chat/completions HTTP/1.1" 200 OK +(APIServer pid=1) INFO: 172.21.0.1:52094 - "POST /v1/chat/completions HTTP/1.1" 200 OK +(APIServer pid=1) INFO 09-18 03:11:39 [loggers.py:323] Engine 000: Avg prompt throughput: 15.4 tokens/s, Avg generation throughput: 76.1 tokens/s, Running: 1 reqs, Waiting: 0 reqs, GPU KV cache usage: 7.1%, Prefix cache hit rate: 45.9%, MM cache hit rate: 84.6% +(APIServer pid=1) INFO: 127.0.0.1:49718 - "GET /health HTTP/1.1" 200 OK +(APIServer pid=1) INFO 09-18 03:11:49 [loggers.py:323] Engine 000: Avg prompt throughput: 0.0 tokens/s, Avg generation throughput: 77.1 tokens/s, Running: 1 reqs, Waiting: 0 reqs, GPU KV cache usage: 7.1%, Prefix cache hit rate: 45.9%, MM cache hit rate: 84.6% +(APIServer pid=1) INFO 09-18 03:11:59 [loggers.py:323] Engine 000: Avg prompt throughput: 0.0 tokens/s, Avg generation throughput: 77.1 tokens/s, Running: 1 reqs, Waiting: 0 reqs, GPU KV cache usage: 7.1%, Prefix cache hit rate: 45.9%, MM cache hit rate: 84.6% +(APIServer pid=1) INFO: 172.21.0.1:35380 - "POST /v1/chat/completions HTTP/1.1" 200 OK +(APIServer pid=1) INFO 09-18 03:12:09 [loggers.py:323] Engine 000: Avg prompt throughput: 7.7 tokens/s, Avg generation throughput: 76.6 tokens/s, Running: 1 reqs, Waiting: 0 reqs, GPU KV cache usage: 7.1%, Prefix cache hit rate: 45.9%, MM cache hit rate: 84.6% +(APIServer pid=1) INFO: 127.0.0.1:41060 - "GET /health HTTP/1.1" 200 OK +(APIServer pid=1) INFO 09-18 03:12:19 [loggers.py:323] Engine 000: Avg prompt throughput: 0.0 tokens/s, Avg generation throughput: 77.2 tokens/s, Running: 1 reqs, Waiting: 0 reqs, GPU KV cache usage: 7.1%, Prefix cache hit rate: 45.9%, MM cache hit rate: 84.6% +(APIServer pid=1) INFO: 172.21.0.1:35078 - "POST /v1/chat/completions HTTP/1.1" 200 OK +(APIServer pid=1) INFO 09-18 03:12:29 [loggers.py:323] Engine 000: Avg prompt throughput: 7.7 tokens/s, Avg generation throughput: 76.6 tokens/s, Running: 1 reqs, Waiting: 0 reqs, GPU KV cache usage: 7.1%, Prefix cache hit rate: 45.8%, MM cache hit rate: 84.6% +(APIServer pid=1) INFO 09-18 03:12:39 [loggers.py:323] Engine 000: Avg prompt throughput: 0.0 tokens/s, Avg generation throughput: 77.1 tokens/s, Running: 1 reqs, Waiting: 0 reqs, GPU KV cache usage: 7.1%, Prefix cache hit rate: 45.8%, MM cache hit rate: 84.6% +(APIServer pid=1) INFO: 127.0.0.1:38006 - "GET /health HTTP/1.1" 200 OK +(APIServer pid=1) INFO 09-18 03:12:49 [loggers.py:323] Engine 000: Avg prompt throughput: 0.0 tokens/s, Avg generation throughput: 77.1 tokens/s, Running: 1 reqs, Waiting: 0 reqs, GPU KV cache usage: 7.1%, Prefix cache hit rate: 45.8%, MM cache hit rate: 84.6% +(APIServer pid=1) INFO: 172.21.0.1:60488 - "POST /v1/chat/completions HTTP/1.1" 200 OK +(APIServer pid=1) INFO: 172.21.0.1:43418 - "POST /v1/chat/completions HTTP/1.1" 200 OK +(APIServer pid=1) INFO 09-18 03:12:59 [loggers.py:323] Engine 000: Avg prompt throughput: 15.8 tokens/s, Avg generation throughput: 76.3 tokens/s, Running: 1 reqs, Waiting: 0 reqs, GPU KV cache usage: 7.1%, Prefix cache hit rate: 45.8%, MM cache hit rate: 84.6% +(APIServer pid=1) INFO 09-18 03:13:09 [loggers.py:323] Engine 000: Avg prompt throughput: 0.0 tokens/s, Avg generation throughput: 77.1 tokens/s, Running: 1 reqs, Waiting: 0 reqs, GPU KV cache usage: 7.1%, Prefix cache hit rate: 45.8%, MM cache hit rate: 84.6% +(APIServer pid=1) INFO: 127.0.0.1:38036 - "GET /health HTTP/1.1" 200 OK +(APIServer pid=1) INFO 09-18 03:13:19 [loggers.py:323] Engine 000: Avg prompt throughput: 0.0 tokens/s, Avg generation throughput: 77.1 tokens/s, Running: 1 reqs, Waiting: 0 reqs, GPU KV cache usage: 7.1%, Prefix cache hit rate: 45.8%, MM cache hit rate: 84.6% +(APIServer pid=1) INFO: 172.21.0.1:43076 - "POST /v1/chat/completions HTTP/1.1" 200 OK +(APIServer pid=1) INFO 09-18 03:13:29 [loggers.py:323] Engine 000: Avg prompt throughput: 7.9 tokens/s, Avg generation throughput: 76.7 tokens/s, Running: 1 reqs, Waiting: 0 reqs, GPU KV cache usage: 7.1%, Prefix cache hit rate: 45.8%, MM cache hit rate: 84.6% +(APIServer pid=1) INFO 09-18 03:13:39 [loggers.py:323] Engine 000: Avg prompt throughput: 0.0 tokens/s, Avg generation throughput: 77.2 tokens/s, Running: 1 reqs, Waiting: 0 reqs, GPU KV cache usage: 7.1%, Prefix cache hit rate: 45.8%, MM cache hit rate: 84.6% +(APIServer pid=1) INFO: 127.0.0.1:59088 - "GET /health HTTP/1.1" 200 OK +(APIServer pid=1) INFO 09-18 03:13:49 [loggers.py:323] Engine 000: Avg prompt throughput: 0.0 tokens/s, Avg generation throughput: 77.1 tokens/s, Running: 1 reqs, Waiting: 0 reqs, GPU KV cache usage: 7.1%, Prefix cache hit rate: 45.8%, MM cache hit rate: 84.6% +(APIServer pid=1) INFO: 172.21.0.1:43398 - "POST /v1/chat/completions HTTP/1.1" 200 OK +(APIServer pid=1) INFO 09-18 03:13:59 [loggers.py:323] Engine 000: Avg prompt throughput: 7.9 tokens/s, Avg generation throughput: 76.7 tokens/s, Running: 1 reqs, Waiting: 0 reqs, GPU KV cache usage: 7.1%, Prefix cache hit rate: 45.8%, MM cache hit rate: 84.6% +(APIServer pid=1) INFO 09-18 03:14:09 [loggers.py:323] Engine 000: Avg prompt throughput: 0.0 tokens/s, Avg generation throughput: 77.1 tokens/s, Running: 1 reqs, Waiting: 0 reqs, GPU KV cache usage: 7.1%, Prefix cache hit rate: 45.8%, MM cache hit rate: 84.6% +(APIServer pid=1) INFO: 127.0.0.1:37614 - "GET /health HTTP/1.1" 200 OK +(APIServer pid=1) INFO: 172.21.0.1:50104 - "POST /v1/chat/completions HTTP/1.1" 200 OK +(APIServer pid=1) INFO 09-18 03:14:19 [loggers.py:323] Engine 000: Avg prompt throughput: 7.9 tokens/s, Avg generation throughput: 76.6 tokens/s, Running: 1 reqs, Waiting: 0 reqs, GPU KV cache usage: 7.1%, Prefix cache hit rate: 45.8%, MM cache hit rate: 84.6% +(APIServer pid=1) INFO: 172.21.0.1:50108 - "POST /v1/chat/completions HTTP/1.1" 200 OK +(APIServer pid=1) INFO 09-18 03:14:29 [loggers.py:323] Engine 000: Avg prompt throughput: 7.9 tokens/s, Avg generation throughput: 76.8 tokens/s, Running: 1 reqs, Waiting: 0 reqs, GPU KV cache usage: 7.1%, Prefix cache hit rate: 45.8%, MM cache hit rate: 84.6% +(APIServer pid=1) INFO 09-18 03:14:39 [loggers.py:323] Engine 000: Avg prompt throughput: 0.0 tokens/s, Avg generation throughput: 77.1 tokens/s, Running: 1 reqs, Waiting: 0 reqs, GPU KV cache usage: 7.1%, Prefix cache hit rate: 45.8%, MM cache hit rate: 84.6% +(APIServer pid=1) INFO: 127.0.0.1:49780 - "GET /health HTTP/1.1" 200 OK +(APIServer pid=1) INFO: 172.21.0.1:36156 - "POST /v1/chat/completions HTTP/1.1" 200 OK +(APIServer pid=1) INFO 09-18 03:14:49 [loggers.py:323] Engine 000: Avg prompt throughput: 7.9 tokens/s, Avg generation throughput: 76.7 tokens/s, Running: 1 reqs, Waiting: 0 reqs, GPU KV cache usage: 7.1%, Prefix cache hit rate: 45.8%, MM cache hit rate: 84.6% +(APIServer pid=1) INFO 09-18 03:14:59 [loggers.py:323] Engine 000: Avg prompt throughput: 0.0 tokens/s, Avg generation throughput: 77.2 tokens/s, Running: 1 reqs, Waiting: 0 reqs, GPU KV cache usage: 7.1%, Prefix cache hit rate: 45.8%, MM cache hit rate: 84.6% +(APIServer pid=1) INFO 09-18 03:15:09 [loggers.py:323] Engine 000: Avg prompt throughput: 0.0 tokens/s, Avg generation throughput: 77.1 tokens/s, Running: 1 reqs, Waiting: 0 reqs, GPU KV cache usage: 7.1%, Prefix cache hit rate: 45.8%, MM cache hit rate: 84.6% +(APIServer pid=1) INFO: 172.21.0.1:33486 - "POST /v1/chat/completions HTTP/1.1" 200 OK +(APIServer pid=1) INFO: 127.0.0.1:41522 - "GET /health HTTP/1.1" 200 OK +(APIServer pid=1) INFO 09-18 03:15:19 [loggers.py:323] Engine 000: Avg prompt throughput: 7.9 tokens/s, Avg generation throughput: 76.7 tokens/s, Running: 1 reqs, Waiting: 0 reqs, GPU KV cache usage: 7.1%, Prefix cache hit rate: 45.8%, MM cache hit rate: 84.6% +(APIServer pid=1) INFO 09-18 03:15:29 [loggers.py:323] Engine 000: Avg prompt throughput: 0.0 tokens/s, Avg generation throughput: 77.2 tokens/s, Running: 1 reqs, Waiting: 0 reqs, GPU KV cache usage: 7.1%, Prefix cache hit rate: 45.8%, MM cache hit rate: 84.6% +(APIServer pid=1) INFO 09-18 03:15:39 [loggers.py:323] Engine 000: Avg prompt throughput: 0.0 tokens/s, Avg generation throughput: 77.1 tokens/s, Running: 1 reqs, Waiting: 0 reqs, GPU KV cache usage: 7.1%, Prefix cache hit rate: 45.8%, MM cache hit rate: 84.6% +(APIServer pid=1) INFO: 172.21.0.1:46554 - "POST /v1/chat/completions HTTP/1.1" 200 OK +(APIServer pid=1) INFO: 127.0.0.1:54702 - "GET /health HTTP/1.1" 200 OK +(APIServer pid=1) INFO 09-18 03:15:49 [loggers.py:323] Engine 000: Avg prompt throughput: 7.5 tokens/s, Avg generation throughput: 76.7 tokens/s, Running: 1 reqs, Waiting: 0 reqs, GPU KV cache usage: 7.1%, Prefix cache hit rate: 45.8%, MM cache hit rate: 84.6% +(APIServer pid=1) INFO 09-18 03:15:59 [loggers.py:323] Engine 000: Avg prompt throughput: 0.0 tokens/s, Avg generation throughput: 77.1 tokens/s, Running: 1 reqs, Waiting: 0 reqs, GPU KV cache usage: 7.1%, Prefix cache hit rate: 45.8%, MM cache hit rate: 84.6% +(APIServer pid=1) INFO 09-18 03:16:09 [loggers.py:323] Engine 000: Avg prompt throughput: 0.0 tokens/s, Avg generation throughput: 76.9 tokens/s, Running: 1 reqs, Waiting: 0 reqs, GPU KV cache usage: 7.1%, Prefix cache hit rate: 45.8%, MM cache hit rate: 84.6% +(APIServer pid=1) INFO: 127.0.0.1:55592 - "GET /health HTTP/1.1" 200 OK +(APIServer pid=1) INFO 09-18 03:16:19 [loggers.py:323] Engine 000: Avg prompt throughput: 0.0 tokens/s, Avg generation throughput: 76.6 tokens/s, Running: 1 reqs, Waiting: 0 reqs, GPU KV cache usage: 7.1%, Prefix cache hit rate: 45.8%, MM cache hit rate: 84.6% +(APIServer pid=1) INFO 09-18 03:16:29 [loggers.py:323] Engine 000: Avg prompt throughput: 0.0 tokens/s, Avg generation throughput: 76.6 tokens/s, Running: 1 reqs, Waiting: 0 reqs, GPU KV cache usage: 8.3%, Prefix cache hit rate: 45.8%, MM cache hit rate: 84.6% +(APIServer pid=1) INFO 09-18 03:16:39 [loggers.py:323] Engine 000: Avg prompt throughput: 0.0 tokens/s, Avg generation throughput: 76.6 tokens/s, Running: 1 reqs, Waiting: 0 reqs, GPU KV cache usage: 8.3%, Prefix cache hit rate: 45.8%, MM cache hit rate: 84.6% +(APIServer pid=1) INFO: 127.0.0.1:41374 - "GET /health HTTP/1.1" 200 OK +(APIServer pid=1) INFO 09-18 03:16:49 [loggers.py:323] Engine 000: Avg prompt throughput: 0.0 tokens/s, Avg generation throughput: 76.6 tokens/s, Running: 1 reqs, Waiting: 0 reqs, GPU KV cache usage: 8.3%, Prefix cache hit rate: 45.8%, MM cache hit rate: 84.6% +(APIServer pid=1) INFO 09-18 03:16:59 [loggers.py:323] Engine 000: Avg prompt throughput: 0.0 tokens/s, Avg generation throughput: 76.6 tokens/s, Running: 1 reqs, Waiting: 0 reqs, GPU KV cache usage: 8.3%, Prefix cache hit rate: 45.8%, MM cache hit rate: 84.6% +(APIServer pid=1) INFO 09-18 03:17:09 [loggers.py:323] Engine 000: Avg prompt throughput: 0.0 tokens/s, Avg generation throughput: 76.6 tokens/s, Running: 1 reqs, Waiting: 0 reqs, GPU KV cache usage: 9.5%, Prefix cache hit rate: 45.8%, MM cache hit rate: 84.6% +(APIServer pid=1) INFO: 127.0.0.1:38054 - "GET /health HTTP/1.1" 200 OK +(APIServer pid=1) INFO 09-18 03:17:19 [loggers.py:323] Engine 000: Avg prompt throughput: 0.0 tokens/s, Avg generation throughput: 76.6 tokens/s, Running: 1 reqs, Waiting: 0 reqs, GPU KV cache usage: 9.5%, Prefix cache hit rate: 45.8%, MM cache hit rate: 84.6% +(APIServer pid=1) INFO 09-18 03:17:29 [loggers.py:323] Engine 000: Avg prompt throughput: 0.0 tokens/s, Avg generation throughput: 76.5 tokens/s, Running: 1 reqs, Waiting: 0 reqs, GPU KV cache usage: 9.5%, Prefix cache hit rate: 45.8%, MM cache hit rate: 84.6% +(APIServer pid=1) INFO 09-18 03:17:39 [loggers.py:323] Engine 000: Avg prompt throughput: 0.0 tokens/s, Avg generation throughput: 76.6 tokens/s, Running: 1 reqs, Waiting: 0 reqs, GPU KV cache usage: 9.5%, Prefix cache hit rate: 45.8%, MM cache hit rate: 84.6% +(APIServer pid=1) INFO: 127.0.0.1:46862 - "GET /health HTTP/1.1" 200 OK +(APIServer pid=1) INFO 09-18 03:17:49 [loggers.py:323] Engine 000: Avg prompt throughput: 0.0 tokens/s, Avg generation throughput: 76.5 tokens/s, Running: 1 reqs, Waiting: 0 reqs, GPU KV cache usage: 10.7%, Prefix cache hit rate: 45.8%, MM cache hit rate: 84.6% +(APIServer pid=1) INFO 09-18 03:17:59 [loggers.py:323] Engine 000: Avg prompt throughput: 0.0 tokens/s, Avg generation throughput: 76.5 tokens/s, Running: 1 reqs, Waiting: 0 reqs, GPU KV cache usage: 10.7%, Prefix cache hit rate: 45.8%, MM cache hit rate: 84.6% +(APIServer pid=1) INFO 09-18 03:18:09 [loggers.py:323] Engine 000: Avg prompt throughput: 0.0 tokens/s, Avg generation throughput: 76.5 tokens/s, Running: 1 reqs, Waiting: 0 reqs, GPU KV cache usage: 10.7%, Prefix cache hit rate: 45.8%, MM cache hit rate: 84.6% +(APIServer pid=1) INFO: 127.0.0.1:43844 - "GET /health HTTP/1.1" 200 OK +(APIServer pid=1) INFO 09-18 03:18:19 [loggers.py:323] Engine 000: Avg prompt throughput: 0.0 tokens/s, Avg generation throughput: 76.6 tokens/s, Running: 1 reqs, Waiting: 0 reqs, GPU KV cache usage: 10.7%, Prefix cache hit rate: 45.8%, MM cache hit rate: 84.6% +(APIServer pid=1) INFO 09-18 03:18:29 [loggers.py:323] Engine 000: Avg prompt throughput: 0.0 tokens/s, Avg generation throughput: 76.5 tokens/s, Running: 1 reqs, Waiting: 0 reqs, GPU KV cache usage: 11.9%, Prefix cache hit rate: 45.8%, MM cache hit rate: 84.6% +(APIServer pid=1) INFO 09-18 03:18:39 [loggers.py:323] Engine 000: Avg prompt throughput: 0.0 tokens/s, Avg generation throughput: 76.5 tokens/s, Running: 1 reqs, Waiting: 0 reqs, GPU KV cache usage: 11.9%, Prefix cache hit rate: 45.8%, MM cache hit rate: 84.6% +(APIServer pid=1) INFO: 127.0.0.1:34270 - "GET /health HTTP/1.1" 200 OK +(APIServer pid=1) INFO 09-18 03:18:49 [loggers.py:323] Engine 000: Avg prompt throughput: 0.0 tokens/s, Avg generation throughput: 76.5 tokens/s, Running: 1 reqs, Waiting: 0 reqs, GPU KV cache usage: 11.9%, Prefix cache hit rate: 45.8%, MM cache hit rate: 84.6% +(APIServer pid=1) INFO 09-18 03:18:59 [loggers.py:323] Engine 000: Avg prompt throughput: 0.0 tokens/s, Avg generation throughput: 76.5 tokens/s, Running: 1 reqs, Waiting: 0 reqs, GPU KV cache usage: 11.9%, Prefix cache hit rate: 45.8%, MM cache hit rate: 84.6% +(APIServer pid=1) INFO 09-18 03:19:09 [loggers.py:323] Engine 000: Avg prompt throughput: 0.0 tokens/s, Avg generation throughput: 76.5 tokens/s, Running: 1 reqs, Waiting: 0 reqs, GPU KV cache usage: 13.1%, Prefix cache hit rate: 45.8%, MM cache hit rate: 84.6% +(APIServer pid=1) [transformers] Token indices sequence length is longer than the specified maximum sequence length for this model (268298 > 262144). Running this sequence through the model will result in indexing errors +(APIServer pid=1) INFO: 172.21.0.1:36216 - "POST /tokenize HTTP/1.1" 200 OK +(APIServer pid=1) INFO: 172.21.0.1:36232 - "POST /tokenize HTTP/1.1" 200 OK +(APIServer pid=1) INFO: 172.21.0.1:36246 - "POST /tokenize HTTP/1.1" 200 OK +(APIServer pid=1) INFO: 172.21.0.1:36254 - "POST /tokenize HTTP/1.1" 200 OK +(APIServer pid=1) INFO: 172.21.0.1:36270 - "POST /tokenize HTTP/1.1" 200 OK +(APIServer pid=1) INFO: 172.21.0.1:53790 - "POST /tokenize HTTP/1.1" 200 OK +(APIServer pid=1) INFO: 172.21.0.1:53794 - "POST /tokenize HTTP/1.1" 200 OK +(APIServer pid=1) INFO: 172.21.0.1:53802 - "POST /tokenize HTTP/1.1" 200 OK +(APIServer pid=1) INFO: 172.21.0.1:53814 - "POST /tokenize HTTP/1.1" 200 OK +(APIServer pid=1) INFO: 172.21.0.1:53826 - "POST /tokenize HTTP/1.1" 200 OK +(APIServer pid=1) INFO: 172.21.0.1:53834 - "POST /tokenize HTTP/1.1" 200 OK +(APIServer pid=1) INFO: 172.21.0.1:53838 - "POST /tokenize HTTP/1.1" 200 OK +(APIServer pid=1) INFO: 172.21.0.1:53846 - "POST /tokenize HTTP/1.1" 200 OK +(APIServer pid=1) INFO: 172.21.0.1:53852 - "POST /tokenize HTTP/1.1" 200 OK +(APIServer pid=1) INFO: 172.21.0.1:53866 - "POST /v1/chat/completions HTTP/1.1" 200 OK +(APIServer pid=1) INFO: 127.0.0.1:55542 - "GET /health HTTP/1.1" 200 OK +(APIServer pid=1) INFO: 172.21.0.1:53876 - "POST /v1/chat/completions HTTP/1.1" 200 OK +(APIServer pid=1) INFO 09-18 03:19:19 [loggers.py:323] Engine 000: Avg prompt throughput: 1937.1 tokens/s, Avg generation throughput: 46.0 tokens/s, Running: 0 reqs, Waiting: 0 reqs, GPU KV cache usage: 0.0%, Prefix cache hit rate: 53.0%, MM cache hit rate: 84.6% +(APIServer pid=1) INFO: 172.21.0.1:53888 - "POST /v1/chat/completions HTTP/1.1" 200 OK +(APIServer pid=1) INFO: 172.21.0.1:53900 - "POST /v1/chat/completions HTTP/1.1" 200 OK +(APIServer pid=1) INFO: 172.21.0.1:59136 - "POST /v1/chat/completions HTTP/1.1" 200 OK +(APIServer pid=1) INFO: 172.21.0.1:59146 - "POST /v1/chat/completions HTTP/1.1" 200 OK +(APIServer pid=1) INFO: 172.21.0.1:59154 - "POST /v1/chat/completions HTTP/1.1" 200 OK +(APIServer pid=1) INFO: 172.21.0.1:59170 - "POST /v1/chat/completions HTTP/1.1" 200 OK +(APIServer pid=1) INFO 09-18 03:19:29 [loggers.py:323] Engine 000: Avg prompt throughput: 637.6 tokens/s, Avg generation throughput: 67.4 tokens/s, Running: 0 reqs, Waiting: 0 reqs, GPU KV cache usage: 0.0%, Prefix cache hit rate: 68.9%, MM cache hit rate: 85.7% +(APIServer pid=1) INFO: 172.21.0.1:59186 - "POST /v1/chat/completions HTTP/1.1" 200 OK +(APIServer pid=1) INFO: 172.21.0.1:59190 - "POST /v1/chat/completions HTTP/1.1" 200 OK +(APIServer pid=1) INFO: 172.21.0.1:59206 - "POST /v1/chat/completions HTTP/1.1" 200 OK +(APIServer pid=1) INFO: 172.21.0.1:44972 - "POST /v1/chat/completions HTTP/1.1" 200 OK +(APIServer pid=1) INFO 09-18 03:19:39 [loggers.py:323] Engine 000: Avg prompt throughput: 643.5 tokens/s, Avg generation throughput: 68.9 tokens/s, Running: 1 reqs, Waiting: 0 reqs, GPU KV cache usage: 7.1%, Prefix cache hit rate: 72.1%, MM cache hit rate: 87.5% +(APIServer pid=1) INFO: 127.0.0.1:58578 - "GET /health HTTP/1.1" 200 OK +(APIServer pid=1) INFO 09-18 03:19:49 [loggers.py:323] Engine 000: Avg prompt throughput: 0.0 tokens/s, Avg generation throughput: 77.1 tokens/s, Running: 1 reqs, Waiting: 0 reqs, GPU KV cache usage: 7.1%, Prefix cache hit rate: 72.1%, MM cache hit rate: 87.5% +(APIServer pid=1) INFO 09-18 03:19:59 [loggers.py:323] Engine 000: Avg prompt throughput: 0.0 tokens/s, Avg generation throughput: 77.1 tokens/s, Running: 1 reqs, Waiting: 0 reqs, GPU KV cache usage: 7.1%, Prefix cache hit rate: 72.1%, MM cache hit rate: 87.5% +(APIServer pid=1) INFO: 172.21.0.1:57826 - "POST /v1/chat/completions HTTP/1.1" 200 OK +(APIServer pid=1) INFO 09-18 03:20:09 [loggers.py:323] Engine 000: Avg prompt throughput: 8.6 tokens/s, Avg generation throughput: 76.7 tokens/s, Running: 1 reqs, Waiting: 0 reqs, GPU KV cache usage: 7.1%, Prefix cache hit rate: 72.1%, MM cache hit rate: 87.5% +(APIServer pid=1) INFO: 127.0.0.1:43346 - "GET /health HTTP/1.1" 200 OK +(APIServer pid=1) INFO 09-18 03:20:19 [loggers.py:323] Engine 000: Avg prompt throughput: 0.0 tokens/s, Avg generation throughput: 77.1 tokens/s, Running: 1 reqs, Waiting: 0 reqs, GPU KV cache usage: 7.1%, Prefix cache hit rate: 72.1%, MM cache hit rate: 87.5% +(APIServer pid=1) INFO: 172.21.0.1:51208 - "POST /v1/chat/completions HTTP/1.1" 200 OK +(APIServer pid=1) INFO: 172.21.0.1:51212 - "POST /v1/chat/completions HTTP/1.1" 200 OK +(APIServer pid=1) INFO 09-18 03:20:29 [loggers.py:323] Engine 000: Avg prompt throughput: 55.5 tokens/s, Avg generation throughput: 74.6 tokens/s, Running: 0 reqs, Waiting: 0 reqs, GPU KV cache usage: 0.0%, Prefix cache hit rate: 74.7%, MM cache hit rate: 87.5% +(APIServer pid=1) INFO: 172.21.0.1:51220 - "POST /v1/chat/completions HTTP/1.1" 200 OK +(APIServer pid=1) INFO: 172.21.0.1:51222 - "POST /v1/chat/completions HTTP/1.1" 200 OK +(APIServer pid=1) INFO 09-18 03:20:39 [loggers.py:323] Engine 000: Avg prompt throughput: 588.2 tokens/s, Avg generation throughput: 71.3 tokens/s, Running: 1 reqs, Waiting: 0 reqs, GPU KV cache usage: 7.1%, Prefix cache hit rate: 74.6%, MM cache hit rate: 88.9% +(APIServer pid=1) INFO: 127.0.0.1:42298 - "GET /health HTTP/1.1" 200 OK +(APIServer pid=1) INFO 09-18 03:20:49 [loggers.py:323] Engine 000: Avg prompt throughput: 0.0 tokens/s, Avg generation throughput: 77.1 tokens/s, Running: 1 reqs, Waiting: 0 reqs, GPU KV cache usage: 7.1%, Prefix cache hit rate: 74.6%, MM cache hit rate: 88.9% +(APIServer pid=1) INFO 09-18 03:20:59 [loggers.py:323] Engine 000: Avg prompt throughput: 0.0 tokens/s, Avg generation throughput: 77.1 tokens/s, Running: 1 reqs, Waiting: 0 reqs, GPU KV cache usage: 7.1%, Prefix cache hit rate: 74.6%, MM cache hit rate: 88.9% +(APIServer pid=1) INFO: 172.21.0.1:42934 - "POST /v1/chat/completions HTTP/1.1" 200 OK +(APIServer pid=1) INFO 09-18 03:21:09 [loggers.py:323] Engine 000: Avg prompt throughput: 8.4 tokens/s, Avg generation throughput: 76.7 tokens/s, Running: 1 reqs, Waiting: 0 reqs, GPU KV cache usage: 7.1%, Prefix cache hit rate: 74.6%, MM cache hit rate: 88.9% +(APIServer pid=1) INFO: 127.0.0.1:58604 - "GET /health HTTP/1.1" 200 OK +(APIServer pid=1) INFO 09-18 03:21:19 [loggers.py:323] Engine 000: Avg prompt throughput: 0.0 tokens/s, Avg generation throughput: 77.1 tokens/s, Running: 1 reqs, Waiting: 0 reqs, GPU KV cache usage: 7.1%, Prefix cache hit rate: 74.6%, MM cache hit rate: 88.9% +(APIServer pid=1) INFO: 172.21.0.1:57912 - "POST /v1/chat/completions HTTP/1.1" 200 OK +(APIServer pid=1) INFO: 172.21.0.1:57914 - "POST /v1/chat/completions HTTP/1.1" 200 OK +(APIServer pid=1) INFO 09-18 03:21:29 [loggers.py:323] Engine 000: Avg prompt throughput: 369.0 tokens/s, Avg generation throughput: 72.5 tokens/s, Running: 1 reqs, Waiting: 0 reqs, GPU KV cache usage: 8.3%, Prefix cache hit rate: 76.6%, MM cache hit rate: 89.5% +(APIServer pid=1) INFO: 172.21.0.1:57928 - "POST /v1/chat/completions HTTP/1.1" 200 OK +(APIServer pid=1) INFO: 172.21.0.1:57934 - "POST /v1/chat/completions HTTP/1.1" 200 OK +(APIServer pid=1) INFO 09-18 03:21:39 [loggers.py:323] Engine 000: Avg prompt throughput: 274.8 tokens/s, Avg generation throughput: 73.3 tokens/s, Running: 1 reqs, Waiting: 0 reqs, GPU KV cache usage: 7.1%, Prefix cache hit rate: 76.6%, MM cache hit rate: 90.0% +(APIServer pid=1) INFO: 127.0.0.1:60802 - "GET /health HTTP/1.1" 200 OK +(APIServer pid=1) INFO 09-18 03:21:49 [loggers.py:323] Engine 000: Avg prompt throughput: 0.0 tokens/s, Avg generation throughput: 77.1 tokens/s, Running: 1 reqs, Waiting: 0 reqs, GPU KV cache usage: 7.1%, Prefix cache hit rate: 76.6%, MM cache hit rate: 90.0% +(APIServer pid=1) INFO: 172.21.0.1:46352 - "POST /v1/chat/completions HTTP/1.1" 200 OK +(APIServer pid=1) INFO 09-18 03:21:59 [loggers.py:323] Engine 000: Avg prompt throughput: 8.7 tokens/s, Avg generation throughput: 76.7 tokens/s, Running: 1 reqs, Waiting: 0 reqs, GPU KV cache usage: 7.1%, Prefix cache hit rate: 76.6%, MM cache hit rate: 90.0% +(APIServer pid=1) INFO 09-18 03:22:09 [loggers.py:323] Engine 000: Avg prompt throughput: 0.0 tokens/s, Avg generation throughput: 77.1 tokens/s, Running: 1 reqs, Waiting: 0 reqs, GPU KV cache usage: 7.1%, Prefix cache hit rate: 76.6%, MM cache hit rate: 90.0% +(APIServer pid=1) INFO: 127.0.0.1:36974 - "GET /health HTTP/1.1" 200 OK +(APIServer pid=1) INFO 09-18 03:22:19 [loggers.py:323] Engine 000: Avg prompt throughput: 0.0 tokens/s, Avg generation throughput: 77.2 tokens/s, Running: 1 reqs, Waiting: 0 reqs, GPU KV cache usage: 7.1%, Prefix cache hit rate: 76.6%, MM cache hit rate: 90.0% +(APIServer pid=1) INFO: 172.21.0.1:53112 - "POST /v1/chat/completions HTTP/1.1" 200 OK +(APIServer pid=1) INFO: 172.21.0.1:53114 - "POST /v1/chat/completions HTTP/1.1" 200 OK +(APIServer pid=1) INFO 09-18 03:22:29 [loggers.py:323] Engine 000: Avg prompt throughput: 369.0 tokens/s, Avg generation throughput: 72.6 tokens/s, Running: 1 reqs, Waiting: 0 reqs, GPU KV cache usage: 8.3%, Prefix cache hit rate: 78.3%, MM cache hit rate: 90.5% +(APIServer pid=1) INFO: 172.21.0.1:53116 - "POST /v1/chat/completions HTTP/1.1" 200 OK +(APIServer pid=1) INFO: 172.21.0.1:53120 - "POST /v1/chat/completions HTTP/1.1" 200 OK +(APIServer pid=1) INFO 09-18 03:22:39 [loggers.py:323] Engine 000: Avg prompt throughput: 274.6 tokens/s, Avg generation throughput: 73.2 tokens/s, Running: 1 reqs, Waiting: 0 reqs, GPU KV cache usage: 7.1%, Prefix cache hit rate: 78.2%, MM cache hit rate: 90.9% +(APIServer pid=1) INFO: 127.0.0.1:54350 - "GET /health HTTP/1.1" 200 OK +(APIServer pid=1) INFO 09-18 03:22:49 [loggers.py:323] Engine 000: Avg prompt throughput: 0.0 tokens/s, Avg generation throughput: 77.2 tokens/s, Running: 1 reqs, Waiting: 0 reqs, GPU KV cache usage: 7.1%, Prefix cache hit rate: 78.2%, MM cache hit rate: 90.9% +(APIServer pid=1) INFO: 172.21.0.1:60880 - "POST /v1/chat/completions HTTP/1.1" 200 OK +(APIServer pid=1) INFO 09-18 03:22:59 [loggers.py:323] Engine 000: Avg prompt throughput: 8.7 tokens/s, Avg generation throughput: 76.6 tokens/s, Running: 1 reqs, Waiting: 0 reqs, GPU KV cache usage: 7.1%, Prefix cache hit rate: 78.2%, MM cache hit rate: 90.9% +(APIServer pid=1) INFO 09-18 03:23:09 [loggers.py:323] Engine 000: Avg prompt throughput: 0.0 tokens/s, Avg generation throughput: 77.1 tokens/s, Running: 1 reqs, Waiting: 0 reqs, GPU KV cache usage: 7.1%, Prefix cache hit rate: 78.2%, MM cache hit rate: 90.9% +(APIServer pid=1) INFO: 127.0.0.1:51446 - "GET /health HTTP/1.1" 200 OK +(APIServer pid=1) INFO 09-18 03:23:19 [loggers.py:323] Engine 000: Avg prompt throughput: 0.0 tokens/s, Avg generation throughput: 77.2 tokens/s, Running: 1 reqs, Waiting: 0 reqs, GPU KV cache usage: 7.1%, Prefix cache hit rate: 78.2%, MM cache hit rate: 90.9% +(APIServer pid=1) INFO: 172.21.0.1:47600 - "POST /v1/chat/completions HTTP/1.1" 200 OK +(APIServer pid=1) INFO: 172.21.0.1:47602 - "POST /v1/chat/completions HTTP/1.1" 200 OK +(APIServer pid=1) INFO: 172.21.0.1:47618 - "POST /v1/chat/completions HTTP/1.1" 200 OK +(APIServer pid=1) INFO 09-18 03:23:29 [loggers.py:323] Engine 000: Avg prompt throughput: 635.1 tokens/s, Avg generation throughput: 69.1 tokens/s, Running: 1 reqs, Waiting: 0 reqs, GPU KV cache usage: 10.7%, Prefix cache hit rate: 79.6%, MM cache hit rate: 91.7% +(APIServer pid=1) INFO: 172.21.0.1:47626 - "POST /v1/chat/completions HTTP/1.1" 200 OK +(APIServer pid=1) INFO 09-18 03:23:39 [loggers.py:323] Engine 000: Avg prompt throughput: 8.7 tokens/s, Avg generation throughput: 76.6 tokens/s, Running: 1 reqs, Waiting: 0 reqs, GPU KV cache usage: 7.1%, Prefix cache hit rate: 79.6%, MM cache hit rate: 91.7% +(APIServer pid=1) INFO: 127.0.0.1:50808 - "GET /health HTTP/1.1" 200 OK +(APIServer pid=1) INFO 09-18 03:23:49 [loggers.py:323] Engine 000: Avg prompt throughput: 0.0 tokens/s, Avg generation throughput: 77.2 tokens/s, Running: 1 reqs, Waiting: 0 reqs, GPU KV cache usage: 7.1%, Prefix cache hit rate: 79.6%, MM cache hit rate: 91.7% +(APIServer pid=1) INFO: 172.21.0.1:33200 - "POST /v1/chat/completions HTTP/1.1" 200 OK +(APIServer pid=1) INFO 09-18 03:23:59 [loggers.py:323] Engine 000: Avg prompt throughput: 8.5 tokens/s, Avg generation throughput: 76.6 tokens/s, Running: 1 reqs, Waiting: 0 reqs, GPU KV cache usage: 7.1%, Prefix cache hit rate: 79.6%, MM cache hit rate: 91.7% +(APIServer pid=1) INFO 09-18 03:24:09 [loggers.py:323] Engine 000: Avg prompt throughput: 0.0 tokens/s, Avg generation throughput: 77.1 tokens/s, Running: 1 reqs, Waiting: 0 reqs, GPU KV cache usage: 7.1%, Prefix cache hit rate: 79.6%, MM cache hit rate: 91.7% +(APIServer pid=1) INFO: 127.0.0.1:53902 - "GET /health HTTP/1.1" 200 OK +(APIServer pid=1) INFO 09-18 03:24:19 [loggers.py:323] Engine 000: Avg prompt throughput: 0.0 tokens/s, Avg generation throughput: 77.1 tokens/s, Running: 1 reqs, Waiting: 0 reqs, GPU KV cache usage: 7.1%, Prefix cache hit rate: 79.6%, MM cache hit rate: 91.7% +(APIServer pid=1) INFO: 172.21.0.1:35818 - "POST /v1/chat/completions HTTP/1.1" 200 OK +(APIServer pid=1) INFO: 172.21.0.1:60464 - "POST /v1/chat/completions HTTP/1.1" 200 OK +(APIServer pid=1) INFO: 172.21.0.1:60480 - "POST /v1/chat/completions HTTP/1.1" 200 OK +(APIServer pid=1) INFO 09-18 03:24:29 [loggers.py:323] Engine 000: Avg prompt throughput: 635.1 tokens/s, Avg generation throughput: 69.2 tokens/s, Running: 1 reqs, Waiting: 0 reqs, GPU KV cache usage: 10.7%, Prefix cache hit rate: 80.8%, MM cache hit rate: 92.3% +(APIServer pid=1) INFO: 172.21.0.1:60486 - "POST /v1/chat/completions HTTP/1.1" 200 OK +(APIServer pid=1) INFO 09-18 03:24:39 [loggers.py:323] Engine 000: Avg prompt throughput: 8.7 tokens/s, Avg generation throughput: 76.6 tokens/s, Running: 1 reqs, Waiting: 0 reqs, GPU KV cache usage: 7.1%, Prefix cache hit rate: 80.8%, MM cache hit rate: 92.3% +(APIServer pid=1) INFO: 127.0.0.1:36330 - "GET /health HTTP/1.1" 200 OK +(APIServer pid=1) INFO 09-18 03:24:49 [loggers.py:323] Engine 000: Avg prompt throughput: 0.0 tokens/s, Avg generation throughput: 77.1 tokens/s, Running: 1 reqs, Waiting: 0 reqs, GPU KV cache usage: 7.1%, Prefix cache hit rate: 80.8%, MM cache hit rate: 92.3% +(APIServer pid=1) INFO: 172.21.0.1:47132 - "POST /v1/chat/completions HTTP/1.1" 200 OK +(APIServer pid=1) INFO 09-18 03:24:59 [loggers.py:323] Engine 000: Avg prompt throughput: 8.7 tokens/s, Avg generation throughput: 76.7 tokens/s, Running: 1 reqs, Waiting: 0 reqs, GPU KV cache usage: 7.1%, Prefix cache hit rate: 80.8%, MM cache hit rate: 92.3% +(APIServer pid=1) INFO 09-18 03:25:09 [loggers.py:323] Engine 000: Avg prompt throughput: 0.0 tokens/s, Avg generation throughput: 77.1 tokens/s, Running: 1 reqs, Waiting: 0 reqs, GPU KV cache usage: 7.1%, Prefix cache hit rate: 80.8%, MM cache hit rate: 92.3% +(APIServer pid=1) INFO: 127.0.0.1:60010 - "GET /health HTTP/1.1" 200 OK +(APIServer pid=1) INFO 09-18 03:25:19 [loggers.py:323] Engine 000: Avg prompt throughput: 0.0 tokens/s, Avg generation throughput: 77.2 tokens/s, Running: 1 reqs, Waiting: 0 reqs, GPU KV cache usage: 7.1%, Prefix cache hit rate: 80.8%, MM cache hit rate: 92.3% +(APIServer pid=1) INFO: 172.21.0.1:37630 - "POST /v1/chat/completions HTTP/1.1" 200 OK +(APIServer pid=1) INFO: 172.21.0.1:60520 - "POST /v1/chat/completions HTTP/1.1" 200 OK +(APIServer pid=1) INFO: 172.21.0.1:60522 - "POST /v1/chat/completions HTTP/1.1" 200 OK +(APIServer pid=1) INFO 09-18 03:25:29 [loggers.py:323] Engine 000: Avg prompt throughput: 635.1 tokens/s, Avg generation throughput: 69.1 tokens/s, Running: 1 reqs, Waiting: 0 reqs, GPU KV cache usage: 10.7%, Prefix cache hit rate: 81.8%, MM cache hit rate: 92.9% +(APIServer pid=1) INFO: 172.21.0.1:60526 - "POST /v1/chat/completions HTTP/1.1" 200 OK +(APIServer pid=1) INFO 09-18 03:25:39 [loggers.py:323] Engine 000: Avg prompt throughput: 8.5 tokens/s, Avg generation throughput: 76.6 tokens/s, Running: 1 reqs, Waiting: 0 reqs, GPU KV cache usage: 7.1%, Prefix cache hit rate: 81.8%, MM cache hit rate: 92.9% +(APIServer pid=1) INFO: 127.0.0.1:43716 - "GET /health HTTP/1.1" 200 OK +(APIServer pid=1) INFO 09-18 03:25:49 [loggers.py:323] Engine 000: Avg prompt throughput: 0.0 tokens/s, Avg generation throughput: 77.1 tokens/s, Running: 1 reqs, Waiting: 0 reqs, GPU KV cache usage: 7.1%, Prefix cache hit rate: 81.8%, MM cache hit rate: 92.9% +(APIServer pid=1) INFO: 172.21.0.1:52900 - "POST /v1/chat/completions HTTP/1.1" 200 OK +(APIServer pid=1) INFO 09-18 03:25:59 [loggers.py:323] Engine 000: Avg prompt throughput: 8.7 tokens/s, Avg generation throughput: 76.7 tokens/s, Running: 1 reqs, Waiting: 0 reqs, GPU KV cache usage: 7.1%, Prefix cache hit rate: 81.8%, MM cache hit rate: 92.9% +(APIServer pid=1) INFO 09-18 03:26:09 [loggers.py:323] Engine 000: Avg prompt throughput: 0.0 tokens/s, Avg generation throughput: 77.1 tokens/s, Running: 1 reqs, Waiting: 0 reqs, GPU KV cache usage: 7.1%, Prefix cache hit rate: 81.8%, MM cache hit rate: 92.9% +(APIServer pid=1) INFO: 127.0.0.1:41628 - "GET /health HTTP/1.1" 200 OK +(APIServer pid=1) INFO 09-18 03:26:19 [loggers.py:323] Engine 000: Avg prompt throughput: 0.0 tokens/s, Avg generation throughput: 77.2 tokens/s, Running: 1 reqs, Waiting: 0 reqs, GPU KV cache usage: 7.1%, Prefix cache hit rate: 81.8%, MM cache hit rate: 92.9% +(APIServer pid=1) INFO: 172.21.0.1:52472 - "POST /v1/chat/completions HTTP/1.1" 200 OK +(APIServer pid=1) INFO: 172.21.0.1:60460 - "POST /v1/chat/completions HTTP/1.1" 200 OK +(APIServer pid=1) INFO: 172.21.0.1:60464 - "POST /v1/chat/completions HTTP/1.1" 200 OK +(APIServer pid=1) INFO: 172.21.0.1:60476 - "POST /v1/chat/completions HTTP/1.1" 200 OK +(APIServer pid=1) INFO 09-18 03:26:29 [loggers.py:323] Engine 000: Avg prompt throughput: 643.8 tokens/s, Avg generation throughput: 68.5 tokens/s, Running: 1 reqs, Waiting: 0 reqs, GPU KV cache usage: 7.1%, Prefix cache hit rate: 82.7%, MM cache hit rate: 93.3% +(APIServer pid=1) INFO 09-18 03:26:39 [loggers.py:323] Engine 000: Avg prompt throughput: 0.0 tokens/s, Avg generation throughput: 77.2 tokens/s, Running: 1 reqs, Waiting: 0 reqs, GPU KV cache usage: 7.1%, Prefix cache hit rate: 82.7%, MM cache hit rate: 93.3% +(APIServer pid=1) INFO: 127.0.0.1:55028 - "GET /health HTTP/1.1" 200 OK +(APIServer pid=1) INFO 09-18 03:26:49 [loggers.py:323] Engine 000: Avg prompt throughput: 0.0 tokens/s, Avg generation throughput: 77.2 tokens/s, Running: 1 reqs, Waiting: 0 reqs, GPU KV cache usage: 7.1%, Prefix cache hit rate: 82.7%, MM cache hit rate: 93.3% +(APIServer pid=1) INFO: 172.21.0.1:34742 - "POST /v1/chat/completions HTTP/1.1" 200 OK +(APIServer pid=1) INFO 09-18 03:26:59 [loggers.py:323] Engine 000: Avg prompt throughput: 8.5 tokens/s, Avg generation throughput: 76.6 tokens/s, Running: 1 reqs, Waiting: 0 reqs, GPU KV cache usage: 7.1%, Prefix cache hit rate: 82.7%, MM cache hit rate: 93.3% +(APIServer pid=1) INFO 09-18 03:27:09 [loggers.py:323] Engine 000: Avg prompt throughput: 0.0 tokens/s, Avg generation throughput: 77.2 tokens/s, Running: 1 reqs, Waiting: 0 reqs, GPU KV cache usage: 7.1%, Prefix cache hit rate: 82.7%, MM cache hit rate: 93.3% +(APIServer pid=1) INFO: 127.0.0.1:49266 - "GET /health HTTP/1.1" 200 OK +(APIServer pid=1) INFO 09-18 03:27:19 [loggers.py:323] Engine 000: Avg prompt throughput: 0.0 tokens/s, Avg generation throughput: 77.1 tokens/s, Running: 1 reqs, Waiting: 0 reqs, GPU KV cache usage: 7.1%, Prefix cache hit rate: 82.7%, MM cache hit rate: 93.3% +(APIServer pid=1) INFO: 172.21.0.1:44380 - "POST /v1/chat/completions HTTP/1.1" 200 OK +(APIServer pid=1) INFO: 172.21.0.1:44394 - "POST /v1/chat/completions HTTP/1.1" 200 OK +(APIServer pid=1) INFO: 172.21.0.1:55470 - "POST /v1/chat/completions HTTP/1.1" 200 OK +(APIServer pid=1) INFO: 172.21.0.1:55480 - "POST /v1/chat/completions HTTP/1.1" 200 OK +(APIServer pid=1) INFO 09-18 03:27:29 [loggers.py:323] Engine 000: Avg prompt throughput: 643.8 tokens/s, Avg generation throughput: 68.5 tokens/s, Running: 1 reqs, Waiting: 0 reqs, GPU KV cache usage: 7.1%, Prefix cache hit rate: 83.5%, MM cache hit rate: 93.8% +(APIServer pid=1) INFO 09-18 03:27:39 [loggers.py:323] Engine 000: Avg prompt throughput: 0.0 tokens/s, Avg generation throughput: 77.2 tokens/s, Running: 1 reqs, Waiting: 0 reqs, GPU KV cache usage: 7.1%, Prefix cache hit rate: 83.5%, MM cache hit rate: 93.8% +(APIServer pid=1) INFO: 127.0.0.1:47762 - "GET /health HTTP/1.1" 200 OK +(APIServer pid=1) INFO 09-18 03:27:49 [loggers.py:323] Engine 000: Avg prompt throughput: 0.0 tokens/s, Avg generation throughput: 77.1 tokens/s, Running: 1 reqs, Waiting: 0 reqs, GPU KV cache usage: 7.1%, Prefix cache hit rate: 83.5%, MM cache hit rate: 93.8% +(APIServer pid=1) INFO: 172.21.0.1:32878 - "POST /v1/chat/completions HTTP/1.1" 200 OK +(APIServer pid=1) INFO 09-18 03:27:59 [loggers.py:323] Engine 000: Avg prompt throughput: 8.7 tokens/s, Avg generation throughput: 76.6 tokens/s, Running: 1 reqs, Waiting: 0 reqs, GPU KV cache usage: 7.1%, Prefix cache hit rate: 83.5%, MM cache hit rate: 93.8% +(APIServer pid=1) INFO 09-18 03:28:09 [loggers.py:323] Engine 000: Avg prompt throughput: 0.0 tokens/s, Avg generation throughput: 77.2 tokens/s, Running: 1 reqs, Waiting: 0 reqs, GPU KV cache usage: 7.1%, Prefix cache hit rate: 83.5%, MM cache hit rate: 93.8% +(APIServer pid=1) INFO: 127.0.0.1:54118 - "GET /health HTTP/1.1" 200 OK +(APIServer pid=1) INFO 09-18 03:28:19 [loggers.py:323] Engine 000: Avg prompt throughput: 0.0 tokens/s, Avg generation throughput: 77.1 tokens/s, Running: 1 reqs, Waiting: 0 reqs, GPU KV cache usage: 7.1%, Prefix cache hit rate: 83.5%, MM cache hit rate: 93.8% +(APIServer pid=1) INFO: 172.21.0.1:37126 - "POST /v1/chat/completions HTTP/1.1" 200 OK +(APIServer pid=1) INFO: 172.21.0.1:37132 - "POST /v1/chat/completions HTTP/1.1" 200 OK +(APIServer pid=1) INFO: 172.21.0.1:38250 - "POST /v1/chat/completions HTTP/1.1" 200 OK +(APIServer pid=1) INFO: 172.21.0.1:38262 - "POST /v1/chat/completions HTTP/1.1" 200 OK +(APIServer pid=1) INFO 09-18 03:28:29 [loggers.py:323] Engine 000: Avg prompt throughput: 643.6 tokens/s, Avg generation throughput: 68.6 tokens/s, Running: 1 reqs, Waiting: 0 reqs, GPU KV cache usage: 7.1%, Prefix cache hit rate: 84.2%, MM cache hit rate: 94.1% +(APIServer pid=1) INFO 09-18 03:28:39 [loggers.py:323] Engine 000: Avg prompt throughput: 0.0 tokens/s, Avg generation throughput: 77.2 tokens/s, Running: 1 reqs, Waiting: 0 reqs, GPU KV cache usage: 7.1%, Prefix cache hit rate: 84.2%, MM cache hit rate: 94.1% +(APIServer pid=1) INFO: 127.0.0.1:47208 - "GET /health HTTP/1.1" 200 OK +(APIServer pid=1) INFO 09-18 03:28:49 [loggers.py:323] Engine 000: Avg prompt throughput: 0.0 tokens/s, Avg generation throughput: 77.1 tokens/s, Running: 1 reqs, Waiting: 0 reqs, GPU KV cache usage: 7.1%, Prefix cache hit rate: 84.2%, MM cache hit rate: 94.1% +(APIServer pid=1) INFO: 172.21.0.1:37546 - "POST /v1/chat/completions HTTP/1.1" 200 OK +(APIServer pid=1) INFO 09-18 03:28:59 [loggers.py:323] Engine 000: Avg prompt throughput: 8.7 tokens/s, Avg generation throughput: 76.6 tokens/s, Running: 1 reqs, Waiting: 0 reqs, GPU KV cache usage: 7.1%, Prefix cache hit rate: 84.2%, MM cache hit rate: 94.1% +(APIServer pid=1) INFO 09-18 03:29:09 [loggers.py:323] Engine 000: Avg prompt throughput: 0.0 tokens/s, Avg generation throughput: 77.1 tokens/s, Running: 1 reqs, Waiting: 0 reqs, GPU KV cache usage: 7.1%, Prefix cache hit rate: 84.2%, MM cache hit rate: 94.1% +(APIServer pid=1) INFO: 127.0.0.1:40912 - "GET /health HTTP/1.1" 200 OK +(APIServer pid=1) INFO 09-18 03:29:19 [loggers.py:323] Engine 000: Avg prompt throughput: 0.0 tokens/s, Avg generation throughput: 77.2 tokens/s, Running: 1 reqs, Waiting: 0 reqs, GPU KV cache usage: 7.1%, Prefix cache hit rate: 84.2%, MM cache hit rate: 94.1% +(APIServer pid=1) INFO: 172.21.0.1:38312 - "POST /v1/chat/completions HTTP/1.1" 200 OK +(APIServer pid=1) INFO: 172.21.0.1:38320 - "POST /v1/chat/completions HTTP/1.1" 200 OK +(APIServer pid=1) INFO: 172.21.0.1:33350 - "POST /v1/chat/completions HTTP/1.1" 200 OK +(APIServer pid=1) INFO: 172.21.0.1:33362 - "POST /v1/chat/completions HTTP/1.1" 200 OK +(APIServer pid=1) INFO 09-18 03:29:29 [loggers.py:323] Engine 000: Avg prompt throughput: 643.8 tokens/s, Avg generation throughput: 68.6 tokens/s, Running: 1 reqs, Waiting: 0 reqs, GPU KV cache usage: 7.1%, Prefix cache hit rate: 84.8%, MM cache hit rate: 94.4% +(APIServer pid=1) INFO 09-18 03:29:39 [loggers.py:323] Engine 000: Avg prompt throughput: 0.0 tokens/s, Avg generation throughput: 77.2 tokens/s, Running: 1 reqs, Waiting: 0 reqs, GPU KV cache usage: 7.1%, Prefix cache hit rate: 84.8%, MM cache hit rate: 94.4% +(APIServer pid=1) INFO: 127.0.0.1:53680 - "GET /health HTTP/1.1" 200 OK +(APIServer pid=1) INFO 09-18 03:29:49 [loggers.py:323] Engine 000: Avg prompt throughput: 0.0 tokens/s, Avg generation throughput: 77.1 tokens/s, Running: 1 reqs, Waiting: 0 reqs, GPU KV cache usage: 7.1%, Prefix cache hit rate: 84.8%, MM cache hit rate: 94.4% +(APIServer pid=1) INFO 09-18 03:29:59 [loggers.py:323] Engine 000: Avg prompt throughput: 0.0 tokens/s, Avg generation throughput: 28.4 tokens/s, Running: 0 reqs, Waiting: 0 reqs, GPU KV cache usage: 0.0%, Prefix cache hit rate: 84.8%, MM cache hit rate: 94.4% +(APIServer pid=1) INFO: 172.21.0.1:46494 - "POST /v1/chat/completions HTTP/1.1" 200 OK +(APIServer pid=1) INFO: 172.21.0.1:46498 - "POST /v1/chat/completions HTTP/1.1" 200 OK +(APIServer pid=1) INFO: 172.21.0.1:54326 - "POST /v1/chat/completions HTTP/1.1" 200 OK +(APIServer pid=1) INFO 09-18 03:30:09 [loggers.py:323] Engine 000: Avg prompt throughput: 51.0 tokens/s, Avg generation throughput: 62.8 tokens/s, Running: 1 reqs, Waiting: 0 reqs, GPU KV cache usage: 7.1%, Prefix cache hit rate: 84.8%, MM cache hit rate: 94.4% +(APIServer pid=1) INFO: 172.21.0.1:54342 - "POST /v1/chat/completions HTTP/1.1" 200 OK +(APIServer pid=1) INFO: 127.0.0.1:54520 - "GET /health HTTP/1.1" 200 OK +(APIServer pid=1) INFO 09-18 03:30:19 [loggers.py:323] Engine 000: Avg prompt throughput: 12.3 tokens/s, Avg generation throughput: 76.6 tokens/s, Running: 1 reqs, Waiting: 0 reqs, GPU KV cache usage: 7.1%, Prefix cache hit rate: 84.8%, MM cache hit rate: 94.4% +(APIServer pid=1) INFO 09-18 03:30:29 [loggers.py:323] Engine 000: Avg prompt throughput: 0.0 tokens/s, Avg generation throughput: 77.1 tokens/s, Running: 1 reqs, Waiting: 0 reqs, GPU KV cache usage: 7.1%, Prefix cache hit rate: 84.8%, MM cache hit rate: 94.4% +(APIServer pid=1) INFO 09-18 03:30:39 [loggers.py:323] Engine 000: Avg prompt throughput: 0.0 tokens/s, Avg generation throughput: 77.0 tokens/s, Running: 1 reqs, Waiting: 0 reqs, GPU KV cache usage: 7.1%, Prefix cache hit rate: 84.8%, MM cache hit rate: 94.4% +(APIServer pid=1) INFO: 127.0.0.1:41504 - "GET /health HTTP/1.1" 200 OK +(APIServer pid=1) INFO 09-18 03:30:49 [loggers.py:323] Engine 000: Avg prompt throughput: 0.0 tokens/s, Avg generation throughput: 76.7 tokens/s, Running: 1 reqs, Waiting: 0 reqs, GPU KV cache usage: 7.1%, Prefix cache hit rate: 84.8%, MM cache hit rate: 94.4% +(APIServer pid=1) INFO 09-18 03:30:59 [loggers.py:323] Engine 000: Avg prompt throughput: 0.0 tokens/s, Avg generation throughput: 76.6 tokens/s, Running: 1 reqs, Waiting: 0 reqs, GPU KV cache usage: 8.3%, Prefix cache hit rate: 84.8%, MM cache hit rate: 94.4% +(APIServer pid=1) INFO 09-18 03:31:09 [loggers.py:323] Engine 000: Avg prompt throughput: 0.0 tokens/s, Avg generation throughput: 76.5 tokens/s, Running: 1 reqs, Waiting: 0 reqs, GPU KV cache usage: 8.3%, Prefix cache hit rate: 84.8%, MM cache hit rate: 94.4% +(APIServer pid=1) INFO: 127.0.0.1:45670 - "GET /health HTTP/1.1" 200 OK +(APIServer pid=1) INFO 09-18 03:31:19 [loggers.py:323] Engine 000: Avg prompt throughput: 0.0 tokens/s, Avg generation throughput: 76.5 tokens/s, Running: 1 reqs, Waiting: 0 reqs, GPU KV cache usage: 8.3%, Prefix cache hit rate: 84.8%, MM cache hit rate: 94.4% +(APIServer pid=1) INFO 09-18 03:31:29 [loggers.py:323] Engine 000: Avg prompt throughput: 0.0 tokens/s, Avg generation throughput: 76.6 tokens/s, Running: 1 reqs, Waiting: 0 reqs, GPU KV cache usage: 8.3%, Prefix cache hit rate: 84.8%, MM cache hit rate: 94.4% +(APIServer pid=1) INFO 09-18 03:31:39 [loggers.py:323] Engine 000: Avg prompt throughput: 0.0 tokens/s, Avg generation throughput: 76.5 tokens/s, Running: 1 reqs, Waiting: 0 reqs, GPU KV cache usage: 9.5%, Prefix cache hit rate: 84.8%, MM cache hit rate: 94.4% +(APIServer pid=1) INFO: 127.0.0.1:37016 - "GET /health HTTP/1.1" 200 OK +(APIServer pid=1) INFO 09-18 03:31:49 [loggers.py:323] Engine 000: Avg prompt throughput: 0.0 tokens/s, Avg generation throughput: 76.5 tokens/s, Running: 1 reqs, Waiting: 0 reqs, GPU KV cache usage: 9.5%, Prefix cache hit rate: 84.8%, MM cache hit rate: 94.4% +(APIServer pid=1) INFO: 172.21.0.1:54348 - "POST /v1/chat/completions HTTP/1.1" 200 OK +(APIServer pid=1) INFO 09-18 03:31:59 [loggers.py:323] Engine 000: Avg prompt throughput: 13.4 tokens/s, Avg generation throughput: 76.0 tokens/s, Running: 1 reqs, Waiting: 0 reqs, GPU KV cache usage: 7.1%, Prefix cache hit rate: 84.8%, MM cache hit rate: 94.4% +(APIServer pid=1) INFO: 172.21.0.1:33524 - "POST /v1/chat/completions HTTP/1.1" 200 OK +(APIServer pid=1) INFO: 172.21.0.1:33196 - "POST /v1/chat/completions HTTP/1.1" 200 OK diff --git a/audit/qualification/baseline.jsonl b/audit/qualification/baseline.jsonl new file mode 100644 index 0000000..beef733 --- /dev/null +++ b/audit/qualification/baseline.jsonl @@ -0,0 +1,92 @@ +{"name": "arithmetic-0", "passed": true, "finish": "stop", "usage": {"prompt_tokens": 59, "total_tokens": 141, "completion_tokens": 82, "completion_tokens_details": {"reasoning_tokens": 75}}, "ttft_s": 0.07743733799998154, "elapsed_s": 1.1465266220002377, "decode_tps": 75.7654212910253, "max_stream_gap_s": 0.03931110200028343, "events": 79, "content": "\n\n3973", "reasoning": "I need to compute 137 * 29 + 0.\n\n137 * 29 = 137 * 30 - 137 = 4110 - 137 = 3973\n\nAdding 0 doesn't change anything, so the answer is 3973.\n", "sha256": "d929fd317464b07ece811a135b5937af8eb36ea0b1e88ec82bc5137102d0a62c"} +{"name": "arithmetic-1", "passed": true, "finish": "stop", "usage": {"prompt_tokens": 59, "total_tokens": 96, "completion_tokens": 37, "completion_tokens_details": {"reasoning_tokens": 30}}, "ttft_s": 0.07911375900039275, "elapsed_s": 0.5503786250001212, "decode_tps": 76.39016314876471, "max_stream_gap_s": 0.0391702790002455, "events": 34, "content": "\n\n5761", "reasoning": "160 * 36 = 5760. 5760 + 1 = 5761.\n", "sha256": "5cd050422245af84010bf1c71082001dcd6d880bdd6dd7002e69a8402ab6c73c"} +{"name": "arithmetic-2", "passed": true, "finish": "stop", "usage": {"prompt_tokens": 59, "total_tokens": 125, "completion_tokens": 66, "completion_tokens_details": {"reasoning_tokens": 59}}, "ttft_s": 0.07782649199998559, "elapsed_s": 0.9297517340000923, "decode_tps": 76.297774494152, "max_stream_gap_s": 0.03924396999991586, "events": 63, "content": "\n\n7871", "reasoning": "183 * 43 = 183 * 40 + 183 * 3 = 7320 + 549 = 7869. Then 7869 + 2 = 7871.\n", "sha256": "c6dabe0673abae92ebdac3d245deb7c9ca89c874d316c46b8ce09fde923a752f"} +{"name": "arithmetic-3", "passed": true, "finish": "stop", "usage": {"prompt_tokens": 59, "total_tokens": 101, "completion_tokens": 42, "completion_tokens_details": {"reasoning_tokens": 34}}, "ttft_s": 0.07816320399979304, "elapsed_s": 0.6155883440005709, "decode_tps": 76.28969497024396, "max_stream_gap_s": 0.039318773000559304, "events": 39, "content": "\n\n10303", "reasoning": "206 * 50 = 10300, then 10300 + 3 = 10303.\n", "sha256": "faf8742035a51aed0549e27e042a16ddce400e4a9f6cb41b47540844a9fde0a7"} +{"name": "arithmetic-4", "passed": true, "finish": "stop", "usage": {"prompt_tokens": 59, "total_tokens": 131, "completion_tokens": 72, "completion_tokens_details": {"reasoning_tokens": 64}}, "ttft_s": 0.07829741899968212, "elapsed_s": 1.0084289859996716, "decode_tps": 76.33328715957965, "max_stream_gap_s": 0.03928925200034428, "events": 69, "content": "\n\n13057", "reasoning": "229 * 57 = 229 * 50 + 229 * 7 = 11450 + 1603 = 13053. Then 13053 + 4 = 13057.\n", "sha256": "5c8a761b69d32970d9e4021e9cddda2caf44a6201a9bf34e503844cd2ce1dc0c"} +{"name": "arithmetic-5", "passed": true, "finish": "stop", "usage": {"prompt_tokens": 59, "total_tokens": 100, "completion_tokens": 41, "completion_tokens_details": {"reasoning_tokens": 33}}, "ttft_s": 0.0779811049997079, "elapsed_s": 0.6016614189993561, "decode_tps": 76.38247787948521, "max_stream_gap_s": 0.0393025610001132, "events": 38, "content": "\n\n16133", "reasoning": "252 * 64 = 16128. 16128 + 5 = 16133.\n", "sha256": "d7ffeebe2bcd01af72cd619ced3e33606c067dbbcee20babfbe1aded61293b92"} +{"name": "arithmetic-6", "passed": true, "finish": "stop", "usage": {"prompt_tokens": 59, "total_tokens": 130, "completion_tokens": 71, "completion_tokens_details": {"reasoning_tokens": 63}}, "ttft_s": 0.0780715090004378, "elapsed_s": 0.9949718440002471, "decode_tps": 76.34417540049711, "max_stream_gap_s": 0.03927524299979268, "events": 68, "content": "\n\n19531", "reasoning": "275 * 71 = 275 * 70 + 275 * 1 = 19250 + 275 = 19525. Then 19525 + 6 = 19531.\n", "sha256": "7ac565b6322b904ff447bc9ef3ed069ebc4e48b8a51e72f9e18919bd8e3000d1"} +{"name": "arithmetic-7", "passed": true, "finish": "stop", "usage": {"prompt_tokens": 59, "total_tokens": 153, "completion_tokens": 94, "completion_tokens_details": {"reasoning_tokens": 86}}, "ttft_s": 0.07872538700030418, "elapsed_s": 1.2967335769999409, "decode_tps": 76.35416638703205, "max_stream_gap_s": 0.039238423000824696, "events": 91, "content": "\n\n23251", "reasoning": "298 * 78 = 298 * 78. Let me compute this step by step.\n\n298 * 78 = 298 * 80 - 298 * 2 = 23840 - 596 = 23244\n\n23244 + 7 = 23251\n", "sha256": "e7b73eb708d2c4c7d2fc5dca2aed40a571c8b0445cd1867d916b47f57ec4cac0"} +{"name": "arithmetic-8", "passed": true, "finish": "stop", "usage": {"prompt_tokens": 59, "total_tokens": 144, "completion_tokens": 85, "completion_tokens_details": {"reasoning_tokens": 77}}, "ttft_s": 0.07762592699964443, "elapsed_s": 1.178134264999244, "decode_tps": 76.32836308417917, "max_stream_gap_s": 0.039215871000124025, "events": 82, "content": "\n\n27293", "reasoning": "Let me compute 321 * 85 + 8.\n\n321 * 85 = 321 * 80 + 321 * 5 = 25680 + 1605 = 27285\n\n27285 + 8 = 27293\n", "sha256": "9d93671c15eac2005a27c4fe38e5d4285b3feca77fa1e90f138c71a2b5164db8"} +{"name": "arithmetic-9", "passed": true, "finish": "stop", "usage": {"prompt_tokens": 59, "total_tokens": 143, "completion_tokens": 84, "completion_tokens_details": {"reasoning_tokens": 76}}, "ttft_s": 0.07886091300042608, "elapsed_s": 1.1658563040000445, "decode_tps": 76.35726948545005, "max_stream_gap_s": 0.039240535999852, "events": 81, "content": "\n\n31657", "reasoning": "Let me compute 344 * 92 + 9.\n\n344 * 92 = 344 * 90 + 344 * 2 = 30960 + 688 = 31648\n\n31648 + 9 = 31657\n", "sha256": "6b95a8b82a4ba5a8335b5f10899663aeebe71fed7e5673836b82ec74cfa1cbee"} +{"name": "arithmetic-10", "passed": true, "finish": "stop", "usage": {"prompt_tokens": 60, "total_tokens": 143, "completion_tokens": 83, "completion_tokens_details": {"reasoning_tokens": 75}}, "ttft_s": 0.07784162200005085, "elapsed_s": 1.1516468820000227, "decode_tps": 76.36393958435457, "max_stream_gap_s": 0.03926086299998133, "events": 80, "content": "\n\n36343", "reasoning": "Let me compute 367 * 99 + 10.\n\n367 * 99 = 367 * (100 - 1) = 36700 - 367 = 36333\n\n36333 + 10 = 36343\n", "sha256": "662f32768d742ea67e45b8109190fc2ba55c429346f4be9b9492ff457cdacc48"} +{"name": "arithmetic-11", "passed": true, "finish": "stop", "usage": {"prompt_tokens": 61, "total_tokens": 151, "completion_tokens": 90, "completion_tokens_details": {"reasoning_tokens": 82}}, "ttft_s": 0.07834389199979341, "elapsed_s": 1.24362464800015, "decode_tps": 76.3764436524967, "max_stream_gap_s": 0.03930683700036752, "events": 87, "content": "\n\n41351", "reasoning": "Let me compute 390 * 106 + 11.\n\n390 * 106 = 390 * 100 + 390 * 6 = 39000 + 2340 = 41340\n\n41340 + 11 = 41351\n", "sha256": "cdbf70494a3484ed891137af4bad12b7417009befd921930fc1fa5e5e3907e27"} +{"name": "json", "passed": true, "finish": "stop", "usage": {"prompt_tokens": 66, "total_tokens": 109, "completion_tokens": 43, "completion_tokens_details": {"reasoning_tokens": 26}}, "ttft_s": 0.07823446200018225, "elapsed_s": 0.6285332779998498, "decode_tps": 76.32217038974217, "max_stream_gap_s": 0.03929639199941448, "events": 40, "content": "\n\n{\"name\":\"设备A\",\"count\":7,\"enabled\":true}", "reasoning": "The user wants me to return exactly a specific JSON object with no markdown formatting. Let me provide exactly what they asked for.\n", "sha256": "2acba5868b21fa90ff7d281b66898b4ed66b6ff2f42a3d5a1d5bd11569806949"} +{"name": "multiturn", "passed": true, "finish": "stop", "usage": {"prompt_tokens": 82, "total_tokens": 146, "completion_tokens": 64, "completion_tokens_details": {"reasoning_tokens": 52}}, "ttft_s": 0.07760646900078427, "elapsed_s": 0.9026451710005858, "decode_tps": 76.36005419781526, "max_stream_gap_s": 0.0392437800001062, "events": 61, "content": "\n\nAQUA-58321", "reasoning": "The user is asking me to recall the code they previously told me to remember. They said \"Remember the code AQUA-58321.\" and now they're asking what the code was. They want me to reply only with the code.\n", "sha256": "5a11c72c78b0b7d9d0ec352a11aa21a4fa1ea34ff915277ee160bbb83bbb1dd6"} +{"name": "image-ocr", "passed": true, "finish": "stop", "usage": {"prompt_tokens": 3135, "total_tokens": 3276, "completion_tokens": 141, "completion_tokens_details": {"reasoning_tokens": 102}}, "ttft_s": 0.37004565999995975, "elapsed_s": 2.196252763999837, "decode_tps": 76.66162271155495, "max_stream_gap_s": 0.039173914000457444, "events": 138, "content": "\n\nVerification code: SPARK-82741\nSum of amounts: 1368 (125 + 478 + 765)", "reasoning": "The user wants the verification code and the sum of three amounts from the invoice image. Reading the image: Verification code is SPARK-82741. Amounts are 125, 478, 765. Sum: 125 + 478 = 603; 603 + 765 = 1368. I need to provide a clean, self-contained response with the code and the sum.\n", "sha256": "d5a5a6c06ec094144e5e0fb001e4373098f6dd3e7092d26288c9302adb505839"} +{"name": "video-120s", "passed": true, "finish": "stop", "usage": {"prompt_tokens": 12069, "total_tokens": 12208, "completion_tokens": 139, "completion_tokens_details": {"reasoning_tokens": 125}}, "ttft_s": 1.4559775520001494, "elapsed_s": 3.255883521999749, "decode_tps": 76.67067185739191, "max_stream_gap_s": 0.03919006599971908, "events": 136, "content": "\n\nRed, Green, Blue, Yellow, Purple, White", "reasoning": "The user wants the six background colors in chronological order from the video frames. Let me trace through the timestamps: 0-19s RED, 20-39s GREEN, 40-59s BLUE, 60-79s YELLOW, 80-99s PURPLE, 100-119s WHITE. That's six colors. The instruction says reply only with English color names. I'll provide them in order, comma-separated or as a list. Since \"reply only with the English color names,\" I'll give a clean list.\n", "sha256": "c21acd6deeb23d270c1265190bca80eff9b3441dc81795bc98e0a8e18987ff7b"} +{"name": "multi-image", "passed": true, "finish": "stop", "usage": {"prompt_tokens": 12356, "total_tokens": 12427, "completion_tokens": 71, "completion_tokens_details": {"reasoning_tokens": 60}}, "ttft_s": 1.3964284699995915, "elapsed_s": 2.3080328639998697, "decode_tps": 76.78769481663845, "max_stream_gap_s": 0.039229910999893036, "events": 68, "content": "\n\nSPARK-82741", "reasoning": "The user wants the verification code from the invoice. All four copies show the same line: \"Verification: SPARK-82741\". The instruction is to reply only with the code. So the answer is SPARK-82741. No need for elaboration.\n", "sha256": "c4d4ac93f59330deca1f3c5fe9842a60a2ac31170fdbc40f311385c571ed9024"} +{"name": "warmup-code", "passed": true, "finish": "length", "usage": {"prompt_tokens": 77, "total_tokens": 333, "completion_tokens": 256, "completion_tokens_details": {"reasoning_tokens": 256}}, "ttft_s": 0.07801521100009268, "elapsed_s": 3.3847212519995082, "decode_tps": 77.11601722024527, "max_stream_gap_s": 0.013402724999650673, "events": 256, "content": "", "reasoning": "We need answer user's request: \"Write a production-quality Python CSV import utility with validation, duplicate handling, error reporting, unit tests, and a detailed explanation.\"\n\nNeed produce final answer with code, tests, explanation. Need likely include a complete utility. Need think through design.\n\nWe need satisfy: production-quality Python CSV import utility with validation, duplicate handling, error reporting, unit tests, detailed explanation.\n\nWe can provide a single module maybe csv_importer.py with dataclasses, exceptions, validators, duplicate strategies, error report, CLI? Unit tests using unittest or pytest. Need detailed explanation.\n\nNeed ensure code is correct. Let's design robust CSV import utility.\n\nRequirements:\n- Import CSV into records, validate rows against schema.\n- Duplicate handling: detect duplicates by key columns, strategies: skip, update, fail, maybe replace? Need transactional? Could use in-memory repository or callback.\n- Error reporting: collect row-level errors, line numbers, field errors, summary, maybe CSV/JSON report.\n- Unit tests: cover valid, invalid, duplicates, encoding, BOM, delimiter, quoting, missing columns, extra columns, type conversion, custom validators, duplicate strategies, error report.\n- Production-quality: type hints", "sha256": "cbe9e9daaa6d8bd684a13ee763690c691bb78147100ac38b4d701f700d3abc62"} +{"name": "perf-code-0", "passed": true, "finish": "length", "usage": {"prompt_tokens": 77, "total_tokens": 2125, "completion_tokens": 2048, "completion_tokens_details": {"reasoning_tokens": 2048}}, "ttft_s": 0.06954696299999341, "elapsed_s": 26.62168105300043, "decode_tps": 77.09361488841316, "max_stream_gap_s": 0.013473821999468782, "events": 2048, "content": "", "reasoning": "We need answer user's request: \"Write a production-quality Python CSV import utility with validation, duplicate handling, error reporting, unit tests, and a detailed explanation.\"\n\nNeed produce final answer with code, tests, explanation. Need likely include a complete utility. Need think through design.\n\nWe need satisfy: production-quality Python CSV import utility with validation, duplicate handling, error reporting, unit tests, detailed explanation.\n\nWe can provide a single module maybe csv_importer.py with dataclasses, exceptions, validators, duplicate strategies, error report, CLI? Unit tests using unittest or pytest. Need detailed explanation.\n\nNeed ensure code is correct. Let's design robust CSV import utility.\n\nRequirements:\n- Import CSV into records, validate rows against schema.\n- Duplicate handling: detect duplicates by key columns, strategies: skip, update, fail, maybe replace? Need transactional? Could use in-memory repository or callback.\n- Error reporting: collect row-level errors, line numbers, field errors, summary, maybe CSV/JSON report.\n- Unit tests: cover valid, invalid, duplicates, encoding, BOM, delimiter, quoting, missing columns, extra columns, type conversion, custom validators, duplicate strategies, error report.\n- Production-quality: type hints, logging, configuration, streaming, memory efficient, context manager, atomic? Maybe use sqlite? But utility generic. Could include repository protocol.\n\nNeed decide scope. Could write a module `csv_importer.py` with:\n- `FieldSpec`: name, required, type, regex, choices, min/max, default, transform, validator callable.\n- `Schema`: fields, key_fields, allow_extra_fields, strict? maybe `required_fields` derived.\n- `DuplicateStrategy`: SKIP, UPDATE, FAIL, REPLACE? For import into repository.\n- `ImportResult`: total_rows, inserted, updated, skipped, errors, error_report.\n- `ImportError`? `ValidationError`? `RowError` dataclass.\n- `CsvImportOptions`: delimiter, quotechar, encoding, has_header, max_errors, etc.\n- `CsvImporter`: init(schema, repository, options, duplicate_strategy, logger). `import_file(path)` or `import_stream(stream)`.\n- Repository protocol: `get_by_key(key)`, `insert(record)`, `update(record)`, maybe `exists(key)`. For tests use InMemoryRepository.\n- Validation: parse header, check required columns present. For each row: ensure length? csv.DictReader handles missing as None, extra as None key? Need handle.\n- Type conversion: str, int, float, bool, date, datetime, Decimal? Use standard.\n- Duplicate handling: key tuple from record. If existing: strategy. If duplicate within file: need track seen keys in current import to avoid duplicates in same file. Could treat as duplicate with strategy. For update, if same key appears later, update existing? Need careful.\n- Error reporting: `RowError(line_number, row_number, raw_row, field, message, code)`. `ImportErrorReport` with add, to_csv, to_json, summary.\n- Logging.\n- CLI maybe optional. Could include `main` for import file to JSON? But maybe too much. Production utility could include CLI. But answer length large. We can include module and tests.\n\nNeed ensure code runs. Let's draft code.\n\nPotential code:\n\nfrom __future__ import annotations\nimport csv, json, logging, re, datetime, decimal, io, os, tempfile, typing as t\nfrom dataclasses import dataclass, field\nfrom enum import Enum\nfrom pathlib import Path\nfrom typing import Any, Callable, Iterable, Mapping, Protocol, Sequence\n\nclass DuplicateStrategy(str, Enum):\n FAIL = \"fail\"\n SKIP = \"skip\"\n UPDATE = \"update\"\n REPLACE = \"replace\" # maybe same as update? Could define replace deletes then inserts? Repository may not support delete. Use update.\n\nMaybe strategies: FAIL, SKIP, UPDATE.\n\nFieldSpec:\n@dataclass(frozen=True)\nclass FieldSpec:\n name: str\n required: bool = False\n type: str = \"str\" # str,int,float,bool,date,datetime,decimal\n regex: str | None = None\n choices: tuple[str,...] | None = None\n min_value: float | None = None\n max_value: float | None = None\n default: Any = None\n transform: Callable[[str], str] | None = None\n validator: Callable[[Any], None] | None = None\n\nBut frozen with callable okay.\n\nSchema:\n@dataclass(frozen=True)\nclass Schema:\n fields: tuple[FieldSpec,...]\n key_fields: tuple[str,...] = ()\n allow_extra_fields: bool = False\n strict_row_length: bool = True? DictReader doesn't give length. Could use csv.reader for row length. But DictReader easier. For extra fields, DictReader puts extras under None key. Need detect.\n\nMaybe use csv.reader to get raw rows and header. This allows row length validation and raw row for error report. But DictReader simpler. For production, use csv.reader and map manually.\n\nImplementation:\n- open file with newline='', encoding.\n- reader = csv.reader(f, delimiter, quotechar, skipinitialspace?)\n- header = next(reader, None). If None: error empty file.\n- normalize header? strip? maybe option normalize_headers=True.\n- map header names to indices. Check duplicates in header? error.\n- Check required schema fields present in header. If not, raise SchemaError? Or return error report? For production, fail fast with ImportSchemaError.\n- If not allow_extra_fields, check unknown header fields? Could collect error and fail.\n- For each row enumerate starting line 2. Need handle blank lines? csv.reader returns [] for blank? Actually blank line returns []? In csv.reader, blank line returns []? It may return [] for empty line. We can skip blank rows? Option skip_blank_rows=True. If row == [] skip.\n- If strict_row_length and len(row) != len(header): error. But if row shorter, missing fields; if longer, extra. Could still validate available? For error reporting, add row error and continue.\n- Build raw dict: for i, name in enumerate(header): value = row[i] if i < len(row) else None. Extra values if len(row)>len(header) maybe under None? We can add error.\n- Validate fields: for each field spec:\n raw = raw.get(name)\n if raw is None or raw == ''? Treat empty as missing. If required: error. Else if empty: value = default (if default not None) else None.\n else transform strip? Option strip_values=True.\n convert type.\n validate regex, choices, min/max, custom validator.\n- If errors: add RowError for each field? Could aggregate.\n- If no errors: record = {field.name: value for field in fields} plus maybe extra fields if allowed? Could include extras? Maybe only schema fields.\n- Duplicate handling:\n key = tuple(record.get(k) for k in schema.key_fields) if key_fields else None.\n If key is not None:\n if key in seen_keys: duplicate within file.\n existing = repository.get_by_key(key) if repository else None.\n if existing is not None or key in seen_keys:\n if strategy FAIL: add error duplicate; continue\n if SKIP: skipped +=1; continue\n if UPDATE: if repository: repository.update(record); updated +=1; seen_keys.add(key); continue\n else: repository.insert(record); inserted +=1; seen_keys.add(key)\n else: repository.insert(record); inserted +=1.\n- Need repository protocol:\nclass Repository(Protocol):\n def get_by_key(self, key: tuple[Any,...]) -> Mapping[str, Any] | None: ...\n def insert(self, record: Mapping[str, Any]) -> None: ...\n def update(self, record: Mapping[str, Any]) -> None: ...\nMaybe update should accept key? If record contains key fields, repository can update by key.\n\nInMemoryRepository:\n def __init__(self, key_fields): self._store = {}; self.key_fields = key_fields\n def _key(self, record): return tuple(record.get(k) for k in self.key_fields)\n get_by_key, insert (raise DuplicateKeyError if exists), update (raise NotFoundError if not exists), all.\n\nError report:\n@dataclass\nclass RowError:\n line_number: int\n row_number: int\n field: str | None\n code: str\n message: str\n raw_row: list[str] | None = None\n\n@dataclass\nclass ImportErrorReport:\n errors: list[RowError] = field(default_factory=list)\n max_errors: int | None = None\n def add(...): if max_errors and len >= max_errors: maybe raise MaxErrorsExceeded? Or stop? Could set truncated.\n def to_json(self): ...\n def to_csv(self): ...\n def summary(self): ...\n\nImportResult:\n total_rows, inserted, updated, skipped, errors, report, started_at, finished_at? Maybe.\n\nExceptions:\nclass CsvImportError(Exception)\nclass SchemaError(CsvImportError)\nclass RepositoryError(CsvImportError)\nclass DuplicateKeyError(RepositoryError)\nclass RecordNotFoundError(RepositoryError)\nclass MaxErrorsExceeded(CsvImportError)\n\nType conversion:\n", "sha256": "8189166f02d582d26c4e5a94a3cc24bcd3ef692edef5d75734f4d3bc74cbb345"} +{"name": "perf-code-1", "passed": true, "finish": "length", "usage": {"prompt_tokens": 77, "total_tokens": 2125, "completion_tokens": 2048, "completion_tokens_details": {"reasoning_tokens": 2048}}, "ttft_s": 0.0694820829994569, "elapsed_s": 26.61911036699985, "decode_tps": 77.10089113502143, "max_stream_gap_s": 0.013594887999715866, "events": 2048, "content": "", "reasoning": "We need answer user's request: \"Write a production-quality Python CSV import utility with validation, duplicate handling, error reporting, unit tests, and a detailed explanation.\"\n\nNeed produce final answer with code, tests, explanation. Need likely include a complete utility. Need think through design.\n\nWe need satisfy: production-quality Python CSV import utility with validation, duplicate handling, error reporting, unit tests, detailed explanation.\n\nWe can provide a single module maybe csv_importer.py with dataclasses, exceptions, validators, duplicate strategies, error report, CLI? Unit tests using unittest or pytest. Need detailed explanation.\n\nNeed ensure code is correct. Let's design robust CSV import utility.\n\nRequirements:\n- Import CSV into records, validate rows against schema.\n- Duplicate handling: detect duplicates by key columns, strategies: skip, update, fail, maybe replace? Need transactional? Could use in-memory repository or callback.\n- Error reporting: collect row-level errors, line numbers, field errors, summary, maybe CSV/JSON report.\n- Unit tests: cover valid, invalid, duplicates, encoding, BOM, delimiter, quoting, missing columns, extra columns, type conversion, custom validators, duplicate strategies, error report.\n- Production-quality: type hints, logging, configuration, streaming, memory efficient, context manager, atomic? Maybe use sqlite? But utility generic. Could include repository protocol.\n\nNeed decide scope. Could write a module `csv_importer.py` with:\n- `FieldSpec`: name, required, type, regex, choices, min/max, default, transform, validator callable.\n- `Schema`: fields, key_fields, allow_extra_fields, strict? maybe `required_fields` derived.\n- `DuplicateStrategy`: SKIP, UPDATE, FAIL, REPLACE? For import into repository.\n- `ImportResult`: total_rows, inserted, updated, skipped, errors, error_report.\n- `ImportError`? `ValidationError`? `RowError` dataclass.\n- `CsvImportOptions`: delimiter, quotechar, encoding, has_header, max_errors, etc.\n- `CsvImporter`: init(schema, repository, options, duplicate_strategy, logger). `import_file(path)` or `import_stream(stream)`.\n- Repository protocol: `get_by_key(key)`, `insert(record)`, `update(record)`, maybe `exists(key)`. For tests use InMemoryRepository.\n- Validation: parse header, check required columns present. For each row: ensure length? csv.DictReader handles missing as None, extra as None key? Need handle.\n- Type conversion: str, int, float, bool, date, datetime, Decimal? Use standard.\n- Duplicate handling: key tuple from record. If existing: strategy. If duplicate within file: need track seen keys in current import to avoid duplicates in same file. Could treat as duplicate with strategy. For update, if same key appears later, update existing? Need careful.\n- Error reporting: `RowError(line_number, row_number, raw_row, field, message, code)`. `ImportErrorReport` with add, to_csv, to_json, summary.\n- Logging.\n- CLI maybe optional. Could include `main` for import file to JSON? But maybe too much. Production utility could include CLI. But answer length large. We can include module and tests.\n\nNeed ensure code runs. Let's draft code.\n\nPotential code:\n\nfrom __future__ import annotations\nimport csv, json, logging, re, datetime, decimal, io, os, tempfile, typing as t\nfrom dataclasses import dataclass, field\nfrom enum import Enum\nfrom pathlib import Path\nfrom typing import Any, Callable, Iterable, Mapping, Protocol, Sequence\n\nclass DuplicateStrategy(str, Enum):\n FAIL = \"fail\"\n SKIP = \"skip\"\n UPDATE = \"update\"\n REPLACE = \"replace\" # maybe same as update? Could define replace deletes then inserts? Repository may not support delete. Use update.\n\nMaybe strategies: FAIL, SKIP, UPDATE.\n\nFieldSpec:\n@dataclass(frozen=True)\nclass FieldSpec:\n name: str\n required: bool = False\n type: str = \"str\" # str,int,float,bool,date,datetime,decimal\n regex: str | None = None\n choices: tuple[str,...] | None = None\n min_value: float | None = None\n max_value: float | None = None\n default: Any = None\n transform: Callable[[str], str] | None = None\n validator: Callable[[Any], None] | None = None\n\nBut frozen with callable okay.\n\nSchema:\n@dataclass(frozen=True)\nclass Schema:\n fields: tuple[FieldSpec,...]\n key_fields: tuple[str,...] = ()\n allow_extra_fields: bool = False\n strict_row_length: bool = True? DictReader doesn't give length. Could use csv.reader for row length. But DictReader easier. For extra fields, DictReader puts extras under None key. Need detect.\n\nMaybe use csv.reader to get raw rows and header. This allows row length validation and raw row for error report. But DictReader simpler. For production, use csv.reader and map manually.\n\nImplementation:\n- open file with newline='', encoding.\n- reader = csv.reader(f, delimiter, quotechar, skipinitialspace?)\n- header = next(reader, None). If None: error empty file.\n- normalize header? strip? maybe option normalize_headers=True.\n- map header names to indices. Check duplicates in header? error.\n- Check required schema fields present in header. If not, raise SchemaError? Or return error report? For production, fail fast with ImportSchemaError.\n- If not allow_extra_fields, check unknown header fields? Could collect error and fail.\n- For each row enumerate starting line 2. Need handle blank lines? csv.reader returns [] for blank? Actually blank line returns []? In csv.reader, blank line returns []? It may return [] for empty line. We can skip blank rows? Option skip_blank_rows=True. If row == [] skip.\n- If strict_row_length and len(row) != len(header): error. But if row shorter, missing fields; if longer, extra. Could still validate available? For error reporting, add row error and continue.\n- Build raw dict: for i, name in enumerate(header): value = row[i] if i < len(row) else None. Extra values if len(row)>len(header) maybe under None? We can add error.\n- Validate fields: for each field spec:\n raw = raw.get(name)\n if raw is None or raw == ''? Treat empty as missing. If required: error. Else if empty: value = default (if default not None) else None.\n else transform strip? Option strip_values=True.\n convert type.\n validate regex, choices, min/max, custom validator.\n- If errors: add RowError for each field? Could aggregate.\n- If no errors: record = {field.name: value for field in fields} plus maybe extra fields if allowed? Could include extras? Maybe only schema fields.\n- Duplicate handling:\n key = tuple(record.get(k) for k in schema.key_fields) if key_fields else None.\n If key is not None:\n if key in seen_keys: duplicate within file.\n existing = repository.get_by_key(key) if repository else None.\n if existing is not None or key in seen_keys:\n if strategy FAIL: add error duplicate; continue\n if SKIP: skipped +=1; continue\n if UPDATE: if repository: repository.update(record); updated +=1; seen_keys.add(key); continue\n else: repository.insert(record); inserted +=1; seen_keys.add(key)\n else: repository.insert(record); inserted +=1.\n- Need repository protocol:\nclass Repository(Protocol):\n def get_by_key(self, key: tuple[Any,...]) -> Mapping[str, Any] | None: ...\n def insert(self, record: Mapping[str, Any]) -> None: ...\n def update(self, record: Mapping[str, Any]) -> None: ...\nMaybe update should accept key? If record contains key fields, repository can update by key.\n\nInMemoryRepository:\n def __init__(self, key_fields): self._store = {}; self.key_fields = key_fields\n def _key(self, record): return tuple(record.get(k) for k in self.key_fields)\n get_by_key, insert (raise DuplicateKeyError if exists), update (raise NotFoundError if not exists), all.\n\nError report:\n@dataclass\nclass RowError:\n line_number: int\n row_number: int\n field: str | None\n code: str\n message: str\n raw_row: list[str] | None = None\n\n@dataclass\nclass ImportErrorReport:\n errors: list[RowError] = field(default_factory=list)\n max_errors: int | None = None\n def add(...): if max_errors and len >= max_errors: maybe raise MaxErrorsExceeded? Or stop? Could set truncated.\n def to_json(self): ...\n def to_csv(self): ...\n def summary(self): ...\n\nImportResult:\n total_rows, inserted, updated, skipped, errors, report, truncated?\n\nExceptions:\nclass CsvImportError(Exception)\nclass SchemaError(CsvImportError)\nclass MaxErrorsExceeded(CsvImportError)\nclass DuplicateKeyError(CsvImportError)\nclass RecordNotFoundError(CsvImportError)\n\nType conversion:\ndef _convert(value, type_name, field_name):\n ", "sha256": "7a66ea3f67718a8f33e49320a131bc2a4c15fa722f1eec4894255f5bb9842532"} +{"name": "perf-code-2", "passed": true, "finish": "length", "usage": {"prompt_tokens": 77, "total_tokens": 2125, "completion_tokens": 2048, "completion_tokens_details": {"reasoning_tokens": 2048}}, "ttft_s": 0.06918037900049967, "elapsed_s": 26.619741354000325, "decode_tps": 77.09818266843658, "max_stream_gap_s": 0.013516140999854542, "events": 2048, "content": "", "reasoning": "We need answer user's request: \"Write a production-quality Python CSV import utility with validation, duplicate handling, error reporting, unit tests, and a detailed explanation.\"\n\nNeed produce final answer with code, tests, explanation. Need likely include a complete utility. Need think through design.\n\nWe need satisfy: production-quality Python CSV import utility with validation, duplicate handling, error reporting, unit tests, detailed explanation.\n\nWe can provide a single module maybe csv_importer.py with dataclasses, exceptions, validators, duplicate strategies, error report, CLI? Unit tests using unittest or pytest. Need detailed explanation.\n\nNeed ensure code is correct. Let's design robust CSV import utility.\n\nRequirements:\n- Import CSV into records, validate rows against schema.\n- Duplicate handling: detect duplicates by key columns, strategies: skip, update, fail, maybe replace? Need transactional? Could use in-memory repository or callback.\n- Error reporting: collect row-level errors, line numbers, field errors, summary, maybe CSV/JSON report.\n- Unit tests: cover valid, invalid, duplicates, encoding, BOM, delimiter, quoting, missing columns, extra columns, type conversion, custom validators, duplicate strategies, error report.\n- Production-quality: type hints, logging, configuration, streaming, memory efficient, context manager, atomic? Maybe use sqlite? But utility generic. Could include repository protocol.\n\nNeed decide scope. Could write a module `csv_importer.py` with:\n- `FieldSpec`: name, required, type, regex, choices, min/max, default, transform, validator callable.\n- `Schema`: fields, key_fields, allow_extra_fields, strict? maybe `required_fields` derived.\n- `DuplicateStrategy`: SKIP, UPDATE, FAIL, REPLACE? For import into repository.\n- `ImportResult`: total_rows, inserted, updated, skipped, errors, error_report.\n- `ImportError`? `ValidationError`? `RowError` dataclass.\n- `CsvImportOptions`: delimiter, quotechar, encoding, has_header, max_errors, etc.\n- `CsvImporter`: init(schema, repository, options, duplicate_strategy, logger). `import_file(path)` or `import_stream(stream)`.\n- Repository protocol: `get_by_key(key)`, `insert(record)`, `update(record)`, maybe `exists(key)`. For tests use InMemoryRepository.\n- Validation: parse header, check required columns present. For each row: ensure length? csv.DictReader handles missing as None, extra as None key? Need handle.\n- Type conversion: str, int, float, bool, date, datetime, Decimal? Use standard.\n- Duplicate handling: key tuple from record. If existing: strategy. If duplicate within file: need track seen keys in current import to avoid duplicates in same file. Could treat as duplicate with strategy. For update, if same key appears later, update existing? Need careful.\n- Error reporting: `RowError(line_number, row_number, raw_row, field, message, code)`. `ImportErrorReport` with add, to_csv, to_json, summary.\n- Logging.\n- CLI maybe optional. Could include `main` for import file to JSON? But maybe too much. Production utility could include CLI. But answer length large. We can include module and tests.\n\nNeed ensure code runs. Let's draft code.\n\nPotential code:\n\nfrom __future__ import annotations\nimport csv, json, logging, re, datetime, decimal, io, os, tempfile, typing as t\nfrom dataclasses import dataclass, field\nfrom enum import Enum\nfrom pathlib import Path\nfrom typing import Any, Callable, Iterable, Mapping, Protocol, Sequence\n\nclass DuplicateStrategy(str, Enum):\n FAIL = \"fail\"\n SKIP = \"skip\"\n UPDATE = \"update\"\n REPLACE = \"replace\" # maybe same as update? Could define replace deletes then inserts? Repository may not support delete. Use update.\n\nMaybe strategies: FAIL, SKIP, UPDATE.\n\nFieldSpec:\n@dataclass(frozen=True)\nclass FieldSpec:\n name: str\n required: bool = False\n type: str = \"str\" # str,int,float,bool,date,datetime,decimal\n regex: str | None = None\n choices: tuple[str,...] | None = None\n min_value: float | None = None\n max_value: float | None = None\n default: Any = None\n transform: Callable[[str], str] | None = None\n validator: Callable[[Any], None] | None = None\n\nBut frozen with callable okay.\n\nSchema:\n@dataclass(frozen=True)\nclass Schema:\n fields: tuple[FieldSpec,...]\n key_fields: tuple[str,...] = ()\n allow_extra_fields: bool = False\n strict_row_length: bool = True? DictReader doesn't give length. Could use csv.reader for row length. But DictReader easier. For extra fields, DictReader puts extras under None key. Need detect.\n\nMaybe use csv.reader to get raw rows and header. This allows row length validation and raw row for error report. But DictReader simpler. For production, use csv.reader and map manually.\n\nImplementation:\n- open file with newline='', encoding.\n- reader = csv.reader(f, delimiter, quotechar, skipinitialspace?)\n- header = next(reader, None). If None: error empty file.\n- normalize header? strip? maybe option normalize_headers=True.\n- map header names to indices. Check duplicates in header? error.\n- Check required schema fields present in header. If not, raise SchemaError? Or return error report? For production, fail fast with ImportSchemaError.\n- If not allow_extra_fields, check unknown header fields? Could collect error and fail.\n- For each row enumerate starting line 2. Need handle blank lines? csv.reader returns [] for blank? Actually blank line returns []? In csv.reader, blank line returns []? It may return [] for empty line. We can skip blank rows? Option skip_blank_rows=True. If row == [] skip.\n- If strict_row_length and len(row) != len(header): error. But if row shorter, missing fields; if longer, extra. Could still validate available? For error reporting, add row error and continue.\n- Build raw dict: for i, name in enumerate(header): value = row[i] if i < len(row) else None. Extra values if len(row)>len(header) maybe under None? We can add error.\n- Validate fields: for each field spec:\n raw = raw.get(name)\n if raw is None or raw == ''? Treat empty as missing. If required: error. Else if empty: value = default (if default not None) else None.\n else transform strip? Option strip_values=True.\n convert type.\n validate regex, choices, min/max, custom validator.\n- If errors: add RowError for each field? Could aggregate.\n- If no errors: record = {field.name: value for field in fields} plus maybe extra fields if allowed? Could include extras? Maybe only schema fields.\n- Duplicate handling:\n key = tuple(record.get(k) for k in schema.key_fields) if key_fields else None.\n If key is not None:\n if key in seen_keys: duplicate within file.\n existing = repository.get_by_key(key) if repository else None.\n if existing is not None or key in seen_keys:\n if strategy FAIL: add error duplicate; continue\n if SKIP: skipped +=1; continue\n if UPDATE: if repository: repository.update(record); updated +=1; seen_keys.add(key); continue\n else: repository.insert(record); inserted +=1; seen_keys.add(key)\n else: repository.insert(record); inserted +=1.\n- Need repository protocol:\nclass Repository(Protocol):\n def get_by_key(self, key: tuple[Any,...]) -> Mapping[str, Any] | None: ...\n def insert(self, record: Mapping[str, Any]) -> None: ...\n def update(self, record: Mapping[str, Any]) -> None: ...\nMaybe update should accept key? If record contains key fields, repository can update by key.\n\nInMemoryRepository:\n def __init__(self, key_fields): self._store = {}; self.key_fields = key_fields\n def _key(self, record): return tuple(record.get(k) for k in self.key_fields)\n get_by_key, insert (raise DuplicateKeyError if exists), update (raise NotFoundError if not exists), all.\n\nError report:\n@dataclass\nclass RowError:\n line_number: int\n row_number: int\n field: str | None\n code: str\n message: str\n raw_row: list[str] | None = None\n\n@dataclass\nclass ImportErrorReport:\n errors: list[RowError] = field(default_factory=list)\n max_errors: int | None = None\n def add(...): if max_errors and len >= max_errors: maybe raise MaxErrorsExceeded? Or stop? Could set truncated.\n def to_json(self): ...\n def to_csv(self): ...\n def summary(self): ...\n\nImportResult:\n total_rows, inserted, updated, skipped, errors, report, started_at, finished_at, duration.\n\nExceptions:\nclass CsvImportError(Exception)\nclass SchemaError(CsvImportError)\nclass MaxErrorsExceeded(CsvImportError)\nclass DuplicateKeyError(CsvImportError)\nclass RecordNotFoundError(CsvImportError)\n\nType conversion:\ndef _convert(value, type_name", "sha256": "e497d36bd6c793c00535cba43b9134626703541d2296105df6ff0349688d8448"} +{"name": "warmup-chinese", "passed": true, "finish": "length", "usage": {"prompt_tokens": 79, "total_tokens": 335, "completion_tokens": 256, "completion_tokens_details": {"reasoning_tokens": 256}}, "ttft_s": 0.06872151900006429, "elapsed_s": 3.373453899999731, "decode_tps": 77.16207262836322, "max_stream_gap_s": 0.01341272899935575, "events": 256, "content": "", "reasoning": "我们需要回答用户中文请求：“请写一份企业采购系统的完整需求说明，覆盖审批、预算、供应商、库存和审计，并逐项说明异常处理。” 需要生成完整需求说明，覆盖审批、预算、供应商、库存、审计，并逐项说明异常处理。需要可能结构化：文档目的、范围、角色、总体流程、功能需求（模块）、非功能需求、数据需求、接口、权限、报表、异常处理逐项。用户要求“逐项说明异常处理”，可能每个需求项都要有异常处理。需要详尽。无需外部信息。最终中文。确保覆盖审批、预算、供应商、库存、审计。可以按需求编号，每项包含功能说明、输入输出、规则、异常处理。可能太长但可接受。需要“完整需求说明”。可以包含采购申请、采购订单、收货、入库、退货、付款、预算控制、供应商准入/评估、库存管理、审计日志。异常处理要逐项：审批异常（超时、驳回、撤回、代理、权限、并发、附件缺失、预算不足、供应商冻结、价格异常、重复申请、接口失败、数据不一致、库存不足、收货差异、退货、盘点差异、审计篡改、日志失败等）。需要逐项说明异常处理", "sha256": "a22b2d597ee38bd41d46da37977764bcdb19294f5d4139f00acf9e592738ab8f"} +{"name": "perf-chinese-0", "passed": true, "finish": "length", "usage": {"prompt_tokens": 79, "total_tokens": 2127, "completion_tokens": 2048, "completion_tokens_details": {"reasoning_tokens": 2048}}, "ttft_s": 0.06851714799995534, "elapsed_s": 26.612284395999268, "decode_tps": 77.11791551194712, "max_stream_gap_s": 0.013450489999740967, "events": 2048, "content": "", "reasoning": "我们需要回答用户中文请求：“请写一份企业采购系统的完整需求说明，覆盖审批、预算、供应商、库存和审计，并逐项说明异常处理。” 需要生成完整需求说明，覆盖审批、预算、供应商、库存、审计，并逐项说明异常处理。需要可能结构化：文档目的、范围、角色、总体流程、功能需求（模块）、非功能需求、数据需求、接口、权限、报表、异常处理逐项。用户要求“逐项说明异常处理”，可能每个需求项都要有异常处理。需要详尽。无需外部信息。最终中文。确保覆盖审批、预算、供应商、库存、审计。可以按需求编号，每项包含功能说明、输入输出、规则、异常处理。可能太长但可接受。需要“完整需求说明”。可以包含采购申请、采购订单、收货、入库、退货、付款、预算控制、供应商准入/评估、库存管理、审计日志。异常处理要逐项：审批异常（超时、驳回、撤回、代理、权限、并发、附件缺失、预算不足、供应商冻结、价格异常、重复申请、接口失败、数据不一致、库存不足、收货差异、退货、盘点差异、审计篡改、日志失败等）。需要逐项说明异常处理，可能每个模块下需求项都有异常处理。为了可读性，使用表格或编号。最终答案要足够详细。\n\n需要思考结构：\n1 文档概述：目的、范围、目标、术语。\n2 角色权限：申请人、部门主管、财务预算员、采购员、采购经理、仓库管理员、质检员、供应商管理员、审计员、系统管理员。\n3 总体业务流程：需求申请->预算校验->审批->寻源/比价->订单->收货->入库->对账付款->审计。\n4 功能需求：\n4.1 基础数据：物料、单位、税率、币种、汇率、组织、成本中心、仓库、库位。\n4.2 采购申请：创建、编辑、提交、撤回、复制、附件、预算占用、审批路由。异常处理。\n4.3 审批：多级审批、金额/类别路由、会签、加签、转办、委托、超时、驳回、撤回、审批意见、电子签名。异常处理。\n4.4 预算：预算编制、预算占用、释放、追加、调整、冻结、超预算控制、预算预警、预算查询。异常处理。\n4.5 供应商：准入、资质、黑名单、冻结、分级、比价、合同、价格协议、评价、淘汰。异常处理。\n4.6 采购订单：生成、变更、取消、关闭、收货、发票、三单匹配。异常处理。\n4.7 库存：入库、出库、调拨、盘点、退货、安全库存、批次/序列号、效期、库存占用、库存预警。异常处理。\n4.8 审计：操作日志、审批日志、数据变更、权限日志、接口日志、报表、追溯、防篡改、归档。异常处理。\n4.9 报表与通知。\n5 非功能需求：性能、可用性、安全、合规、集成、扩展。\n6 数据模型/关键字段。\n7 接口：ERP、财务、OA、WMS、税务、银企、主数据。\n8 验收标准。\n\n“逐项说明异常处理”：可以在每个功能需求项后加“异常处理”。或者单独“异常处理清单”按需求编号。为了逐项，最好每个需求编号都有异常处理。可能用表格：编号、需求、规则、异常处理。但完整需求说明可能太长。可以用分节，每节列需求项，每项包含“异常处理”。\n\n需要确保覆盖审批、预算、供应商、库存、审计。可以编号：\nREQ-APP-001 采购申请创建 ... 异常处理：...\nREQ-APP-002 ...\n...\n这样逐项。\n\n需要可能包含异常处理通用原则：幂等、事务、补偿、人工介入、通知、日志。然后逐项。\n\n需要避免过于简略。最终中文。\n\n让我们设计详细需求：\n文档版本、修订记录可省略。\n\n1 概述\n1.1 目的\n1.2 范围\n1.3 目标\n1.4 术语\n\n2 角色与权限\n表格：角色、职责、权限。\n\n3 总体流程\n文字流程。\n\n4 功能需求\n4.1 基础数据与主数据\nREQ-BASE-001 物料主数据：字段、状态、异常处理。\nREQ-BASE-002 组织/成本中心/仓库：异常处理。\nREQ-BASE-003 价格目录/合同价：异常处理。\n\n4.2 采购申请\nREQ-PR-001 创建申请：必填、物料、数量、预算科目、期望日期、附件。异常：字段缺失、物料停用、重复、预算科目无效、附件超限、并发编辑。\nREQ-PR-002 预算预占：提交时占用。异常：预算不足、预算冻结、占用失败、释放失败、并发超占。\nREQ-PR-003 提交审批：路由。异常：审批人缺失、部门变更、金额阈值、路由失败、重复提交。\nREQ-PR-004 撤回/作废：未审批可撤回，已审批需作废。异常：已收货、已付款、状态冲突。\nREQ-PR-005 变更申请：数量/金额/供应商变更。异常：预算差异、审批重走、已生成订单。\n\n4.3 审批\nREQ-APR-001 审批路由：按金额、类别、部门、预算。异常：无匹配、多匹配、审批人离职、权限不足。\nREQ-APR-002 审批操作：同意、驳回、转办、加签、会签、退回。异常：超时、重复审批、并发审批、意见必填、附件缺失。\nREQ-APR-003 委托/代理：异常：循环委托、过期、权限不足。\nREQ-APR-004 审批通知：异常：通知失败、邮箱无效、重试。\nREQ-APR-005 审批记录：不可篡改。异常：日志写入失败、时间异常。\n\n4.4 预算\nREQ-BUD-001 预算编制/导入：异常：重复、格式、科目无效、版本冲突。\nREQ-BUD-002 预算占用/释放：异常：余额不足、占用记录不存在、重复释放、跨期、汇率变化。\nREQ-BUD-003 预算调整/追加：异常：审批未通过、权限、超上限、生效日期。\nREQ-BUD-004 预算预警/冻结：异常：阈值配置错误、冻结/解冻冲突。\nREQ-BUD-005 预算查询/报表：异常：数据延迟、权限、口径不一致。\n\n4.5 供应商\nREQ-SUP-001 供应商准入：资质、银行、税务、合规。异常：资料缺失、证照过期、重复、黑名单、审批超时。\nREQ-SUP-002 供应商状态：正常/冻结/黑名单/淘汰。异常：状态变更冲突、已下单、在途订单。\nREQ-SUP-003 供应商评估：质量、交期、价格、服务。异常：数据缺失、评分规则变更、申诉。\nREQ-SUP-004 价格协议/合同：异常：价格失效、重复协议、币种税率不一致、合同未生效。\nREQ-SUP-005 供应商门户/对账：异常：登录失败、数据不一致、争议。\n\n4.6 采购订单与收货\nREQ-PO-001 订单生成：从申请/合同。异常：供应商冻结、价格变化、预算释放/重占、审批状态。\nREQ-PO-002 订单变更/取消：异常：已收货、已开票、已付款、变更审批。\nREQ-PO-003 收货：数量、批次、质检。异常：超收、少收、错料、质检不合格、重复收货、接口失败。\nREQ-PO-004 入库：异常：库存不足? 入库增加库存，库位无效、批次重复、库存锁定、WMS接口失败。\nREQ-PO-005 退货：异常：退货数量超入库、库存不足、已付款、退货审批。\nREQ-PO-006 发票/三单匹配：异常：金额差异、税率、重复发票、供应商不一致、汇率。\n\n4.7 库存\nREQ-INV-001 库存台账：实时、占用、可用。异常：并发扣减、负库存、数据不一致。\nREQ-INV-002 安全库存/补货：异常：参数错误、重复生成、库存不足。\nREQ-INV-003 调拨：异常：调出不足、在途、跨组织、审批。\nREQ-INV-004 盘点：异常：盘盈盘亏、重复盘点、差异审批、库存锁定。\nREQ-INV-005 批次/序列号/效期：异常：过期、批次混用、序列号重复。\nREQ-INV-006 库存预警：异常：阈值、通知失败。\n\n4.8 审计\nREQ-AUD-001 操作日志：谁何时何操作。异常：日志失败、敏感信息、存储满。\n", "sha256": "2652020a1b7c30d6c14a7fab85da123692626a78089213929b6f5d48f0a49347"} +{"name": "perf-chinese-1", "passed": true, "finish": "length", "usage": {"prompt_tokens": 79, "total_tokens": 2127, "completion_tokens": 2048, "completion_tokens_details": {"reasoning_tokens": 2048}}, "ttft_s": 0.0701422599995567, "elapsed_s": 26.61247469099999, "decode_tps": 77.12208432779563, "max_stream_gap_s": 0.01398543499999505, "events": 2048, "content": "", "reasoning": "我们需要回答用户中文请求：“请写一份企业采购系统的完整需求说明，覆盖审批、预算、供应商、库存和审计，并逐项说明异常处理。” 需要生成完整需求说明，覆盖审批、预算、供应商、库存、审计，并逐项说明异常处理。需要可能结构化：文档目的、范围、角色、总体流程、功能需求（模块）、非功能需求、数据需求、接口、权限、报表、异常处理逐项。用户要求“逐项说明异常处理”，可能每个需求项都要有异常处理。需要详尽。无需外部信息。最终中文。确保覆盖审批、预算、供应商、库存、审计。可以按需求编号，每项包含功能说明、输入输出、规则、异常处理。可能太长但可接受。需要“完整需求说明”。可以包含采购申请、采购订单、收货、入库、退货、付款、预算控制、供应商准入/评估、库存管理、审计日志。异常处理要逐项：审批异常（超时、驳回、撤回、代理、权限、并发、附件缺失、预算不足、供应商冻结、价格异常、重复申请、接口失败、数据不一致、库存不足、收货差异、退货、盘点差异、审计篡改、日志失败等）。需要逐项说明异常处理，可能每个模块下需求项都有异常处理。为了可读性，使用表格或编号。最终答案要足够详细。\n\n需要思考结构：\n1 文档概述：目的、范围、目标、术语。\n2 角色权限：申请人、部门主管、财务预算员、采购员、采购经理、仓库管理员、质检员、供应商管理员、审计员、系统管理员。\n3 总体业务流程：需求申请->预算校验->审批->寻源/比价->订单->收货->入库->对账付款->审计。\n4 功能需求：\n4.1 基础数据：物料、单位、税率、币种、汇率、组织、成本中心、仓库、库位。\n4.2 采购申请：创建、编辑、提交、撤回、复制、附件、预算占用、审批路由。异常处理。\n4.3 审批：多级审批、金额/类别路由、会签、加签、转办、委托、超时、驳回、撤回、审批意见、电子签名。异常处理。\n4.4 预算：预算编制、预算占用、释放、追加、调整、冻结、超预算控制、预算预警、预算查询。异常处理。\n4.5 供应商：准入、资质、黑名单、冻结、分级、比价、合同、价格协议、评价、淘汰。异常处理。\n4.6 采购订单：生成、变更、取消、关闭、收货、发票、三单匹配。异常处理。\n4.7 库存：入库、出库、调拨、盘点、退货、安全库存、批次/序列号、效期、库存占用、库存预警。异常处理。\n4.8 审计：操作日志、审批日志、数据变更、权限日志、接口日志、报表、追溯、防篡改、归档。异常处理。\n4.9 报表与通知。\n5 非功能需求：性能、可用性、安全、合规、集成、扩展。\n6 数据模型/关键字段。\n7 接口：ERP、财务、OA、WMS、税务、银企、主数据。\n8 验收标准。\n\n“逐项说明异常处理”：可以在每个功能需求项后加“异常处理”。或者单独“异常处理清单”按需求编号。为了逐项，最好每个需求编号都有异常处理。可能用表格：编号、需求、规则、异常处理。但完整需求说明可能太长。可以用分节，每节列需求项，每项包含“异常处理”。\n\n需要确保覆盖审批、预算、供应商、库存、审计。可以编号：\nREQ-APP-001 采购申请创建 ... 异常处理：...\nREQ-APP-002 ...\n...\n这样逐项。\n\n需要可能包含异常处理通用原则：幂等、事务、补偿、人工介入、通知、日志。然后逐项。\n\n需要避免过于简略。最终中文。\n\n让我们设计详细需求：\n文档版本、修订记录可省略。\n\n1 概述\n1.1 目的\n1.2 范围\n1.3 目标\n1.4 术语\n\n2 角色与权限\n表格：角色、职责、权限。\n\n3 总体流程\n文字流程。\n\n4 功能需求\n4.1 基础数据与主数据\nREQ-BASE-001 物料主数据：字段、状态、异常处理。\nREQ-BASE-002 组织/成本中心/仓库：异常处理。\nREQ-BASE-003 价格目录/合同价：异常处理。\n\n4.2 采购申请\nREQ-PR-001 创建申请：必填、物料、数量、预算科目、期望日期、附件。异常：字段缺失、物料停用、重复、预算科目无效、附件超限、并发编辑。\nREQ-PR-002 预算预占：提交时占用。异常：预算不足、预算冻结、占用失败、释放失败、并发超占。\nREQ-PR-003 提交审批：路由。异常：审批人缺失、部门变更、金额阈值、路由失败、重复提交。\nREQ-PR-004 撤回/作废：未审批可撤回，已审批需作废。异常：已收货、已付款、状态冲突。\nREQ-PR-005 变更申请：数量/金额/供应商变更。异常：预算差异、审批重走、已生成订单。\n\n4.3 审批\nREQ-APR-001 审批路由：按金额、类别、部门、预算。异常：无匹配、多匹配、审批人离职、权限不足。\nREQ-APR-002 审批操作：同意、驳回、转办、加签、会签、退回。异常：超时、重复审批、并发审批、意见必填、附件缺失。\nREQ-APR-003 委托/代理：异常：循环委托、过期、权限不足。\nREQ-APR-004 审批通知：异常：通知失败、邮箱无效、重试。\nREQ-APR-005 审批记录：不可篡改。异常：日志写入失败、时间异常。\n\n4.4 预算\nREQ-BUD-001 预算编制/导入：异常：重复、格式、科目无效、版本冲突。\nREQ-BUD-002 预算占用/释放：异常：余额不足、占用记录不存在、重复释放、跨期、汇率变化。\nREQ-BUD-003 预算调整/追加：异常：审批未通过、权限、超上限、生效日期。\nREQ-BUD-004 预算预警/冻结：异常：阈值配置错误、冻结/解冻冲突。\nREQ-BUD-005 预算查询/报表：异常：数据延迟、权限、口径不一致。\n\n4.5 供应商\nREQ-SUP-001 供应商准入：资质、银行、税务、合规。异常：资料缺失、证照过期、重复、黑名单、审批超时。\nREQ-SUP-002 供应商状态：正常/冻结/黑名单/淘汰。异常：状态变更冲突、已下单、在途订单。\nREQ-SUP-003 供应商评估：质量、交期、价格、服务。异常：数据缺失、评分规则变更、申诉。\nREQ-SUP-004 价格协议/合同：异常：价格失效、重复协议、币种税率不一致、合同未生效。\nREQ-SUP-005 供应商门户/对账：异常：登录失败、数据不一致、争议。\n\n4.6 采购订单与收货\nREQ-PO-001 订单生成：从申请/合同。异常：供应商冻结、价格变化、预算释放/重占、审批状态。\nREQ-PO-002 订单变更/取消：异常：已收货、已开票、已付款、变更审批。\nREQ-PO-003 收货：数量、批次、质检。异常：超收、少收、错料、质检不合格、重复收货、接口失败。\nREQ-PO-004 入库：异常：库存不足? 入库增加库存，库位无效、批次重复、库存锁定、WMS接口失败。\nREQ-PO-005 退货：异常：退货数量超入库、库存不足、已付款、退货审批。\nREQ-PO-006 发票/三单匹配：异常：金额差异、税率、重复发票、供应商不一致、汇率。\n\n4.7 库存\nREQ-INV-001 库存台账：实时、占用、可用。异常：并发扣减、负库存、数据不一致。\nREQ-INV-002 安全库存/补货：异常：参数错误、重复生成、库存不足。\nREQ-INV-003 调拨：异常：调出不足、在途、跨组织、审批。\nREQ-INV-004 盘点：异常：盘盈盘亏、重复盘点、差异审批、库存锁定。\nREQ-INV-005 批次/序列号/效期：异常：批次不存在、过期、序列号重复。\nREQ-INV-006 库存预警：异常：阈值、通知失败。\n\n4.8 审计\nREQ-AUD-001 操作日志：谁、何时、何事、前后值。异常：日志失败、敏感信息、", "sha256": "6c3b975da1b2d7b1f35e52d1f1d1a9c0b3bc7deab5e703c9cc22d30ec27357f7"} +{"name": "perf-chinese-2", "passed": true, "finish": "length", "usage": {"prompt_tokens": 79, "total_tokens": 2127, "completion_tokens": 2048, "completion_tokens_details": {"reasoning_tokens": 2048}}, "ttft_s": 0.07043461099965498, "elapsed_s": 26.613534969000284, "decode_tps": 77.11985308389164, "max_stream_gap_s": 0.01346164500046143, "events": 2048, "content": "", "reasoning": "我们需要回答用户中文请求：“请写一份企业采购系统的完整需求说明，覆盖审批、预算、供应商、库存和审计，并逐项说明异常处理。” 需要生成完整需求说明，覆盖审批、预算、供应商、库存、审计，并逐项说明异常处理。需要可能结构化：文档目的、范围、角色、总体流程、功能需求（模块）、非功能需求、数据需求、接口、权限、报表、异常处理逐项。用户要求“逐项说明异常处理”，可能每个需求项都要有异常处理。需要详尽。无需外部信息。最终中文。确保覆盖审批、预算、供应商、库存、审计。可以按需求编号，每项包含功能说明、输入输出、规则、异常处理。可能太长但可接受。需要“完整需求说明”。可以包含采购申请、采购订单、收货、入库、退货、付款、预算控制、供应商准入/评估、库存管理、审计日志。异常处理要逐项：审批异常（超时、驳回、撤回、代理、权限、并发、附件缺失、预算不足、供应商冻结、价格异常、重复申请、接口失败、数据不一致、库存不足、收货差异、退货、盘点差异、审计篡改、日志失败等）。需要逐项说明异常处理，可能每个模块下需求项都有异常处理。为了可读性，使用表格或编号。最终答案要足够详细。\n\n需要思考结构：\n1 文档概述：目的、范围、目标、术语。\n2 角色权限：申请人、部门主管、财务预算员、采购员、采购经理、仓库管理员、质检员、供应商管理员、审计员、系统管理员。\n3 总体业务流程：需求申请->预算校验->审批->寻源/比价->订单->收货->入库->对账付款->审计。\n4 功能需求：\n4.1 基础数据：物料、单位、税率、币种、汇率、组织、成本中心、仓库、库位。\n4.2 采购申请：创建、编辑、提交、撤回、复制、附件、预算占用、审批路由。异常处理。\n4.3 审批：多级审批、金额/类别路由、会签、加签、转办、委托、超时、驳回、撤回、审批意见、电子签名。异常处理。\n4.4 预算：预算编制、预算占用、释放、追加、调整、冻结、超预算控制、预算预警、预算查询。异常处理。\n4.5 供应商：准入、资质、黑名单、冻结、分级、比价、合同、价格协议、评价、淘汰。异常处理。\n4.6 采购订单：生成、变更、取消、关闭、收货、发票、三单匹配。异常处理。\n4.7 库存：入库、出库、调拨、盘点、退货、安全库存、批次/序列号、效期、库存占用、库存预警。异常处理。\n4.8 审计：操作日志、审批日志、数据变更、权限日志、接口日志、报表、追溯、防篡改、归档。异常处理。\n4.9 报表与通知。\n5 非功能需求：性能、可用性、安全、合规、集成、扩展。\n6 数据模型/关键字段。\n7 接口：ERP、财务、OA、WMS、税务、银企、主数据。\n8 验收标准。\n\n“逐项说明异常处理”：可以在每个功能需求项后加“异常处理”。或者单独“异常处理清单”按需求编号。为了逐项，最好每个需求编号都有异常处理。可能用表格：编号、需求、规则、异常处理。但完整需求说明可能太长。可以用分节，每节列需求项，每项包含“异常处理”。\n\n需要确保覆盖审批、预算、供应商、库存、审计。可以编号：\nREQ-APP-001 采购申请创建 ... 异常处理：...\nREQ-APP-002 ...\n...\n这样逐项。\n\n需要可能包含异常处理通用原则：幂等、事务、补偿、人工介入、通知、日志。然后逐项。\n\n需要避免过于简略。最终中文。\n\n让我们设计详细需求：\n文档版本、修订记录可省略。\n\n1 概述\n1.1 目的\n1.2 范围\n1.3 目标\n1.4 术语\n\n2 角色与权限\n表格：角色、职责、权限。\n\n3 总体流程\n文字流程。\n\n4 功能需求\n4.1 基础数据与主数据\nREQ-BASE-001 物料主数据：字段、状态、异常处理。\nREQ-BASE-002 组织/成本中心/仓库：异常处理。\nREQ-BASE-003 价格目录/合同价：异常处理。\n\n4.2 采购申请\nREQ-PR-001 创建申请：必填、物料、数量、预算科目、期望日期、附件。异常：字段缺失、物料停用、重复、预算科目无效、附件超限、并发编辑。\nREQ-PR-002 预算预占：提交时占用。异常：预算不足、预算冻结、占用失败、释放失败、并发超占。\nREQ-PR-003 提交审批：路由。异常：审批人缺失、部门变更、金额阈值、路由失败、重复提交。\nREQ-PR-004 撤回/作废：未审批可撤回，已审批需作废。异常：已收货、已付款、状态冲突。\nREQ-PR-005 变更申请：数量/金额/供应商变更。异常：预算差异、审批重走、已生成订单。\n\n4.3 审批\nREQ-APR-001 审批路由：按金额、类别、部门、预算。异常：无匹配、多匹配、审批人离职、权限不足。\nREQ-APR-002 审批操作：同意、驳回、转办、加签、会签、退回。异常：超时、重复审批、并发审批、意见必填、附件缺失。\nREQ-APR-003 委托/代理：异常：循环委托、过期、权限不足。\nREQ-APR-004 审批通知：异常：通知失败、邮箱无效、重试。\nREQ-APR-005 审批记录：不可篡改。异常：日志写入失败、时间异常。\n\n4.4 预算\nREQ-BUD-001 预算编制/导入：异常：重复、格式、科目无效、版本冲突。\nREQ-BUD-002 预算占用/释放：异常：余额不足、占用记录不存在、重复释放、跨期、汇率变化。\nREQ-BUD-003 预算调整/追加：异常：审批未通过、权限、超上限、生效日期。\nREQ-BUD-004 预算预警/冻结：异常：阈值配置错误、冻结/解冻冲突。\nREQ-BUD-005 预算查询/报表：异常：数据延迟、权限、口径不一致。\n\n4.5 供应商\nREQ-SUP-001 供应商准入：资质、银行、税务、合规。异常：资料缺失、证照过期、重复、黑名单、审批超时。\nREQ-SUP-002 供应商状态：正常/冻结/黑名单/淘汰。异常：状态变更冲突、已下单、在途订单。\nREQ-SUP-003 供应商评估：质量、交期、价格、服务。异常：数据缺失、评分规则变更、申诉。\nREQ-SUP-004 价格协议/合同：异常：价格失效、重复协议、币种税率不一致、合同未生效。\nREQ-SUP-005 供应商门户/对账：异常：登录失败、数据不一致、争议。\n\n4.6 采购订单与收货\nREQ-PO-001 订单生成：从申请/合同。异常：供应商冻结、价格变化、预算释放/重占、审批状态。\nREQ-PO-002 订单变更/取消：异常：已收货、已开票、已付款、变更审批。\nREQ-PO-003 收货：数量、批次、质检。异常：超收、少收、错料、质检不合格、重复收货、接口失败。\nREQ-PO-004 入库：异常：库存不足? 入库增加库存，库位无效、批次重复、库存锁定、WMS接口失败。\nREQ-PO-005 退货：异常：退货数量超入库、库存不足、已付款、退货审批。\nREQ-PO-006 发票/三单匹配：异常：金额差异、税率、重复发票、供应商不一致、汇率。\n\n4.7 库存\nREQ-INV-001 库存台账：实时、占用、可用。异常：并发扣减、负库存、数据不一致。\nREQ-INV-002 安全库存/补货：异常：参数错误、重复生成、库存不足。\nREQ-INV-003 调拨：异常：调出不足、在途、跨组织、审批。\nREQ-INV-004 盘点：异常：盘盈盘亏、重复盘点、差异审批、库存锁定。\nREQ-INV-005 批次/序列号/效期：异常：批次不存在、过期、序列号重复。\nREQ-INV-006 库存预警：异常：阈值、通知失败、误报。\n\n4.8 审计\nREQ-AUD-001 操作日志：谁、何时、对象、前后值。异常：日志失败、敏感", "sha256": "2bf61a63d08e3a7cfc60a9e5b45b3dc7c684789670564d4992e97342fd540e99"} +{"name": "warmup-reasoning", "passed": true, "finish": "length", "usage": {"prompt_tokens": 79, "total_tokens": 335, "completion_tokens": 256, "completion_tokens_details": {"reasoning_tokens": 256}}, "ttft_s": 0.07024974799969641, "elapsed_s": 3.373716119999699, "decode_tps": 77.1916439535652, "max_stream_gap_s": 0.013408251999862841, "events": 256, "content": "", "reasoning": "We need answer user: \"Explain and prove why Dijkstra works with nonnegative weights. Include a counterexample with negative edges and compare Bellman-Ford.\" Need produce final with explanation, proof, counterexample, comparison. Need be careful. Need likely include algorithm, invariant, proof by induction, contradiction. Counterexample negative edge where Dijkstra fails. Compare Bellman-Ford: handles negative edges no negative cycles, O(VE), DP relaxation, detects negative cycles. Maybe mention Dijkstra with priority queue O((V+E)logV) or O(E+VlogV). Need maybe include pseudocode? Could include. Need ensure proof rigorous: nonnegative weights ensure once vertex extracted min tentative distance, it's final. Lemma: for any unsettled vertex v, dist[v] >= true shortest distance? Actually tentative distances are upper bounds. Need show extracted u has dist[u]=δ(s,u). Suppose not, there is shorter path P. Let y be first unsettled vertex on P, x predecessor settled. Since edge weights nonnegative, δ(s,y) <= δ(s,u) < dist[u]? Need formal. Let's craft.\n\nNeed maybe discuss Dijkstra variants: greedy selects unvisited vertex with smallest tentative distance. Nonnegative weights crucial because", "sha256": "51b23381901883a745d555beff0aa2851aa37aae459f841a99cd0b901fc1b72a"} +{"name": "perf-reasoning-0", "passed": true, "finish": "length", "usage": {"prompt_tokens": 79, "total_tokens": 2127, "completion_tokens": 2048, "completion_tokens_details": {"reasoning_tokens": 2037}}, "ttft_s": 0.06953137100026652, "elapsed_s": 26.60551587700047, "decode_tps": 77.14053343440644, "max_stream_gap_s": 0.02612606699949538, "events": 2037, "content": "", "reasoning": "We need answer user: \"Explain and prove why Dijkstra works with nonnegative weights. Include a counterexample with negative edges and compare Bellman-Ford.\" Need produce final with explanation, proof, counterexample, comparison. Need be careful. Need likely include algorithm, invariant, proof by induction, contradiction. Counterexample negative edge where Dijkstra fails. Compare Bellman-Ford: handles negative edges no negative cycles, O(VE), DP relaxation, detects negative cycles. Maybe mention Dijkstra with priority queue O((V+E)logV) or O(E+VlogV). Need maybe include pseudocode? Could include. Need ensure proof rigorous: nonnegative weights ensure once vertex extracted min tentative distance, it's final. Lemma: for any unsettled vertex v, dist[v] >= true shortest distance? Actually tentative distances are upper bounds. Need show extracted u has dist[u]=δ(s,u). Suppose not, there is shorter path P. Let y be first unsettled vertex on P, x predecessor settled. Since edge weights nonnegative, δ(s,y) <= δ(s,u) < dist[u]? Need formal. Let's craft.\n\nNeed maybe discuss Dijkstra variants: greedy selects unvisited vertex with smallest tentative distance. Nonnegative weights crucial because path length cannot decrease by adding edges. If negative edge, a settled vertex could later be improved via unsettled vertex with larger tentative distance because negative edge reduces total.\n\nCounterexample: graph s->a weight 1, s->b weight 2, b->a weight -2. Shortest s->a = 0 via s-b-a. Dijkstra: dist s=0, a=1, b=2. Extract a (1) finalize. Then extract b (2), relax b->a gives 0 but a already finalized, if algorithm doesn't update finalized, returns 1. If implementation allows reinsert? Standard Dijkstra with visited set fails. If no visited but priority queue with duplicates? It might update a after finalized? Standard Dijkstra assumes no negative edges; if you allow reprocessing, it may not terminate with negative cycles, but with this graph if you don't mark visited, it could find 0? Let's see: start PQ (s0). pop s, relax a1,b2. pop a1, relax none. pop b2, relax a0 push. pop a0. If no visited, returns 0. But standard Dijkstra marks visited and doesn't revisit. Need present standard. Or use counterexample where even with reinsert? Negative cycles cause infinite. But for negative edge no cycle, Dijkstra with visited fails. Need maybe mention if implementation doesn't finalize, it's not Dijkstra's correctness proof and may fail with negative cycles.\n\nAlternative counterexample: s->a=1, s->b=2, b->a=-2. Dijkstra finalizes a at 1, misses 0. Good.\n\nNeed compare Bellman-Ford: initializes dist[s]=0, relax all edges V-1 times. Works with negative weights because it doesn't finalize vertices; repeated relaxations propagate improvements along paths with up to k edges. Proof by induction: after i iterations, dist[v] <= length of shortest path from s to v using at most i edges. If no negative cycles, shortest simple path has at most V-1 edges, so after V-1 iterations optimal. Extra iteration detects negative cycle reachable from source if any edge can still relax. Complexity O(VE), slower than Dijkstra but handles negative edges. Dijkstra faster but requires nonnegative. Also mention Johnson's algorithm? Not necessary.\n\nNeed maybe include proof of nonnegative: Let's formulate.\n\nDefinitions: directed graph G=(V,E), weight w:E->R nonnegative. Source s. δ(v) shortest path distance. Dijkstra maintains S settled vertices, d[v] tentative. Initially d[s]=0, others ∞. At each step choose u∉S with minimum d[u], add to S, relax outgoing edges.\n\nTheorem: If all weights ≥0, when u added to S, d[u]=δ(u).\n\nProof:\nInvariant: For every v, d[v] ≥ δ(v)? Actually d[v] is length of some discovered path, so d[v] ≥ δ(v) (upper bound). For settled vertices, d=δ. Need prove.\n\nInduction on |S|. Base S={s}: d[s]=0=δ(s) if no negative cycles? With nonnegative, shortest from s to s is 0 (unless negative cycle reachable? nonnegative no negative cycles). For v≠s, d[v]≥δ(v).\n\nInductive step: assume all settled have correct distances. Let u be min tentative among unsettled. Need show d[u]=δ(u). Suppose δ(u)y relaxed, so d[y] ≤ d[x]+w(x,y)=δ(x)+w(x,y). Since P is shortest and weights nonnegative, δ(y) ≤ δ(u) (because suffix y->u has nonnegative length, so δ(u) ≤ δ(y)+nonnegative? Wait need δ(y) ≤ δ(u). If P is shortest s->u, prefix s->y is a shortest path to y? In nonnegative? Actually any prefix of a shortest path is shortest if no negative cycles? With nonnegative yes, if prefix not shortest, replace prefix to get shorter s->u. So δ(y)=δ(x)+w(x,y). Also δ(u)=δ(y)+length(y->u) ≥ δ(y) because suffix nonnegative. Thus d[y] ≤ δ(y) ≤ δ(u) < d[u]. But y unsettled, contradicting u chosen with minimum tentative distance. Therefore δ(u)≥d[u]. Since d[u] is length of some path, d[u]≥δ(u). So equality.\n\nNeed be careful: d[y] ≤ δ(y) ≤ δ(u) < d[u]. If δ(y) could be less than d[y]? d[y] is upper bound, so d[y]≥δ(y). We have d[y] ≤ δ(y) from relaxation? Actually d[y] ≤ d[x]+w = δ(x)+w = δ(y) if prefix shortest. So d[y]=δ(y). Good. Then contradiction.\n\nAlternative proof: Suppose u settled with d[u]>δ(u). Let P shortest path. Let y first unsettled, x previous settled. Since weights nonnegative, δ(y) ≤ δ(u) < d[u]. But d[y] ≤ δ(y) < d[u], contradiction. Need justify d[y] ≤ δ(y)? d[y] is upper bound, so d[y]≥δ(y), not ≤. But after x settled, d[y] ≤ d[x]+w(x,y). If prefix shortest, d[x]=δ(x), so d[y] ≤ δ(x)+w(x,y)=δ(y). Combined with d[y]≥δ(y) gives equality. Good.\n\nNeed maybe mention if zero weights okay: nonnegative includes zero; proof uses suffix length ≥0. If zero, δ(y) ≤ δ(u) still.\n\nNeed maybe discuss why negative breaks proof: suffix can have negative length, so δ(y) may be > δ(u); a vertex y with larger tentative distance can lead to shorter path to u via negative edge. In counterexample, y=b has d=2 > d[a]=1, but δ(a)=0 <1.\n\nNeed compare Bellman-Ford maybe table.\n\nNeed maybe include pseudocode for Dijkstra and Bellman-Ford.\n\nPotential nuance: Dijkstra with nonnegative weights but negative edges not reachable? If negative edges not affecting source? It still works if no negative edge on any path that can improve settled? But general condition: nonnegative weights sufficient; not necessary. Could mention.\n\nNeed maybe include proof of Bellman-Ford: after k iterations, d[v] ≤ shortest path using ≤k edges. Induction. If no negative cycles, shortest path simple ≤V-1 edges. If negative cycle reachable, distances undefined (-∞); BF detects by Vth iteration relaxation.\n\nNeed maybe include complexity and use cases.\n\nNeed final answer structured: 1. Setup and algorithm. 2. Why nonnegative matters/intuition. 3. Proof. 4. Counterexample. 5. Bellman-Ford comparison. Maybe include table.\n\nNeed ensure no hidden chain-of-thought in final. Just answer.\n\nLet's craft detailed proof.\n\nPotential issue: In proof, we assume shortest path exists. With nonnegative weights, if no negative cycles, shortest path exists (finite) for reachable vertices; if unreachable δ=∞. Dijkstra sets ∞. For reachable, finite. If graph has zero cycles, still shortest simple path exists. Good.\n\nLet's write:\n\nLet δ(v) be infimum length of s-v paths. With nonnegative weights, if reachable, there is a shortest path with no repeated vertices (remove cycles nonnegative doesn't increase length). So finite.\n\nDijkstra pseudocode:\nfor v: d[v]=∞; d[s]=0; S=∅; PQ with s.\nwhile PQ not empty:\n u=extract-min\n if u in S continue? Standard.\n S.add(u)\n for (u,v) in E: if d[v] > d[u]+w(u,v): d[v]=...\n\nProof:\nInvariant: (1) d[v] is length of some s-v path (or ∞), so d[v", "sha256": "1276bd3ceb6153dcd8ef9f0dd3b2d09c66ea6c1d84d5ae2bbc5e37a220ec5f86"} +{"name": "perf-reasoning-1", "passed": true, "finish": "length", "usage": {"prompt_tokens": 79, "total_tokens": 2127, "completion_tokens": 2048, "completion_tokens_details": {"reasoning_tokens": 2037}}, "ttft_s": 0.07017212099981407, "elapsed_s": 26.604105001999415, "decode_tps": 77.14649800240561, "max_stream_gap_s": 0.026043742999718233, "events": 2037, "content": "", "reasoning": "We need answer user: \"Explain and prove why Dijkstra works with nonnegative weights. Include a counterexample with negative edges and compare Bellman-Ford.\" Need produce final with explanation, proof, counterexample, comparison. Need be careful. Need likely include algorithm, invariant, proof by induction, contradiction. Counterexample negative edge where Dijkstra fails. Compare Bellman-Ford: handles negative edges no negative cycles, O(VE), DP relaxation, detects negative cycles. Maybe mention Dijkstra with priority queue O((V+E)logV) or O(E+VlogV). Need maybe include pseudocode? Could include. Need ensure proof rigorous: nonnegative weights ensure once vertex extracted min tentative distance, it's final. Lemma: for any unsettled vertex v, dist[v] >= true shortest distance? Actually tentative distances are upper bounds. Need show extracted u has dist[u]=δ(s,u). Suppose not, there is shorter path P. Let y be first unsettled vertex on P, x predecessor settled. Since edge weights nonnegative, δ(s,y) <= δ(s,u) < dist[u]? Need formal. Let's craft.\n\nNeed maybe discuss Dijkstra variants: greedy selects unvisited vertex with smallest tentative distance. Nonnegative weights crucial because path length cannot decrease by adding edges. If negative edge, a settled vertex could later be improved via unsettled vertex with larger tentative distance because negative edge reduces total.\n\nCounterexample: graph s->a weight 1, s->b weight 2, b->a weight -2. Shortest s->a = 0 via s-b-a. Dijkstra: dist s=0, a=1, b=2. Extract a (1) finalize. Then extract b (2), relax b->a gives 0 but a already finalized, if algorithm doesn't update finalized, returns 1. If implementation allows reinsert? Standard Dijkstra with visited set fails. If no visited but priority queue with duplicates? It might update a after finalized? Standard Dijkstra assumes no negative edges; if you allow reprocessing, it may not terminate with negative cycles, but with this graph if you don't mark visited, it could find 0? Let's see: start PQ (s0). pop s, relax a1,b2. pop a1, relax none. pop b2, relax a0 push. pop a0. If no visited, returns 0. But standard Dijkstra marks visited and doesn't revisit. Need present standard. Or use counterexample where even with reinsert? Negative cycles cause infinite. But for negative edge no cycle, Dijkstra with visited fails. Need maybe mention if implementation doesn't finalize, it's not Dijkstra's correctness proof and may fail with negative cycles.\n\nAlternative counterexample: s->a=1, s->b=2, b->a=-2. Dijkstra finalizes a at 1, misses 0. Good.\n\nNeed compare Bellman-Ford: initializes dist[s]=0, relax all edges V-1 times. Works with negative weights because it doesn't finalize vertices; repeated relaxations propagate improvements along paths with up to k edges. Proof by induction: after i iterations, dist[v] <= length of shortest path from s to v using at most i edges. If no negative cycles, shortest simple path has at most V-1 edges, so after V-1 iterations optimal. Extra iteration detects negative cycle reachable from source if any edge can still relax. Complexity O(VE), slower than Dijkstra but handles negative edges. Dijkstra faster but requires nonnegative. Also mention Johnson's algorithm? Not necessary.\n\nNeed maybe include proof of nonnegative: Let's formulate.\n\nDefinitions: directed graph G=(V,E), weight w:E->R nonnegative. Source s. δ(v) shortest path distance. Dijkstra maintains S settled vertices, d[v] tentative. Initially d[s]=0, others ∞. At each step choose u∉S with minimum d[u], add to S, relax outgoing edges.\n\nTheorem: If all weights ≥0, when u added to S, d[u]=δ(u).\n\nProof:\nInvariant: For every v, d[v] ≥ δ(v)? Actually d[v] is length of some discovered path, so d[v] ≥ δ(v) (upper bound). For settled vertices, d=δ. Need prove.\n\nInduction on |S|. Base S={s}: d[s]=0=δ(s) if no negative cycles? With nonnegative, shortest from s to s is 0 (unless negative cycle reachable? nonnegative no negative cycles). For v≠s, d[v]≥δ(v).\n\nInductive step: assume all settled have correct distances. Let u be min tentative among unsettled. Need show d[u]=δ(u). Suppose δ(u)y relaxed, so d[y] ≤ d[x]+w(x,y)=δ(x)+w(x,y). Since P is shortest and weights nonnegative, δ(y) ≤ δ(u) (because suffix y->u has nonnegative length, so δ(u) ≤ δ(y)+nonnegative? Wait need δ(y) ≤ δ(u). If P is shortest s->u, prefix s->y is a shortest path to y? In nonnegative? Actually any prefix of a shortest path is shortest if no negative cycles? With nonnegative yes, if prefix not shortest, replace prefix to get shorter s->u. So δ(y)=δ(x)+w(x,y). Also δ(u)=δ(y)+length(y->u) ≥ δ(y) because suffix nonnegative. Thus d[y] ≤ δ(y) ≤ δ(u) < d[u]. But y unsettled, contradicting u chosen with minimum tentative distance. Therefore δ(u)≥d[u]. Since d[u] is length of some path, d[u]≥δ(u). So equality.\n\nNeed be careful: d[y] ≤ δ(y) ≤ δ(u) < d[u]. If δ(y) could be less than d[y]? d[y] is upper bound, so d[y]≥δ(y). We have d[y] ≤ δ(y) from relaxation? Actually d[y] ≤ d[x]+w = δ(x)+w = δ(y) if prefix shortest. So d[y]=δ(y). Good. Then contradiction.\n\nAlternative proof: Suppose u settled with d[u]>δ(u). Let P shortest path. Let y first unsettled, x previous settled. Since weights nonnegative, δ(y) ≤ δ(u) < d[u]. But d[y] ≤ δ(y) < d[u], contradiction. Need justify d[y] ≤ δ(y)? d[y] is upper bound, so d[y]≥δ(y), not ≤. But after x settled, d[y] ≤ d[x]+w(x,y). If prefix shortest, d[x]=δ(x), so d[y] ≤ δ(x)+w(x,y)=δ(y). Combined with d[y]≥δ(y) gives equality. Good.\n\nNeed maybe mention if zero weights okay: nonnegative includes zero; proof uses suffix length ≥0. If zero, δ(y) ≤ δ(u) still.\n\nNeed maybe discuss why negative breaks proof: suffix can have negative length, so δ(y) may be > δ(u); a vertex y with larger tentative distance can lead to shorter path to u via negative edge. In counterexample, y=b has d=2 > d[a]=1, but δ(a)=0 <1.\n\nNeed compare Bellman-Ford maybe table.\n\nNeed maybe include pseudocode for Dijkstra and Bellman-Ford.\n\nPotential nuance: Dijkstra with nonnegative weights but negative edges not reachable? If negative edges not affecting source? It still works if no negative edge on any path that can improve settled? But general condition: nonnegative weights sufficient; not necessary. Could mention.\n\nNeed maybe include proof of Bellman-Ford: after k iterations, d[v] ≤ shortest path using ≤k edges. Induction. If no negative cycles, shortest path simple ≤V-1 edges. If negative cycle reachable, distances undefined (-∞); BF detects by Vth iteration relaxation.\n\nNeed maybe include complexity and use cases.\n\nNeed final answer structured: 1. Setup and algorithm. 2. Why nonnegative matters/intuition. 3. Proof. 4. Counterexample. 5. Bellman-Ford comparison. Maybe include table.\n\nNeed ensure no hidden chain-of-thought in final. Just answer.\n\nLet's craft detailed proof.\n\nPotential issue: In proof, we assume shortest path exists. With nonnegative weights, if no negative cycles, shortest path exists (finite) for reachable vertices; if unreachable δ=∞. Dijkstra sets ∞. For reachable, finite. If graph has zero cycles, still shortest simple path exists. Good.\n\nLet's write:\n\nLet δ(v) be infimum length of s-v paths. With nonnegative weights, if reachable, there is a shortest path with no repeated vertices (remove cycles nonnegative doesn't increase length). So finite.\n\nDijkstra pseudocode:\nfor v: d[v]=∞; d[s]=0; S=∅; PQ with s.\nwhile PQ not empty:\n u=extract-min\n if u in S continue? Standard.\n S.add(u)\n for (u,v) in E: if d[v] > d[u]+w(u,v): d[v]=...\n\nProof:\nInvariant: (1) d[v] is length of some s-v path (or ∞), hence d[v", "sha256": "61474ffd5fa513eae4c1dbcbf768e4cdc2efbcb6e3a609e7e1f1a28366c43507"} +{"name": "perf-reasoning-2", "passed": true, "finish": "length", "usage": {"prompt_tokens": 79, "total_tokens": 2127, "completion_tokens": 2048, "completion_tokens_details": {"reasoning_tokens": 2037}}, "ttft_s": 0.06906132600033743, "elapsed_s": 26.604923668000083, "decode_tps": 77.14088856875408, "max_stream_gap_s": 0.026071571999636944, "events": 2037, "content": "", "reasoning": "We need answer user: \"Explain and prove why Dijkstra works with nonnegative weights. Include a counterexample with negative edges and compare Bellman-Ford.\" Need produce final with explanation, proof, counterexample, comparison. Need be careful. Need likely include algorithm, invariant, proof by induction, contradiction. Counterexample negative edge where Dijkstra fails. Compare Bellman-Ford: handles negative edges no negative cycles, O(VE), DP relaxation, detects negative cycles. Maybe mention Dijkstra with priority queue O((V+E)logV) or O(E+VlogV). Need maybe include pseudocode? Could include. Need ensure proof rigorous: nonnegative weights ensure once vertex extracted min tentative distance, it's final. Lemma: for any unsettled vertex v, dist[v] >= true shortest distance? Actually tentative distances are upper bounds. Need show extracted u has dist[u]=δ(s,u). Suppose not, there is shorter path P. Let y be first unsettled vertex on P, x predecessor settled. Since edge weights nonnegative, δ(s,y) <= δ(s,u) < dist[u]? Need formal. Let's craft.\n\nNeed maybe discuss Dijkstra variants: greedy selects unvisited vertex with smallest tentative distance. Nonnegative weights crucial because path length cannot decrease by adding edges. If negative edge, a settled vertex could later be improved via unsettled vertex with larger tentative distance because negative edge reduces total.\n\nCounterexample: graph s->a weight 1, s->b weight 2, b->a weight -2. Shortest s->a = 0 via s-b-a. Dijkstra: dist s=0, a=1, b=2. Extract a (1) finalize. Then extract b (2), relax b->a gives 0 but a already finalized, if algorithm doesn't update finalized, returns 1. If implementation allows reinsert? Standard Dijkstra with visited set fails. If no visited but priority queue with duplicates? It might update a after finalized? Standard Dijkstra assumes no negative edges; if you allow reprocessing, it may not terminate with negative cycles, but with this graph if you don't mark visited, it could find 0? Let's see: start PQ (s0). pop s, relax a1,b2. pop a1, relax none. pop b2, relax a0 push. pop a0. If no visited, returns 0. But standard Dijkstra marks visited and doesn't revisit. Need present standard. Or use counterexample where even with reinsert? Negative cycles cause infinite. But for negative edge no cycle, Dijkstra with visited fails. Need maybe mention if implementation doesn't finalize, it's not Dijkstra's correctness proof and may fail with negative cycles.\n\nAlternative counterexample: s->a=1, s->b=2, b->a=-2. Dijkstra finalizes a at 1, misses 0. Good.\n\nNeed compare Bellman-Ford: initializes dist[s]=0, relax all edges V-1 times. Works with negative weights because it doesn't finalize vertices; repeated relaxations propagate improvements along paths with up to k edges. Proof by induction: after i iterations, dist[v] <= length of shortest path from s to v using at most i edges. If no negative cycles, shortest simple path has at most V-1 edges, so after V-1 iterations optimal. Extra iteration detects negative cycle reachable from source if any edge can still relax. Complexity O(VE), slower than Dijkstra but handles negative edges. Dijkstra faster but requires nonnegative. Also mention Johnson's algorithm? Not necessary.\n\nNeed maybe include proof of nonnegative: Let's formulate.\n\nDefinitions: directed graph G=(V,E), weight w:E->R nonnegative. Source s. δ(v) shortest path distance. Dijkstra maintains S settled vertices, d[v] tentative. Initially d[s]=0, others ∞. At each step choose u∉S with minimum d[u], add to S, relax outgoing edges.\n\nTheorem: If all weights ≥0, when u added to S, d[u]=δ(u).\n\nProof:\nInvariant: For every v, d[v] ≥ δ(v)? Actually d[v] is length of some discovered path, so d[v] ≥ δ(v) (upper bound). For settled vertices, d=δ. Need prove.\n\nInduction on |S|. Base S={s}: d[s]=0=δ(s) if no negative cycles? With nonnegative, shortest from s to s is 0 (unless negative cycle reachable? nonnegative no negative cycles). For v≠s, d[v]≥δ(v).\n\nInductive step: assume all settled have correct distances. Let u be min tentative among unsettled. Need show d[u]=δ(u). Suppose δ(u)y relaxed, so d[y] ≤ d[x]+w(x,y)=δ(x)+w(x,y). Since P is shortest and weights nonnegative, δ(y) ≤ δ(u) (because suffix y->u has nonnegative length, so δ(u) ≤ δ(y)+nonnegative? Wait need δ(y) ≤ δ(u). If P is shortest s->u, prefix s->y is a shortest path to y? In nonnegative? Actually any prefix of a shortest path is shortest if no negative cycles? With nonnegative yes, if prefix not shortest, replace prefix to get shorter s->u. So δ(y)=δ(x)+w(x,y). Also δ(u)=δ(y)+length(y->u) ≥ δ(y) because suffix nonnegative. Thus d[y] ≤ δ(y) ≤ δ(u) < d[u]. But y unsettled, contradicting u chosen with minimum tentative distance. Therefore δ(u)≥d[u]. Since d[u] is length of some path, d[u]≥δ(u). So equality.\n\nNeed be careful: d[y] ≤ δ(y) ≤ δ(u) < d[u]. If δ(y) could be less than d[y]? d[y] is upper bound, so d[y]≥δ(y). We have d[y] ≤ δ(y) from relaxation? Actually d[y] ≤ d[x]+w = δ(x)+w = δ(y) if prefix shortest. So d[y]=δ(y). Good. Then contradiction.\n\nAlternative proof: Suppose u settled with d[u]>δ(u). Let P shortest path. Let y first unsettled, x previous settled. Since weights nonnegative, δ(y) ≤ δ(u) < d[u]. But d[y] ≤ δ(y) < d[u], contradiction. Need justify d[y] ≤ δ(y)? d[y] is upper bound, so d[y]≥δ(y), not ≤. But after x settled, d[y] ≤ d[x]+w(x,y). If prefix shortest, d[x]=δ(x), so d[y] ≤ δ(x)+w(x,y)=δ(y). Combined with d[y]≥δ(y) gives equality. Good.\n\nNeed maybe mention if zero weights okay: nonnegative includes zero; proof uses suffix length ≥0. If zero, δ(y) ≤ δ(u) still.\n\nNeed maybe discuss why negative breaks proof: suffix can have negative length, so δ(y) may be > δ(u); a vertex y with larger tentative distance can lead to shorter path to u via negative edge. In counterexample, y=b has d=2 > d[a]=1, but δ(a)=0 <1.\n\nNeed compare Bellman-Ford maybe table.\n\nNeed maybe include pseudocode for Dijkstra and Bellman-Ford.\n\nPotential nuance: Dijkstra with nonnegative weights but negative edges not reachable? If negative edges not affecting source? It still works if no negative edge on any path that can improve settled? But general condition: nonnegative weights sufficient; not necessary. Could mention.\n\nNeed maybe include proof of Bellman-Ford: after k iterations, d[v] ≤ shortest path using ≤k edges. Induction. If no negative cycles, shortest path simple ≤V-1 edges. If negative cycle reachable, distances undefined (-∞); BF detects by Vth iteration relaxation.\n\nNeed maybe include complexity and use cases.\n\nNeed final answer structured: 1. Setup and algorithm. 2. Why nonnegative matters/intuition. 3. Proof. 4. Counterexample. 5. Bellman-Ford comparison. Maybe include table.\n\nNeed ensure no hidden chain-of-thought in final. Just answer.\n\nLet's craft detailed proof.\n\nPotential issue: In proof, we assume shortest path exists. With nonnegative weights, if no negative cycles, shortest path exists (finite) for reachable vertices; if unreachable δ=∞. Dijkstra sets ∞. For reachable, finite. If graph has zero cycles, still shortest simple path exists. Good.\n\nLet's write:\n\nLet δ(v) be infimum length of s-v paths. With nonnegative weights, if reachable, there is a shortest path with no repeated vertices (remove cycles nonnegative doesn't increase length). So finite.\n\nDijkstra pseudocode:\nfor v: d[v]=∞; d[s]=0; S=∅; PQ with s.\nwhile PQ not empty:\n u=extract-min\n if u in S continue? Standard visited.\n S.add(u)\n for (u,v) in E: if d[v] > d[u]+w(u,v): d[v]=...\n\nProof:\nInvariant: (1) d[v] is length of some s-v path (or ∞), hence d", "sha256": "fd3a67bbb6d4633e566a26b2438cfead87191d87594da1e3543ab4265f7c6832"} +{"name": "long-output-16384", "passed": true, "finish": "length", "usage": {"prompt_tokens": 75, "total_tokens": 16459, "completion_tokens": 16384, "completion_tokens_details": {"reasoning_tokens": 12136}}, "ttft_s": 0.07076310200136504, "elapsed_s": 213.90097701500054, "decode_tps": 76.61686204301199, "max_stream_gap_s": 0.03923290800048562, "events": 16379, "content": "\n\n# Chapter 1: Algorithms — Specification, Design, Analysis, and Implementation\n\n## 1.1 What Is an Algorithm?\n\nAn **algorithm** is a finite, precise sequence of computational steps that transforms an input into an output. Algorithms are the core of computer science because they describe *how* a problem is solved, not merely *what* the answer is.\n\nA well-formed algorithm usually has the following properties:\n\n1. **Input**: zero or more values supplied to the algorithm.\n2. **Output**: at least one value produced by the algorithm.\n3. **Finiteness**: the algorithm terminates after a finite number of steps.\n4. **Definiteness**: each step is unambiguous.\n5. **Effectiveness**: each step is basic enough to be carried out mechanically.\n6. **Generality**: the algorithm works for a class of inputs, not just one specific case.\n\n### Example 1.1: Euclid’s Algorithm for Greatest Common Divisor\n\nThe greatest common divisor, or GCD, of two integers is the largest positive integer that divides both.\n\nEuclid’s algorithm is based on the identity:\n\n\\[\n\\gcd(a,b) = \\gcd(b, a \\bmod b)\n\\]\n\nThe process repeats until the second argument becomes zero.\n\n#### Python Implementation\n\n```python\ndef gcd(a: int, b: int) -> int:\n \"\"\"\n Return the greatest common divisor of a and b.\n \"\"\"\n a, b = abs(a), abs(b)\n while b != 0:\n a, b = b, a % b\n return a\n```\n\n#### Worked Example\n\nCompute `gcd(48, 18)`.\n\n| Step | a | b | a % b |\n|---:|---:|---:|---:|\n| 1 | 48 | 18 | 12 |\n| 2 | 18 | 12 | 6 |\n| 3 | 12 | 6 | 0 |\n| 4 | 6 | 0 | stop |\n\nThe algorithm returns `6`.\n\n#### Complexity\n\nIf the numbers are bounded by `n`, Euclid’s algorithm runs in:\n\n\\[\nO(\\log n)\n\\]\n\narithmetic steps.\n\n---\n\n## 1.2 Specifying Algorithms\n\nBefore writing code, we should specify what the algorithm is supposed to do. A good specification includes:\n\n- **Preconditions**: assumptions about the input.\n- **Postconditions**: guarantees about the output.\n- **Invariants**: properties that remain true during execution.\n\n### Example 1.2: Maximum of a List\n\n**Problem**: Given a nonempty list of numbers, return the largest element.\n\n**Precondition**: `a` is nonempty.\n\n**Postcondition**: The returned value `m` satisfies:\n\n\\[\nm \\in a\n\\]\n\nand for every `x` in `a`:\n\n\\[\nx \\le m\n\\]\n\n#### Python Implementation\n\n```python\ndef maximum(a: list[int | float]) -> int | float:\n \"\"\"\n Return the maximum element of a nonempty list.\n \"\"\"\n if not a:\n raise ValueError(\"maximum() requires a nonempty list\")\n\n m = a[0]\n for x in a[1:]:\n if x > m:\n m = x\n return m\n```\n\n#### Loop Invariant\n\nAfter processing the first `i` elements, `m` is the maximum of `a[0:i]`.\n\nAt termination, `i == len(a)`, so `m` is the maximum of the entire list.\n\n---\n\n## 1.3 Correctness of Algorithms\n\nAn algorithm is **correct** if, for every valid input, it produces an output satisfying the specification.\n\nThere are two common notions:\n\n- **Partial correctness**: if the algorithm terminates, the result is correct.\n- **Total correctness**: the algorithm terminates and the result is correct.\n\n### Example 1.3: Linear Search\n\n**Problem**: Search for a target value in a list. Return its index if found, otherwise return `-1`.\n\n#### Python Implementation\n\n```python\ndef linear_search(a: list[int], target: int) -> int:\n \"\"\"\n Return the index of target in a, or -1 if absent.\n \"\"\"\n for i, x in enumerate(a):\n if x == target:\n return i\n return -1\n```\n\n#### Worked Example\n\nSearch for `7` in:\n\n```python\na = [4, 2, 7, 1, 9]\n```\n\n| Index | Value | Comparison | Result |\n|---:|---:|---|---|\n| 0 | 4 | 4 == 7? | continue |\n| 1 | 2 | 2 == 7? | continue |\n| 2 | 7 | 7 == 7? | return 2 |\n\n#### Invariant\n\nAt the start of each loop iteration with index `i`, if `target` occurs in `a`, then its first occurrence is at some index `j >= i`.\n\nWhen the loop finishes without returning, no occurrence exists.\n\n---\n\n## 1.4 Efficiency and Asymptotic Notation\n\nAlgorithms are evaluated by their resource usage, especially:\n\n- **Time**: number of elementary operations.\n- **Space**: amount of memory used.\n\nBecause exact operation counts depend on hardware and implementation, we use **asymptotic notation** to describe growth as input size increases.\n\n### Big-O Notation\n\nWe write:\n\n\\[\nf(n) = O(g(n))\n\\]\n\nif there exist constants `c > 0` and `n0` such that for all `n >= n0`:\n\n\\[\nf(n) \\le c \\cdot g(n)\n\\]\n\nBig-O gives an upper bound.\n\n### Big-Omega Notation\n\nWe write:\n\n\\[\nf(n) = \\Omega(g(n))\n\\]\n\nif there exist constants `c > 0` and `n0` such that for all `n >= n0`:\n\n\\[\nf(n) \\ge c \\cdot g(n)\n\\]\n\nBig-Omega gives a lower bound.\n\n### Big-Theta Notation\n\nWe write:\n\n\\[\nf(n) = \\Theta(g(n))\n\\]\n\nif:\n\n\\[\nf(n) = O(g(n))\n\\]\n\nand:\n\n\\[\nf(n) = \\Omega(g(n))\n\\]\n\nBig-Theta gives a tight bound.\n\n### Common Growth Rates\n\nFrom slowest to fastest growth:\n\n\\[\n1 < \\log n < n < n \\log n < n^2 < n^3 < 2^n < n!\n\\]\n\n### Example 1.4: Comparing Functions\n\nSuppose:\n\n```python\ndef f(n):\n return 3 * n + 5\n```\n\nThen:\n\n\\[\nf(n) = \\Theta(n)\n\\]\n\nbecause the dominant term is `3n`.\n\nSuppose:\n\n```python\ndef g(n):\n return n * n + 10 * n + 100\n```\n\nThen:\n\n\\[\ng(n) = \\Theta(n^2)\n\\]\n\n---\n\n## 1.5 Recurrences and the Master Theorem\n\nMany recursive algorithms are analyzed using recurrences.\n\nFor example, if an algorithm divides a problem of size `n` into `a` subproblems of size `n/b`, and uses `f(n)` work outside recursion, then:\n\n\\[\nT(n) = aT(n/b) + f(n)\n\\]\n\n### Master Theorem\n\nFor:\n\n\\[\nT(n) = aT(n/b) + f(n)\n\\]\n\nwhere `a >= 1`, `b > 1`:\n\n1. If:\n\n\\[\nf(n) = O(n^{\\log_b a - \\epsilon})\n\\]\n\nfor some `epsilon > 0`, then:\n\n\\[\nT(n) = \\Theta(n^{\\log_b a})\n\\]\n\n2. If:\n\n\\[\nf(n) = \\Theta(n^{\\log_b a} \\log^k n)\n\\]\n\nthen:\n\n\\[\nT(n) = \\Theta(n^{\\log_b a} \\log^{k+1} n)\n\\]\n\n3. If:\n\n\\[\nf(n) = \\Omega(n^{\\log_b a + \\epsilon})\n\\]\n\nfor some `epsilon > 0`, and a regularity condition holds, then:\n\n\\[\nT(n) = \\Theta(f(n))\n\\]\n\n### Example 1.5: Merge Sort Recurrence\n\nMerge sort satisfies:\n\n\\[\nT(n) = 2T(n/2) + \\Theta(n)\n\\]\n\nHere:\n\n\\[\na = 2, \\quad b = 2, \\quad f(n) = \\Theta(n)\n\\]\n\n\\[\n\\log_b a = \\log_2 2 = 1\n\\]\n\nSo:\n\n\\[\nf(n) = \\Theta(n^{\\log_b a})\n\\]\n\nThis is case 2 with `k = 0`, giving:\n\n\\[\nT(n) = \\Theta(n \\log n)\n\\]\n\n---\n\n# 2. Searching Algorithms\n\nSearching is one of the most fundamental algorithmic tasks.\n\n## 2.1 Linear Search\n\nLinear search checks elements one by one.\n\n### Complexity\n\n- Best case: `O(1)`\n- Worst case: `O(n)`\n- Average case: `O(n)`\n\n### Python Implementation\n\n```python\ndef linear_search(a: list[int], target: int) -> int:\n for i, x in enumerate(a):\n if x == target:\n return i\n return -1\n```\n\n### Additional Example: Linear Search with Predicate\n\nSometimes we search for the first element satisfying a condition.\n\n```python\ndef find_first(a: list[int], predicate) -> int:\n for i, x in enumerate(a):\n if predicate(x):\n return i\n return -1\n```\n\nExample:\n\n```python\nassert find_first([1, 4, 7, 10], lambda x: x > 5) == 2\n```\n\n---\n\n## 2.2 Binary Search\n\nBinary search works on sorted arrays. It repeatedly compares the target to the middle element and discards half of the remaining search interval.\n\n### Preconditions\n\n- The array must be sorted in nondecreasing order.\n\n### Python Implementation\n\n```python\ndef binary_search(a: list[int], target: int) -> int:\n \"\"\"\n Return the index of target in sorted list a, or -1 if absent.\n \"\"\"\n lo, hi = 0, len(a) - 1\n\n while lo <= hi:\n mid = (lo + hi) // 2\n\n if a[mid] == target:\n return mid\n elif a[mid] < target:\n lo = mid + 1\n else:\n hi = mid - 1\n\n return -1\n```\n\n### Worked Example\n\nSearch for `23` in:\n\n```python\na = [2, 5, 8, 12, 16, 23, 38, 56, 72, 91]\n```\n\n| Iteration | lo | hi | mid | a[mid] | Action |\n|---:|---:|---:|---:|---:|---|\n| 1 | 0 | 9 | 4 | 16 | 16 < 23, so lo = 5 |\n| 2 | 5 | 9 | 7 | 56 | 56 > 23, so hi = 6 |\n| 3 | 5 | 6 | 5 | 23 | found, return 5 |\n\n### Correctness Invariant\n\nAt the start of each iteration, if `target` exists in `a`, then its index lies in the interval `[lo, hi]`.\n\n### Complexity\n\nEach iteration halves the search interval.\n\n\\[\nT(n) = T(n/2) + O(1)\n\\]\n\nTherefore:\n\n\\[\nT(n) = O(\\log n)\n\\]\n\n### Lower Bound\n\nAny comparison-based search in an unsorted list requires `Omega(n)` comparisons in the worst case.\n\nFor sorted lists, binary search is optimal up to constant factors.\n\n---\n\n## 2.3 Additional Example: Lower Bound and Upper Bound\n\nBinary search can also find insertion positions.\n\n### Lower Bound\n\n`lower_bound(a, x)` returns the first index where `x` could be inserted without breaking sorted order.\n\n```python\ndef lower_bound(a: list[int], x: int) -> int:\n lo, hi = 0, len(a)\n while lo < hi:\n mid = (lo + hi) // 2\n if a[mid] < x:\n lo = mid + 1\n else:\n hi = mid\n return lo\n```\n\n### Upper Bound\n\n`upper_bound(a, x)` returns the first index where all elements are strictly greater than `x`.\n\n```python\ndef upper_bound(a: list[int], x: int) -> int:\n lo, hi = 0, len(a)\n while lo < hi:\n mid = (lo + hi) // 2\n if a[mid] <= x:\n lo = mid + 1\n else:\n hi = mid\n return lo\n```\n\n### Example\n\n```python\na = [1, 2, 2, 2, 3, 5]\n\nassert lower_bound(a, 2) == 1\nassert upper_bound(a, 2) == 4\n```\n\n---\n\n## 2.4 Additional Example: Binary Search on Answer\n\nBinary search is not limited to searching arrays. It can search over a monotonic answer space.\n\n### Problem\n\nGiven package weights and a number of days, find the minimum ship capacity such that all packages can be shipped within the given days.\n\n### Python Implementation\n\n```python\ndef min_ship_capacity(weights: list[int], days: int) -> int:\n \"\"\"\n Return the minimum capacity needed to ship all packages within days.\n \"\"\"\n lo = max(weights)\n hi = sum(weights)\n\n def feasible(capacity: int) -> bool:\n used_days = 1\n current_load = 0\n\n for w in weights:\n if current_load + w > capacity:\n used_days += 1\n current_load = 0\n current_load += w\n\n return used_days <= days\n\n while lo < hi:\n mid = (lo + hi) // 2\n if feasible(mid):\n hi = mid\n else:\n lo = mid + 1\n\n return lo\n```\n\n### Example\n\n```python\nweights = [1, 2, 3, 4, 5, 6, 7, 8, 9, 10]\ndays = 5\n\nassert min_ship_capacity(weights, days) == 15\n```\n\n---\n\n## 2.5 Additional Example: Two-Pointer Search\n\nFor a sorted array, two pointers can solve many problems in linear time.\n\n### Problem\n\nFind two numbers in a sorted array that sum to a target.\n\n### Python Implementation\n\n```python\ndef two_sum_sorted(a: list[int], target: int) -> tuple[int, int] | None:\n \"\"\"\n Return indices of two elements summing to target, or None.\n \"\"\"\n i, j = 0, len(a) - 1\n\n while i < j:\n s = a[i] + a[j]\n\n if s == target:\n return i, j\n elif s < target:\n i += 1\n else:\n j -= 1\n\n return None\n```\n\n### Example\n\n```python\na = [1, 2, 4, 7, 11, 15]\n\nassert two_sum_sorted(a, 9) == (1, 3)\n```\n\n### Complexity\n\n\\[\nO(n)\n\\]\n\n---\n\n# 3. Sorting Algorithms\n\nSorting arranges elements in a specified order. Sorting is foundational because many algorithms require sorted input.\n\n## 3.1 Insertion Sort\n\nInsertion sort builds a sorted portion of the array one element at a time.\n\n### Python Implementation\n\n```python\ndef insertion_sort(a: list[int]) -> list[int]:\n \"\"\"\n Sort a in place and return it.\n \"\"\"\n for i in range(1, len(a)):\n key = a[i]\n j = i - 1\n\n while j >= 0 and a[j] > key:\n a[j + 1] = a[j]\n j -= 1\n\n a[j + 1] = key\n\n return a\n```\n\n### Worked Example\n\nSort:\n\n```python\na = [5, 2, 4, 6, 1, 3]\n```\n\n| Step | Array |\n|---:|---|\n| Initial | `[5, 2, 4, 6, 1, 3]` |\n| Insert 2 | `[2, 5, 4, 6, 1, 3]` |\n| Insert 4 | `[2, 4, 5, 6, 1, 3]` |\n| Insert 6 | `[2, 4, 5, 6, 1, 3]` |\n| Insert 1 | `[1, 2, 4, 5, 6, 3]` |\n| Insert 3 | `[1, 2, 3, 4, 5, 6]` |\n\n### Complexity\n\n- Best case, already sorted: `O(n)`\n- Worst case, reverse sorted: `O(n^2)`\n- Average case: `O(n^2)`\n\n### Properties\n\n- In-place\n- Stable\n- Good for small arrays or nearly sorted arrays\n\n---\n\n## 3.2 Merge Sort\n\nMerge sort is a divide-and-conquer algorithm.\n\n### Idea\n\n1. Split the array into two halves.\n2. Recursively sort each half.\n3. Merge the two sorted halves.\n\n### Python Implementation\n\n```python\ndef merge_sort(a: list[int]) -> list[int]:\n \"\"\"\n Return a sorted copy of a.\n \"\"\"\n if len(a) <= 1:\n return a\n\n mid = len(a) // 2\n left = merge_sort(a[:mid])\n right = merge_sort(a[mid:])\n\n return merge(left, right)\n\n\ndef merge(left: list[int], right: list[int]) -> list[int]:\n \"\"\"\n Merge two sorted lists into one sorted list.\n \"\"\"\n i = j = 0\n result = []\n\n while i < len(left) and j < len(right):\n if left[i] <= right[j]:\n result.append(left[i])\n i += 1\n else:\n result.append(right[j])\n j += 1\n\n", "reasoning": "We need to respond to user: \"Write an extensive technical textbook chapter explaining algorithms, with many worked examples and Python implementations. Continue with additional examples throughout.\"\n\nWe need produce final answer: extensive technical textbook chapter. Need likely include many examples and Python implementations. Need maybe continue with additional examples throughout. We can write a long chapter. Need ensure no analysis in final? final only content. Need maybe structure: title, objectives, sections: what is algorithm, correctness, complexity, asymptotic notation, design strategies, sorting, searching, graphs, DP, greedy, divide and conquer, backtracking, randomized, amortized, practical considerations. Include worked examples and Python code. Extensive. Need maybe not too huge? Desired oververbosity 9. User asks extensive. We can produce a long chapter, maybe 10k-15k words? Token budget maybe okay. Need include many worked examples and Python implementations. Need continue with additional examples throughout. Could be a textbook chapter with exercises? Need maybe include code blocks. Need ensure Python implementations correct.\n\nNeed think about content. We can write chapter: \"Algorithms: Specification, Design, Analysis, and Implementation\". Include definitions, pseudocode, Python. Worked examples: linear search, binary search, insertion sort, merge sort, quicksort, heap sort? BFS/DFS, Dijkstra, Floyd-Warshall, DP Fibonacci, knapsack, LCS, greedy coin change, Huffman? Backtracking N-Queens. Maybe also string matching KMP? Maybe too much. Need many examples.\n\nNeed maybe include complexity tables. Need include worked examples with recurrence solving. Need include Python implementations with type hints. Need include tests? Could include doctest style.\n\nNeed be careful with code correctness. Let's plan.\n\nChapter outline:\n1. Introduction: algorithm as finite sequence of instructions, properties: input, output, finiteness, effectiveness, determinism. Example: making tea? formal.\n2. Algorithm specification: pre/postconditions, invariants. Example: max of array.\n3. Correctness: partial/total, loop invariants. Worked example: linear search invariant.\n4. Efficiency: RAM model, operations, best/worst/average, asymptotic notation definitions, examples.\n5. Design paradigms: brute force, divide and conquer, greedy, DP, backtracking, graph algorithms, randomized.\n6. Searching: linear search, binary search (iterative/recursive), lower bound, worked example trace. Python.\n7. Sorting: insertion sort, merge sort, quicksort, heap sort maybe, counting sort. Worked examples, recurrences, Python.\n8. Graph algorithms: representation, BFS, DFS, topological sort, shortest paths Dijkstra, Bellman-Ford, Floyd-Warshall. Python.\n9. Dynamic programming: principles, Fibonacci, coin change, knapsack, LCS. Python.\n10. Greedy: activity selection, fractional knapsack, Huffman? Worked examples, proof idea.\n11. Backtracking: N-Queens, subset sum. Python.\n12. String algorithms: Rabin-Karp? KMP? Maybe include simple and KMP.\n13. Amortized analysis: dynamic array. Python.\n14. Randomized algorithms: quicksort randomized, Monte Carlo/ Las Vegas. Python.\n15. Practical considerations: recursion limits, memory, I/O, testing, profiling.\n16. Summary, exercises.\n\nNeed \"Continue with additional examples throughout.\" Could include \"Additional Example\" boxes.\n\nNeed maybe include mathematical notation. Need final answer only. Need maybe not too terse.\n\nLet's draft. Need ensure code correct.\n\nPotential code:\n\nlinear_search:\ndef linear_search(a, target):\n for i,x in enumerate(a):\n if x == target: return i\n return -1\n\nbinary_search:\ndef binary_search(a, target):\n lo, hi = 0, len(a)-1\n while lo <= hi:\n mid = (lo+hi)//2\n if a[mid] == target: return mid\n elif a[mid] < target: lo = mid+1\n else: hi = mid-1\n return -1\n\nTrace example.\n\nInsertion sort:\ndef insertion_sort(a):\n for i in range(1,len(a)):\n key=a[i]; j=i-1\n while j>=0 and a[j] > key:\n a[j+1]=a[j]; j-=1\n a[j+1]=key\n return a\n\nMerge sort:\ndef merge_sort(a):\n if len(a)<=1: return a\n mid=len(a)//2\n left=merge_sort(a[:mid]); right=merge_sort(a[mid:])\n return merge(left,right)\ndef merge(left,right):\n i=j=0; out=[]\n while i pivot]\n return quicksort(less)+equal+quicksort(greater)\nBut not in-place. Could include in-place Lomuto:\ndef quicksort_inplace(a, low=0, high=None):\n if high is None: high=len(a)-1\n if low < high:\n p=partition(a, low, high)\n quicksort_inplace(a, low, p-1)\n quicksort_inplace(a, p+1, high)\ndef partition(a, low, high):\n pivot=a[high]; i=low-1\n for j in range(low, high):\n if a[j] <= pivot:\n i+=1; a[i],a[j]=a[j],a[i]\n a[i+1],a[high]=a[high],a[i+1]\n return i+1\n\nHeap sort:\ndef heap_sort(a):\n import heapq? Could implement. Use heapq.heapify then pop. But textbook maybe implement sift.\n n=len(a)\n def sift_down(start, end):\n root=start\n while True:\n child=2*root+1\n if child>end: break\n if child+1 <= end and a[child+1] > a[child]: child +=1\n if a[root] < a[child]:\n a[root],a[child]=a[child],a[root]\n root=child\n else: break\n for i in range(n//2-1,-1,-1): sift_down(i,n-1)\n for end in range(n-1,0,-1):\n a[0],a[end]=a[end],a[0]\n sift_down(0,end-1)\n return a\n\nCounting sort:\ndef counting_sort(a, k=None):\n if not a: return []\n if k is None: k=max(a)+1\n counts=[0]*k\n for x in a: counts[x]+=1\n out=[]\n for i,c in enumerate(counts): out.extend([i]*c)\n return out\n\nGraphs:\nadj dict. BFS:\ndef bfs(graph, start):\n visited={start}; queue=deque([start]); order=[]\n while queue:\n u=queue.popleft(); order.append(u)\n for v in graph[u]:\n if v not in visited:\n visited.add(v); queue.append(v)\n return order\n\nDFS recursive/iterative.\n\nTopological sort Kahn:\ndef topological_sort(graph):\n indeg={u:0 for u in graph}\n for u in graph:\n for v in graph[u]: indeg[v]=indeg.get(v,0)+1\n queue=deque([u for u,d in indeg.items() if d==0])\n order=[]\n while queue:\n u=queue.popleft(); order.append(u)\n for v in graph[u]:\n indeg[v]-=1\n if indeg[v]==0: queue.append(v)\n if len(order)!=len(indeg): raise ValueError(\"cycle\")\n return order\n\nDijkstra:\ndef dijkstra(graph, source):\n dist={u: math.inf for u in graph}\n dist[source]=0\n pq=[(0,source)]\n while pq:\n d,u=heappop(pq)\n if d != dist[u]: continue\n for v,w in graph[u].items():\n nd=d+w\n if nd < dist.get(v, math.inf):\n dist[v]=nd; heappush(pq,(nd,v))\n return dist\n\nBellman-Ford:\ndef bellman_ford(graph, source):\n dist={u: math.inf for u in graph}\n dist[source]=0\n vertices=list(graph)\n for _ in range(len(vertices)-1):\n updated=False\n for u in graph:\n if dist[u] == math.inf: continue\n for v,w in graph[u].items():\n if dist[u]+w < dist.get(v, math.inf):\n dist[v]=dist[u]+w; updated=True\n if not updated: break\n for u in graph:\n for v,w in graph[u].items():\n if dist[u]+w < dist.get(v, math.inf): raise ValueError(\"negative cycle\")\n return dist\n\nFloyd-Warshall:\ndef floyd_warshall(graph):\n vertices=list(graph)\n dist={u:{v: math.inf for v in vertices} for u in vertices}\n for u in vertices: dist[u][u]=0\n for u in graph:\n for v,w in graph[u].items(): dist[u][v]=min(dist[u][v], w)\n for k in vertices:\n for i in vertices:\n if dist[i][k] == math.inf: continue\n for j in vertices:\n if dist[k][j] == math.inf: continue\n if dist[i][k]+dist[k][j] < dist[i][j]: dist[i][j]=dist[i][k]+dist[k][j]\n return dist\n\nDP:\nfib memo:\ndef fib(n):\n if n<2: return n\n a,b=0,1\n for _ in range(2,n+1): a,b=b,a+b\n return b\n\ncoin change min coins:\ndef coin_change(coins, amount):\n dp=[math.inf]*(amount+1); dp[0]=0\n for i in range(1, amount+1):\n for c in coins:\n if c <= i and dp[i-c]+1 < dp[i]: dp[i]=dp[i-c]+1\n return -1 if dp[amount]==math.inf else dp[amount]\n\n0/1 knapsack:\ndef knapsack(weights, values, capacity):\n n=len(weights)\n dp=[0]*(capacity+1)\n for i in range(n):\n for w in range(capacity, weights[i]-1, -1):\n dp[w]=max(dp[w], dp[w-weights[i]]+values[i])\n return dp[capacity]\n\nLCS:\ndef lcs_length(x,y):\n m,n=len(x),len(y)\n dp=[[0]*(n+1) for _ in range(m+1)]\n for i in range(1,m+1):\n for j in range(1,n+1):\n if x[i-1]==y[j-1]: dp[i][j]=dp[i-1][j-1]+1\n else: dp[i][j]=max(dp[i-1][j], dp[i][j-1])\n return dp[m][n]\n\nGreedy activity selection:\ndef activity_selection(activities): # (start,end)\n sorted by end\n selected=[]; last_end=-inf\n for s,e in sorted:\n if s >= last_end: selected.append((s,e)); last_end=e\n return selected\n\nFractional knapsack:\ndef fractional_knapsack(items, capacity): # (weight,value)\n sorted by value/weight desc\n total=0\n for w,v in items:\n if capacity <=0: break\n take=min(w, capacity)\n total += take * (v/w)\n capacity -= take\n return total\n\nHuffman maybe code? Could include but lengthy. Maybe skip or include simple.\n\nBacktracking N-Queens:\ndef solve_n_queens(n):\n solutions=[]\n cols=set(); diag1=set(); diag2=set()\n def backtrack(row, board):\n if row==n:\n solutions.append(board[:])\n return\n for col in range(n):\n if col in cols or row-col in diag1 or row+col in diag2: continue\n cols.add(col); diag1.add(row-col); diag2.add(row+col)\n board.append(col)\n backtrack(row+1, board)\n board.pop(); cols.remove(col); diag1.remove(row-col); diag2.remove(row+col)\n backtrack(0, [])\n return solutions\n\nSubset sum:\ndef subset_sum(nums, target):\n def backtrack(i, current):\n if current == target: return True\n if i == len(nums) or current > target: return False\n return backtrack(i+1, current+nums[i]) or backtrack(i+1, current)\n return backtrack(0,0)\nBut negative? assume positive.\n\nString matching:\nnaive:\ndef naive_search(text, pattern):\n n,m=len(text),len(pattern)\n for i in range(n-m+1):\n if text[i:i+m] == pattern: return i\n return -1\n\nKMP:\ndef kmp_search(text, pattern):\n def lps(p):\n m=len(p); lps=[0]*m; length=0; i=1\n while i pivot]\n return randomized_quicksort(less)+equal+randomized_quicksort(greater)\n\nNeed include worked examples with traces.\n\nNeed maybe include complexity analysis:\n- Linear search worst O(n), best O(1), average O(n).\n- Binary search O(log n), recurrence T(n)=T(n/2)+O(1).\n- Insertion sort worst O(n^2), best O(n), average O(n^2).\n- Merge sort recurrence T(n)=2T(n/2)+O(n)=O(n log n), stable, O(n) extra.\n- Quicksort average O(n log n), worst O(n^2), in-place O(log n) stack.\n- Heap sort O(n log n), in-place, not stable.\n- Counting sort O(n+k).\n- BFS/DFS O(V+E).\n- Dijkstra with binary heap O((V+E)log V), with Fibonacci O(E+VlogV).\n- Bellman-Ford O(VE).\n- Floyd-Warshall O(V^3).\n- DP examples.\n\nNeed include asymptotic notation definitions:\nBig-O: exists c,n0. Omega, Theta, o, omega. Examples: 3n+5 = O(n), not O(1). n log n.\n\nNeed include recurrence solving: Master theorem maybe.\n\nNeed include proof of correctness for binary search? Invariant: if target in a, it lies in [lo,hi]. Termination.\n\nNeed include worked examples:\nExample 1: max algorithm.\nExample 2: linear search trace.\nExample 3: binary search trace on [2,5,8,12,16,23,38,56,72,91] target 23.\nExample 4: insertion sort trace [5,2,4,6,1,3].\nExample 5: merge sort trace [8,3,1,5,9,2,7,4].\nExample 6: quicksort trace [6,1,4,9,2,8,3,5] pivot 5? maybe.\nExample 7: heap sort trace maybe.\nExample 8: BFS graph trace.\nExample 9: Dijkstra trace.\nExample 10: DP coin change table.\nExample 11: knapsack table.\nExample 12: LCS table.\nExample 13: greedy activity selection.\nExample 14: N-Queens partial.\nExample 15: KMP failure function.\n\nNeed maybe include exercises at end.\n\nNeed ensure final answer is a chapter, not just list. Use headings.\n\nNeed maybe mention Python code uses standard library. Need include imports.\n\nNeed maybe include \"Additional Example\" throughout.\n\nNeed maybe include \"worked example\" with step-by-step.\n\nNeed maybe include \"Python implementation\" code blocks.\n\nNeed maybe include \"Complexity\" after each.\n\nNeed maybe include \"Common pitfalls\".\n\nNeed maybe include \"Testing\" with assert.\n\nNeed maybe include \"Further reading\".\n\nNeed be mindful of final length. We can produce maybe 12,000 words? Token limit maybe 16k? We have enough? Need not exceed. We can produce around 10k-12k words. Let's estimate. Each section maybe 500-1000 words. 15 sections = 10k. Code blocks add. Need maybe okay.\n\nNeed maybe include mathematical formulas in plain text. Use Unicode? okay.\n\nNeed maybe include \"Chapter 1: Algorithms\".\n\nLet's craft final with detailed content.\n\nPotential final:\n\n# Chapter 1: Algorithms: Specification, Design, Analysis, and Implementation\n\nLearning objectives...\n\n1.1 What is an algorithm?\nDefinition, properties. Example: Euclid's algorithm.\nPython gcd.\nWorked example gcd(48,18).\n\n1.2 Specifying algorithms\nPre/postconditions, invariants. Example max.\nPython max.\nLoop invariant proof.\n\n1.3 Correctness\nPartial/total. Example linear search invariant.\n\n1.4 Efficiency and asymptotic notation\nRAM model, operation counts. Big-O definitions. Examples.\nTable.\n\n1.5 Recurrences and Master theorem.\n\n2 Searching\nLinear search code, trace.\nBinary search code, trace, proof, lower bound.\nAdditional example: binary search for first true predicate.\n\n3 Sorting\nInsertion sort code, trace, analysis.\nMerge sort code, trace, recurrence.\nQuicksort code, trace, analysis, randomized.\nHeap sort code, analysis.\nCounting sort code, analysis.\nComparison table.\n\n4 Graph algorithms\nRepresentation.\nBFS code, trace, shortest unweighted.\nDFS code, topological sort.\nDijkstra code, trace.\nBellman-Ford code.\nFloyd-Warshall code.\n\n5 Dynamic programming\nPrinciples.\nFibonacci.\nCoin change table.\n0/1 knapsack table.\nLCS table.\n\n6 Greedy\nActivity selection proof.\nFractional knapsack.\nHuffman maybe brief.\n\n7 Backtracking\nN-Queens code, trace.\nSubset sum.\n\n8 String matching\nNaive, KMP code, failure function example.\n\n9 Amortized analysis\nDynamic array.\n\n10 Randomized algorithms\nRandomized quicksort, Monte Carlo.\n\n11 Practical implementation\nTesting, profiling, recursion, memory.\n\n12 Summary and exercises.\n\nNeed include many worked examples. Could include \"Additional Example\" after each.\n\nNeed ensure code correctness. Let's test mentally.\n\nEuclid:\ndef gcd(a,b):\n while b:\n a,b=b,a%b\n return abs(a)\nWorks.\n\nMax:\ndef maximum(a):\n if not a: raise ValueError\n m=a[0]\n for x in a[1:]:\n if x>m: m=x\n return m\n\nLinear search trace:\na=[4,2,7,1,9], target=7. i=0 no, i=1 no, i=2 yes return 2.\n\nBinary search trace:\na sorted [2,5,8,12,16,23,38,56,72,91], target 23.\nlo=0 hi=9 mid=4 a[4]=16 <23 lo=5\nmid=(5+9)//2=7 a[7]=56 >23 hi=6\nmid=(5+6)//2=5 a[5]=23 return 5.\n\nInsertion sort trace [5,2,4,6,1,3]:\ni=1 key=2 shift 5 -> [2,5,4,6,1,3]\ni=2 key=4 shift 5 -> [2,4,5,6,1,3]\ni=3 key=6 no shift\ni=4 key=1 shift 6,5,4,2 -> [1,2,4,5,6,3]\ni=5 key=3 shift 6,5,4 -> [1,2,3,4,5,6]\n\nMerge sort trace maybe [8,3,1,5,9,2,7,4]: split [8,3,1,5] [9,2,7,4], etc. Could summarize.\n\nQuicksort trace with Lomuto pivot last. Array [6,1,4,9,2,8,3,5], pivot 5. Partition: i=-1. j=0 6>5; j=1 1<=5 i=0 swap a0,a1 -> [1,6,4,9,2,8,3,5]; j=2 4<=5 i=1 swap a1,a2 -> [1,4,6,9,2,8,3,5]; j=3 9>; j=4 2<=5 i=2 swap a2,a4 -> [1,4,2,9,6,8,3,5]; j=5 8>; j=6 3<=5 i=3 swap a3,a6 -> [1,4,2,3,6,8,9,5]; final swap i+1=4 with high=7 -> [1,4,2,3,5,8,9,6], pivot index 4. Recursively. Good.\n\nHeap sort trace maybe too long. Could include small [4,10,3,5,1]. Build max heap:\nn=5, start=1: sift 10? Let's not detailed.\n\nBFS trace graph:\ngraph = {\n 'A': ['B','C'],\n 'B': ['D','E'],\n 'C': ['F'],\n 'D': [],\n 'E': [],\n 'F': []\n}\nBFS order A B C D E F.\n\nDijkstra trace graph:\ngraph = {\n 'A': {'B':1, 'C':4},\n 'B': {'C':2, 'D':5},\n 'C': {'D':1},\n 'D': {}\n}\ndist A=0, B=1, C=3 via B, D=4 via C.\n\nBellman-Ford maybe negative edge example.\n\nFloyd-Warshall maybe small.\n\nDP coin change: coins [1,3,4], amount 6. dp: 0=0,1=1,2=2,3=1,4=1,5=2,6=2 (3+3 or 4+1+1? 2). Good.\n\nKnapsack: weights [2,3,4,5], values [3,4,5,6], capacity 5. Optimal 7? items 2+3 values 7 or 5 value 6. DP returns 7.\n\nLCS: \"ABCBDAB\", \"BDCABA\" length 4? Common \"BCBA\" or \"BDAB\". Good.\n\nActivity selection: activities [(1,4),(3,5),(0,6),(5,7),(3,8),(5,9),(6,10),(8,11),(8,12),(2,14),(12,16)] sorted by end: (1,4),(3,5),(0,6),(5,7),(3,8),(5,9),(6,10),(8,11),(8,12),(2,14),(12,16). Greedy selects (1,4),(5,7),(8,11),(12,16) maybe 4. Good.\n\nFractional knapsack: items [(10,60),(20,100),(30,120)], capacity 50. ratios 6,5,4. take 10 value60, 20 value100, 20 of 30 value80 total240.\n\nN-Queens n=4 solutions 2. Code returns list of column placements.\n\nSubset sum: nums [3,34,4,12,5,2], target 9 -> True (4+5). Code with positive and current > target works if all positive. Good.\n\nKMP example pattern \"ABABAC\" lps? Let's compute: p A B A B A C. lps[0]=0. i=1 B vs A no lps[1]=0. i=2 A vs A length=1 lps[2]=1. i=3 B vs p[1]=B length=2 lps[3]=2. i=4 A vs p[2]=A length=3 lps[4]=3. i=5 C vs p[3]=B no length=lps[2]=1; C vs p[1]=B no length=lps[0]=0; lps[5]=0. lps [0,0,1,2,3,0]. Good.\n\nDynamic array code: self._data += [None]*(self._capacity - len(self._data)). If capacity 0 -> max(1,0)=1, len=0, extend 1. If capacity 1 size=1, new cap=2, len=1, extend 1. Good. But if _data length equals capacity? yes. Could simpler use list append? But to illustrate capacity. Need maybe not use None? okay.\n\nRandomized quicksort code uses random.choice. Good.\n\nNeed maybe include \"assert\" tests.\n\nNeed maybe include \"Additional Example: binary search on rotated array\"? Could include. But already many.\n\nNeed maybe include \"Additional Example: selection sort\"? Could include but not necessary. User asks continue with additional examples throughout. We can include many.\n\nNeed maybe include \"Additional Example: two-pointer sum\". Could include in searching/sorting? Maybe add section \"Common patterns\" with two pointers, sliding window, prefix sums. But chapter already long. Could include additional examples throughout.\n\nMaybe include \"Additional Example: two-sum sorted array\" with Python.\n\nNeed maybe include \"Additional Example: sliding window maximum\"? Could be too much. But user wants extensive. Could include a section \"Additional algorithmic patterns\" with examples: two pointers, sliding window, prefix sums, binary search on answer. That adds value.\n\nNeed maybe include \"Additional Example: binary search on answer\" e.g. minimize max pages? Could include.\n\nNeed maybe include \"Additional Example: prefix sums range sum\".\n\nNeed maybe include \"Additional Example: stack parentheses\".\n\nBut final may become too long. Need balance.\n\nNeed maybe include \"Additional Example\" boxes after main sections.\n\nLet's design final with enough examples.\n\nPotential final structure:\n\n# Algorithms: A Technical Textbook Chapter\n\n## 1. What is an algorithm?\n- definition, properties\n- Example 1.1 Euclid\n- Python gcd\n- Worked trace\n\n## 2. Specification and correctness\n- pre/post, invariants\n- Example 2.1 maximum\n- Example 2.2 linear search invariant\n- Python\n\n## 3. Efficiency and asymptotic notation\n- definitions, examples, table\n- recurrence, master theorem\n- Example 3.1 operation count\n\n## 4. Searching\n- linear search code, trace\n- binary search code, trace, proof\n- Additional Example 4.1 binary search on answer (capacity)\n- Additional Example 4.2 two-pointer sorted two-sum\n\n## 5. Sorting\n- insertion sort code, trace\n- merge sort code, trace, recurrence\n- quicksort code, trace, randomized\n- heap sort code\n- counting sort code\n- comparison table\n- Additional Example 5.1 stable sort by key? Python sorted stable.\n\n## 6. Graph algorithms\n- representation\n- BFS code, trace, shortest path\n- DFS code, topological sort\n- Dijkstra code, trace\n- Bellman-Ford code\n- Floyd-Warshall code\n- Additional Example 6.1 connected components\n\n## 7. Dynamic programming\n- principles\n- Fibonacci\n- coin change table\n- knapsack table\n- LCS table\n- Additional Example 7.1 longest increasing subsequence O(n log n)? Could include O(n^2) and bisect.\n\n## 8. Greedy algorithms\n- activity selection code, proof\n- fractional knapsack code\n- Additional Example 8.1 minimum number of coins? not always greedy; counterexample.\n\n## 9. Backtracking\n- N-Queens code, trace\n- subset sum code\n- Additional Example 9.1 permutations\n\n## 10. String algorithms\n- naive search\n- KMP code, failure function example\n- Additional Example 10.1 Rabin-Karp? Could include simple rolling hash.\n\n## 11. Amortized analysis\n- dynamic array code, analysis\n\n## 12. Randomized algorithms\n- randomized quicksort, Monte Carlo primality? maybe simple.\n\n## 13. Practical considerations\n- testing, profiling, recursion, memory, I/O\n\n## 14. Summary and exercises\n\nNeed include code for LIS:\ndef lis_length(a):\n tails=[]\n for x in a:\n i=bisect_left(tails,x)\n if i==len(tails): tails.append(x)\n else: tails[i]=x\n return len(tails)\nNeed import bisect.\n\nRabin-Karp maybe:\ndef rabin_karp(text, pattern, base=256, mod=10**9+7):\n n,m=len(text),len(pattern)\n if m==0: return 0\n h=1\n for _ in range(m-1): h=(h*base)%mod\n p=0; t=0\n for i in range(m): p=(p*base+ord(pattern[i]))%mod; t=(t*base+ord(text[i]))%mod\n for i in range(n-m+1):\n if p==t and text[i:i+m]==pattern: return i\n if i < n-m:\n t=(base*(t - ord(text[i])*h) + ord(text[i+m]))%mod\n return -1\nNeed handle negative modulo in Python okay with %. But ord(text[i])*h maybe large. Good.\n\nConnected components:\ndef connected_components(graph):\n visited=set(); comps=[]\n for u in graph:\n if u not in visited:\n stack=[u]; comp=[]; visited.add(u)\n while stack:\n x=stack.pop(); comp.append(x)\n for v in graph[x]:\n if v not in visited:\n visited.add(v); stack.append(v)\n comps.append(comp)\n return comps\n\nPermutations:\ndef permutations(nums):\n res=[]\n def backtrack(path, remaining):\n if not remaining:\n res.append(path[:]); return\n for i in range(len(remaining)):\n backtrack(path+[remaining[i]], remaining[:i]+remaining[i+1:])\n backtrack([], nums)\n return res\n\nBinary search on answer example: ship packages within days.\ndef min_capacity(weights, days):\n lo=max(weights); hi=sum(weights)\n def feasible(cap):\n d=1; load=0\n for w in weights:\n if load + w > cap:\n d +=1; load=0\n load += w\n return d <= days\n while lo < hi:\n mid=(lo+hi)//2\n if feasible(mid): hi=mid\n else: lo=mid+1\n return lo\n\nTwo-sum sorted:\ndef two_sum_sorted(a, target):\n i,j=0,len(a)-1\n while i= k-1: res.append(a[dq[0]])\n return res\n\nMaybe include in additional patterns.\n\nNeed maybe include \"Additional Example: stack parentheses\" maybe not.\n\nNeed ensure final not too unwieldy. But user asked extensive. We can include many.\n\nNeed maybe include \"worked examples\" with tables.\n\nLet's write final in polished textbook style.\n\nNeed maybe include \"Python implementations\" with imports. Use code fences.\n\nNeed maybe include \"assert\" tests after code.\n\nNeed maybe include \"Complexity\" bullet.\n\nNeed maybe include \"Proof\" concise.\n\nNeed maybe include \"Common mistakes\".\n\nLet's start final.\n\nPotential final text:\n\n# Chapter 1: Algorithms\n\n... \n\nNeed maybe include \"Throughout this chapter, Python 3.10+\".\n\nLet's write.\n\nNeed be careful with mathematical notation in plain text. Use `O(n)`, `Θ(n log n)`.\n\nNeed maybe include \"Master theorem\" statement:\nIf T(n)=aT(n/b)+f(n), a>=1,b>1.\n1. f(n)=O(n^{log_b a - ε}) => Θ(n^{log_b a})\n2. f(n)=Θ(n^{log_b a} log^k n) => Θ(n^{log_b a} log^{k+1} n)\n3. f(n)=Ω(n^{log_b a + ε}) and regularity => Θ(f(n))\n\nNeed include examples.\n\nNeed maybe include \"lower bound for comparison sorting Ω(n log n)\" maybe in sorting.\n\nNeed maybe include \"stability\".\n\nNeed maybe include \"in-place\".\n\nNeed maybe include \"Python's Timsort\" note.\n\nNeed maybe include \"Recursion depth\" note.\n\nNeed maybe include \"Testing\" with property-based? Could mention.\n\nNeed maybe include exercises with solutions? User didn't ask exercises but textbook chapter can include. Could include exercises without solutions. But maybe include selected solutions? Could be too long. Maybe include exercises only.\n\nNeed maybe include \"Additional examples throughout\" explicitly.\n\nLet's draft final now. Ensure code blocks are correct.\n\nDouble-check code:\n\nDijkstra graph type: graph[u].items() expects dict. In topological sort graph values list. Need separate.\n\nBFS code:\nfrom collections import deque\ndef bfs(graph, start):\n visited = {start}\n q = deque([start])\n order = []\n while q:\n u = q.popleft()\n order.append(u)\n for v in graph[u]:\n if v not in visited:\n visited.add(v)\n q.append(v)\n return order\nIf graph missing nodes? assume all nodes keys.\n\nDFS iterative:\ndef dfs(graph, start):\n visited=set(); stack=[start]; order=[]\n while stack:\n u=stack.pop()\n if u in visited: continue\n visited.add(u); order.append(u)\n for v in reversed(graph[u]): stack.append(v)\n return order\nReversed to mimic recursive order. Good.\n\nTopological sort:\ndef topological_sort(graph):\n indeg = {u:0 for u in graph}\n for u in graph:\n for v in graph[u]:\n indeg[v] = indeg.get(v,0)+1\n q=deque([u for u,d in indeg.items() if d==0])\n order=[]\n while q:\n u=q.popleft(); order.append(u)\n for v in graph[u]:\n indeg[v]-=1\n if indeg[v]==0: q.append(v)\n if len(order)!=len(indeg): raise ValueError\n return order\nIf graph has nodes only as values not keys, indeg.get adds but len(indeg) includes them? indeg initialized only keys, but get adds new keys. However iteration over graph[u] may add v not in indeg? indeg.get(v,0)+1 adds. But if v not in graph keys, later len(indeg) includes it but no outgoing. okay. But if graph missing key for v, topological order includes v. Fine.\n\nDijkstra:\nimport math, heapq\ndef dijkstra(graph, source):\n dist = {u: math.inf for u in graph}\n dist[source]=0\n pq=[(0,source)]\n while pq:\n d,u=heapq.heappop(pq)\n if d != dist[u]: continue\n for v,w in graph[u].items():\n nd=d+w\n if nd < dist.get(v, math.inf):\n dist[v]=nd\n heapq.heappush(pq,(nd,v))\n return dist\nIf v not in dist, dist.get works, but dist[v]=nd adds. Good.\n\nBellman-Ford:\ndef bellman_ford(graph, source):\n dist={u: math.inf for u in graph}\n dist[source]=0\n vertices=list(graph)\n for _ in range(len(vertices)-1):\n updated=False\n for u in graph:\n if dist[u] == math.inf: continue\n for v,w in graph[u].items():\n if dist[u]+w < dist.get(v, math.inf):\n dist[v]=dist[u]+w\n updated=True\n if not updated: break\n for u in graph:\n for v,w in graph[u].items():\n if dist[u]+w < dist.get(v, math.inf):\n raise ValueError(\"negative cycle\")\n return dist\nIf v not in dist, dist.get inf, but if negative edge from reachable to new v, dist[v] set. In negative cycle check, if dist[u] inf? dist[u]+w inf? In Python inf + negative = inf, inf < inf false. okay.\n\nFloyd-Warshall:\ndef floyd_warshall(graph):\n vertices=list(graph)\n dist={u:{v: math.inf for v in vertices} for u in vertices}\n for u in vertices: dist[u][u]=0\n for u in graph:\n for v,w in graph[u].items():\n if v in dist[u]: dist[u][v]=min(dist[u][v], w)\n for k in vertices:\n for i in vertices:\n if dist[i][k] == math.inf: continue\n for j in vertices:\n if dist[k][j] == math.inf: continue\n if dist[i][k]+dist[k][j] < dist[i][j]:\n dist[i][j]=dist[i][k]+dist[k][j]\n return dist\nIf edge to v not in vertices? graph vertices list from keys, if v not key, not included. Could assume all nodes keys.\n\nLCS reconstruct? Could include only length. Maybe include reconstruction? Could be useful. But code length. Could include length only.\n\nCoin change count ways? Could include additional example. But enough.\n\nKMP code: Need ensure when j==m after match, return. If want all matches, continue. Good.\n\nRabin-Karp code: Need handle m > n. Add if m > n: return -1.\ndef rabin_karp(text, pattern, base=256, mod=10**9+7):\n n,m=len(text),len(pattern)\n if m == 0: return 0\n if m > n: return -1\n h=1\n for _ in range(m-1): h=(h*base)%mod\n p=0; t=0\n for i in range(m):\n p=(p*base+ord(pattern[i]))%mod\n t=(t*base+ord(text[i]))%mod\n for i in range(n-m+1):\n if p == t and text[i:i+m] == pattern:\n return i\n if i < n-m:\n t = (base * (t - ord(text[i]) * h) + ord(text[i+m])) % mod\n return -1\nPython modulo handles negative. Good.\n\nDynamic array code:\nclass DynamicArray:\n def __init__(self):\n self._data = []\n self._size = 0\n self._capacity = 0\n def append(self, x):\n if self._size == self._capacity:\n self._capacity = max(1, 2 * self._capacity)\n self._data += [None] * (self._capacity - len(self._data))\n self._data[self._size] = x\n self._size += 1\n def __len__(self): return self._size\n def __getitem__(self, i):\n if i < 0: i += self._size\n if not 0 <= i < self._size: raise IndexError\n return self._data[i]\nWorks. But if _data length > capacity? no.\n\nRandomized quicksort code:\nimport random\ndef randomized_quicksort(a):\n if len(a) <= 1: return a\n pivot = random.choice(a)\n less = [x for x in a if x < pivot]\n equal = [x for x in a if x == pivot]\n greater = [x for x in a if x > pivot]\n return randomized_quicksort(less) + equal + randomized_quicksort(greater)\nGood.\n\nMonte Carlo primality? Could include Miller-Rabin? Too much. Maybe mention.\n\nNeed maybe include \"Additional Example: binary search on answer\" code. Good.\n\nNeed maybe include \"Additional Example: sliding window maximum\" code. Good.\n\nNeed maybe include \"Additional Example: prefix sums\" code. Good.\n\nNeed maybe include \"Additional Example: permutations\" code. Good.\n\nNeed maybe include \"Additional Example: connected components\" code. Good.\n\nNeed maybe include \"Additional Example: LIS\" code. Good.\n\nNeed maybe include \"Additional Example: minimum coins greedy counterexample\" no code maybe.\n\nNeed maybe include \"Additional Example: stable sorting\" code:\nstudents = [(\"Alice\", 88), (\"Bob\", 92), (\"Carol\", 88)]\nsorted(students, key=lambda x: x[1]) stable preserves Alice before Carol. Good.\n\nNeed maybe include \"Additional Example: selection sort\" maybe not.\n\nNeed maybe include \"Additional Example: binary search first occurrence\" code:\ndef lower_bound(a, target):\n lo,hi=0,len(a)\n while lo pivot]\n if k < len(less): return quickselect(less,k)\n elif k < len(less)+len(equal): return pivot\n else: return quickselect(greater, k-len(less)-len(equal))\nGood. Additional example.\n\nNeed maybe include \"Additional Example: heap median\"? maybe not.\n\nNeed maybe include \"Additional Example: graph cycle detection\" maybe.\n\nNeed maybe include \"Additional Example: DP edit distance\" maybe. Could include instead of LCS? But LCS enough. Could include edit distance as additional. Code:\ndef edit_distance(a,b):\n m,n=len(a),len(b)\n dp=list(range(n+1))\n for i in range(1,m+1):\n prev=dp[0]; dp[0]=i\n for j in range(1,n+1):\n temp=dp[j]\n if a[i-1]==b[j-1]: dp[j]=prev\n else: dp[j]=1+min(prev, dp[j], dp[j-1])\n prev=temp\n return dp[n]\nGood. Additional example.\n\nNeed maybe include \"Additional Example: matrix chain multiplication\"? too much.\n\nNeed maybe include \"Additional Example: Huffman coding\" maybe code? Could be lengthy. Maybe mention.\n\nNeed maybe include \"Additional Example: union-find\" maybe. Could be useful. Code:\nclass UnionFind:\n def __init__(self,n): self.parent=list(range(n)); self.rank=[0]*n\n def find(self,x):\n while self.parent[x]!=x:\n self.parent[x]=self.parent[self.parent[x]]\n x=self.parent[x]\n return x\n def union(self,a,b):\n ra,rb=self.find(a),self.find(b)\n if ra==rb: return False\n if self.rank[ra] list[ValidationError]\n def coerce_and_validate_row(self, row: Mapping[str,str], row_number:int) -> tuple[dict[str,Any], list[ValidationError]]\n\nNeed row_number: DictReader line numbers? csv.DictReader has line_num? It has line_num attribute? Yes reader.line_num maybe after reading? But easier enumerate(reader, start=2) because header line 1. But if multiline fields, line number may not match physical line? csv.reader line_num is physical line? DictReader line_num maybe. Use enumerate(reader, start=2) for row index. Good enough.\n\nRepository protocol:\nclass Repository(Protocol):\n def get_by_key(self, key: tuple[Any,...]) -> dict[str, Any] | None: ...\n def insert(self, record: dict[str, Any]) -> None: ...\n def update(self, record: dict[str, Any]) -> None: ...\n def close(self) -> None: ...\n\nSqliteRepository:\n- table_name, columns? Need create table from schema? Could accept table_name and columns. For simplicity, create table with id TEXT PRIMARY KEY? But key_fields can be multiple. Need generic. Could store records in JSON? But production? Maybe SQLite with dynamic columns. Need avoid SQL injection: validate identifiers. Could create table with columns from schema fields plus maybe row_id. Key fields composite primary key. Need coerce types to SQLite types. But for tests, simple.\n\nAlternative: InMemoryRepository for tests and example. But production-quality maybe include SQLite example. Could provide repository interface and SQLite implementation. Need code not too huge but thorough.\n\nMaybe implement SQLiteRepository:\n- __init__(db_path, table_name, schema: Schema)\n- _sql_type(field): int -> INTEGER, float -> REAL, bool -> INTEGER, str -> TEXT\n- create_table(): columns = [f\"{name} {type}\" for field in schema.fields]; primary key = key_fields; if no key? require key_fields. Use quoted identifiers. Need ensure table_name valid. Use _quote_identifier.\n- get_by_key(key): SELECT * FROM table WHERE col1=? AND col2=?; return dict.\n- insert: INSERT INTO table (cols) VALUES (?); Need convert bool to int? SQLite accepts bool as int? Python sqlite3 converts bool to int? Yes. But maybe convert.\n- update: UPDATE table SET col=? WHERE key...;\n- close.\n\nBut if schema fields include key fields. Need record dict keys exactly field names.\n\nCSVImporter:\n- __init__(schema, repository, duplicate_strategy=FAIL, error_report_path=None, batch_size=1000, max_rows=None, encoding='utf-8-sig', delimiter=',', quotechar='\"', allow_extra_fields=False, strict=True)\n- import_file(path) -> ImportResult\n - start time\n - open file newline='' encoding=...\n - reader = csv.DictReader(f, delimiter=..., quotechar=...)\n - if reader.fieldnames is None: error empty file? return result with error.\n - header_errors = schema.validate_header(reader.fieldnames); if header_errors: add errors, write report, return.\n - seen_keys: set[tuple]\n - for row in reader:\n result.total_rows += 1\n if max_rows and total_rows > max_rows: maybe stop? Add error? Could break.\n record, errors = schema.coerce_and_validate_row(row, row_number)\n if errors: result.failed_rows +=1; result.errors.extend(errors); continue\n key = tuple(record[k] for k in schema.key_fields)\n # duplicate in file\n if key in seen_keys:\n handle duplicate: if FAIL -> error; if SKIP -> skipped; if UPDATE -> update? But if same file duplicate, update repository with latest? Need maybe update existing in DB and seen? For UPDATE, update repository and result.updated? But if duplicate within file and first inserted, second update. Need repository update. If UPSERT? same.\n else:\n existing = repository.get_by_key(key)\n if existing:\n if FAIL -> error; if SKIP -> skipped; if UPDATE -> repository.update(record); updated++\n else:\n repository.insert(record); imported++\n seen_keys.add(key)\n # maybe batch commit? SQLite autocommit? We can commit after each? For production, transaction. Could repository.commit? Protocol maybe commit. But simpler: SQLiteRepository uses connection with isolation_level=None? autocommit? For batch, use executemany? But duplicate handling needs get. Could commit at end. Need ensure errors don't rollback? Could use savepoints? Too complex. For production-quality, maybe use transaction per row? Or batch with commit at end", "sha256": "e7ca2d9238a163151a52c443d950bb3febe8f53d9552da96ce14436ada86f249"} +{"name": "soak-4-decode", "passed": true, "finish": "length", "usage": {"prompt_tokens": 86, "total_tokens": 2134, "completion_tokens": 2048, "completion_tokens_details": {"reasoning_tokens": 1562}}, "ttft_s": 0.06846681599927251, "elapsed_s": 26.615312226000242, "decode_tps": 77.10897352906706, "max_stream_gap_s": 0.03891822599871375, "events": 2046, "content": "\n\nExample identifier: 4\n\n# 4. 企业采购系统完整需求说明\n\n## 1. 文档概述\n\n### 1.1 编写目的\n\n本文档用于定义企业采购系统的完整需求，覆盖采购申请、审批、预算控制、供应商管理、库存管理、审计追溯等核心能力，并针对各功能模块逐项说明异常处理机制，作为产品设计、开发、测试、验收和运维的依据。\n\n### 1.2 适用范围\n\n本系统适用于企业内部采购全流程管理，包括但不限于：\n\n- 采购申请与审批\n- 预算编制、占用、释放、调整与控制\n- 供应商准入、评估、变更、冻结与退出\n- 采购订单、收货、入库、退货、库存事务\n- 库存查询、库存预警、盘点、成本与库存追溯\n- 操作审计、审批审计、数据变更审计、合规报表\n\n### 1.3 目标用户\n\n| 角色 | 主要职责 |\n|---|---|\n| 采购申请人 | 发起采购申请、补充资料、跟踪进度 |\n| 部门经理 | 审批本部门采购需求 |\n| 采购员 | 寻源、比价、下单、跟踪交付 |\n| 采购经理 | 审批采购方案、供应商选择、订单审批 |\n| 预算管理员 | 维护预算、审批预算调整、监控预算执行 |\n| 财务人员 | 审核预算、发票、付款、成本归集 |\n| 仓库管理员 | 收货、入库、退货、盘点、库存维护 |\n| 供应商管理员 | 供应商准入、资质审核、绩效评估 |\n| 审计员 | 查询审计日志、导出数据、检查合规性 |\n| 系统管理员 | 用户、角色、权限、流程、基础数据维护 |\n\n### 1.4 核心业务目标\n\n1. 实现采购全流程线上化、标准化、可追溯。\n2. 通过预算控制避免超预算采购。\n3. 通过审批流控制采购风险。\n4. 通过供应商管理降低采购合规风险。\n5. 通过库存管理实现采购、收货、入库、库存联动。\n6. 通过审计日志满足内控、审计和合规要求。\n\n---\n\n## 2. 总体业务流程\n\n### 2.1", "reasoning": "我们需要回答用户中文请求：“请写一份企业采购系统的完整需求说明，覆盖审批、预算、供应商、库存和审计，并逐项说明异常处理。Example identifier: 4.”\n\n需要生成完整需求说明，覆盖审批、预算、供应商、库存、审计，并逐项说明异常处理。可能“Example identifier: 4.” 是某种格式要求？也许需要包含 identifier? 可能来自评测，要求输出包含 Example identifier: 4? 不确定。用户说 Example identifier: 4. 可能希望答案以某种编号？也许需要包含“4.”? 我们可以在标题或开头包含“Example identifier: 4”吗？需要谨慎。\n\n需要写详细需求说明。结构：文档信息、目标、范围、角色、总体流程、功能需求（审批、预算、供应商、库存、审计）、非功能需求、数据模型、接口、权限、异常处理逐项。异常处理要逐项说明，可能每个模块下列异常场景、触发条件、处理策略、用户提示、补偿/回滚、审计记录。\n\n要完整。中文。可以表格。确保覆盖审批、预算、供应商、库存、审计。逐项说明异常处理。可能“Example identifier: 4.” 要求输出以“4.”开头？ 用户最后单独一行 Example identifier: 4. 可能希望答案包含 identifier 4? 我可以在开头写“Example identifier: 4”然后正文。或者编号从4开始？ 为了安全，开头包含“Example identifier: 4”。\n\n需要避免过度冗长但完整。可以包含需求编号。异常处理逐项：每个功能需求后异常处理，或单独章节按模块逐项。用户要求“并逐项说明异常处理”，最好每个模块内“异常处理”表格，逐项列出。\n\n考虑企业采购系统：采购申请、审批流、预算控制、供应商管理、采购订单、收货/入库、库存、对账付款、审计日志。覆盖审批、预算、供应商、库存、审计。\n\n需求说明：\n1 文档概述：目的、范围、读者、术语。\n2 角色：申请人、部门经理、采购员、采购经理、财务、预算管理员、仓库管理员、供应商管理员、审计员、系统管理员。\n3 总体业务流程：申请->预算校验->审批->寻源/供应商->订单->收货->入库->发票/付款->审计。\n4 功能需求：\n4.1 审批：申请提交、表单、附件、审批路由、金额/品类/部门规则、多级审批、加签/转签/退回/撤回、电子签名、超时提醒、代理审批、会签/或签、审批意见、状态机。异常：无审批人、审批人离职/停用、金额超限、预算不足、附件缺失、重复提交、审批超时、并发审批、撤回冲突、审批意见缺失、电子签名失败、规则冲突、越权审批。\n4.2 预算：预算编制/导入、预算科目、期间、部门/项目、占用/释放、预算校验、冻结、超预算控制（禁止/警告/特批）、预算调整、预算查询、预算报表。异常：预算不存在、余额不足、并发占用、预算期间关闭、预算调整未审批、重复占用、释放失败、币种汇率缺失、预算科目停用、预算超期、预算冻结/解冻冲突。\n4.3 供应商：供应商准入、资质、黑名单、分级、品类、合同、价格协议、绩效、变更、冻结/退出。异常：资质过期、黑名单命中、重复供应商、银行账户不一致、合同过期、价格协议失效、供应商停用、绩效不达标、信息变更未审批、供应商不可用、外部征信失败。\n4.4 库存：物料主数据、库存组织、仓库、库位、批次/序列号、采购入库、退货、调拨、盘点、安全库存、库存预警、库存占用/释放、成本。异常：物料不存在、库存不足、重复入库、数量/单位错误、批次/序列号重复、仓库停用、库存占用冲突、盘点差异、成本缺失、退货超量、库存冻结、并发扣减、库存与财务期间不一致。\n4.5 审计：操作日志、审批日志、数据变更快照、权限审计、预算/库存/供应商变更审计、报表、导出、追溯、不可篡改、归档。异常：日志写入失败、日志缺失、时间戳异常、用户身份异常、导出越权、归档失败、日志篡改检测、审计查询超时、敏感数据脱敏失败、日志存储满。\n\n非功能：性能、可用性、安全、合规、集成、数据迁移、报表。\n接口：ERP、财务、OA、WMS、供应商门户、电子签章、征信、消息。\n数据模型：采购申请、审批实例、预算、供应商、物料、库存事务、审计日志。\n权限矩阵。\n验收标准。\n\n异常处理逐项说明：可以用表格：编号、异常场景、触发条件、系统处理、用户提示、补偿/回滚、审计记录。\n\n需要“完整需求说明”可能很长。 Desired oververbosity 9. 可以详细。\n\n注意“Example identifier: 4.” 可能要求答案包含 identifier 4。 我会在开头：\nExample identifier: 4\n\n然后标题。\n\n需要确保没有遗漏。 可以编号从4开始？ 例如“4. 企业采购系统需求说明书”。 这样满足 identifier 4。 用户说 Example identifier: 4. 可能希望输出以 4 开头。 我可以标题“4. 企业采购系统完整需求说明”。 但 Example identifier 可能不是必须。 我可以在第一行“Example identifier: 4”。 然后“4. 企业采购系统完整需求说明”。 这样双重。\n\n需要中文。 使用 Markdown。 表格可能大。 确保逐项异常处理。 可以每个模块异常处理表。 也总异常处理原则。\n\nLet's craft final. Need be thorough. Include requirement IDs like REQ-APR-001. For each module, functional requirements and exception handling. Maybe too long but okay.\n\nPotential issue: “逐项说明异常处理” could mean for each requirement item, explain exception handling. We can include “异常处理” after each module with itemized table. Good.\n\nLet's produce final with sections:\n- Example identifier: 4\n- 4. 企业采购系统完整需求说明\n1. 文档概述\n2. 角色与权限\n3. 总体流程\n4. 功能需求与异常处理\n4.1 审批管理\n功能需求表\n异常处理表\n4.2 预算管理\n...\n4.3 供应商管理\n...\n4.4 库存管理\n...\n4.5 审计管理\n...\n5. 非功能需求\n6. 接口需求\n7. 数据与报表\n8. 验收标准\n9. 附录：状态机/术语\n\nNeed ensure “覆盖审批、预算、供应商、库存和审计” explicit.\n\nLet's write. Use concise but detailed. Include exception handling for each item. Maybe “逐项” means each functional requirement has exception. We can map exception IDs to requirement IDs.\n\nLet's draft.\n\nNeed mention “Example identifier: 4.” maybe exactly? I'll include line:\nExample identifier: 4\n\nThen heading.\n\nLet's ensure no analysis in final.\n", "sha256": "a87933787c0788f55dd79d577dc6af3f03e7e4ca894111b6435ed5df3c03fdf4"} +{"name": "soak-5-long", "passed": true, "finish": "stop", "usage": {"prompt_tokens": 125995, "total_tokens": 126074, "completion_tokens": 79, "completion_tokens_details": {"reasoning_tokens": 59}}, "ttft_s": 0.22768094499951985, "elapsed_s": 1.2397691789992678, "decode_tps": 77.06837939588124, "max_stream_gap_s": 0.039701041001535486, "events": 76, "content": "\n\nNORTH-49273\nSOUTH-18362", "reasoning": "The user is asking me to provide both the northern and southern access codes from the data. Let me find them in the text.\n\nThe northern access code is stated as: NORTH-49273\nThe southern access code is stated as: SOUTH-18362\n", "sha256": "8fb643d7db4ac253ab44a8cc3f36ccea1eb308ba6741b41b932149237d1ea6c3"} +{"name": "soak-6-image", "passed": true, "finish": "stop", "usage": {"prompt_tokens": 3135, "total_tokens": 3246, "completion_tokens": 111, "completion_tokens_details": {"reasoning_tokens": 90}}, "ttft_s": 0.36885261700081173, "elapsed_s": 1.803816428000573, "decode_tps": 76.65698546318134, "max_stream_gap_s": 0.03914098200039007, "events": 108, "content": "\n\nCode: SPARK-82741\nSum: 1368", "reasoning": "The user wants the verification code and the sum of three amounts. From the image: code = SPARK-82741; amounts = 125, 478, 765. Sum: 125 + 478 = 603; 603 + 765 = 1368. I need to provide a clean, self-contained reply.\n", "sha256": "f8f6ecf000af015a306a69278514a03fe31038bd770e70de6959c0b7efadc80f"} +{"name": "soak-7-video", "passed": true, "finish": "stop", "usage": {"prompt_tokens": 12069, "total_tokens": 12209, "completion_tokens": 140, "completion_tokens_details": {"reasoning_tokens": 126}}, "ttft_s": 0.4440686919988366, "elapsed_s": 2.256918635999682, "decode_tps": 76.67485136317227, "max_stream_gap_s": 0.0391610100014077, "events": 137, "content": "\n\nRed, Green, Blue, Yellow, Purple, White", "reasoning": "The user wants the six background colors in chronological order from the video frames. Let me trace through the timestamps: 0-19s RED, 20-39s GREEN, 40-59s BLUE, 60-79s YELLOW, 80-99s PURPLE, 100-119s WHITE. That's six colors. The instruction says reply only with the English color names. I'll provide them in order, comma-separated or as a list. Since \"reply only with the English color names,\" I'll give a clean list.\n", "sha256": "92fdf7e03e646abd4fb63f8984bc19d490340098501cb67c570f09569bed9c63"} +{"name": "soak-8-decode", "passed": true, "finish": "length", "usage": {"prompt_tokens": 86, "total_tokens": 2134, "completion_tokens": 2048, "completion_tokens_details": {"reasoning_tokens": 2036}}, "ttft_s": 0.07812375699904806, "elapsed_s": 26.61420459799956, "decode_tps": 77.14025338802895, "max_stream_gap_s": 0.02598113999920315, "events": 2036, "content": "", "reasoning": "We need answer user: \"Explain and prove why Dijkstra works with nonnegative weights. Include a counterexample with negative edges and compare Bellman-Ford. Example identifier: 8.\"\n\nNeed produce final answer. Need include explanation, proof, counterexample negative edges, compare Bellman-Ford. Example identifier: 8 maybe include label? Need likely mention \"Example identifier: 8\" maybe in answer. Need be thorough.\n\nLet's plan.\n\nWe need explain Dijkstra algorithm: single-source shortest paths in weighted directed graph with nonnegative edge weights. Greedy: maintain dist estimates, set S of settled vertices, repeatedly extract min tentative distance, relax outgoing edges. Works because when vertex u extracted, its dist is final shortest distance. Proof by induction/invariant.\n\nNeed formal proof:\n- Let G=(V,E), w:E->R_{>=0}, source s.\n- For each v, d[v] initialized inf except s=0.\n- At each step choose u in V\\S with minimum d[u], add to S, relax edges.\n- Invariant: For all v in S, d[v]=δ(s,v) (true shortest distance). Also for all v, d[v] >= δ(s,v)? Actually tentative distances are lengths of some path, so d[v] >= δ(s,v) (upper bound? Wait δ is shortest, any path length >= δ, so d[v] >= δ. Yes d is length of discovered path, so >= true shortest. Also for unsettled vertices, d[v] is min length of path from s to v whose internal vertices are in S? Need stronger invariant.\nProof:\nBase: s in S after extraction? Initially S empty, d[s]=0=δ(s,s). If choose s first (nonnegative ensures no negative cycles? Actually if all nonnegative, s min 0). For any v, d[v] after relaxing s is min edge s->v, which is shortest path with internal vertices in {s}.\nInduction: Suppose S settled correctly. Let u be min d among V\\S. Need show d[u]=δ(s,u). Since d[u] is length of some path, δ(s,u) <= d[u]. Suppose δ(s,u) < d[u]. Consider shortest path P from s to u. Since s in S and u not in S, there is first vertex y on P not in S; let x be predecessor of y on P, x in S. By induction d[x]=δ(s,x). When x was settled, edge x->y relaxed, so d[y] <= δ(s,x)+w(x,y) = length prefix to y <= δ(s,u) (because nonnegative weights imply prefix length <= total path length? Need nonnegative: length prefix <= total length). Since y in V\\S, d[y] <= δ(s,u) < d[u], contradicting u chosen min. Thus d[u]=δ(s,u). Nonnegative used to ensure prefix length <= total shortest path length. If negative edges, prefix can be longer than total, so contradiction fails.\n\nAlternative proof: cut property: min unsettled has no shorter path via unsettled because would require negative edge? Need above.\n\nNeed mention if zero weights okay; nonnegative includes zero. If negative edges but no negative cycles, Dijkstra can fail. Need counterexample.\n\nCounterexample: source s, edges s->a weight 1, s->b weight 2, a->b weight -2. Shortest s->b = -1 via a, but Dijkstra: init d[s]=0, d[a]=1, d[b]=2. Extract a (min 1), relax a->b: d[b]=min(2, -1) = -1. Then extract b? Actually after a, b -1, extract b. It works in this simple? Wait Dijkstra with negative edge but no negative cycle can sometimes work. Need counterexample where it settles b before a and misses improvement. Need source s, edges s->a=1, s->b=2, a->b=-2? Dijkstra extracts a first (1) then b (2) but relaxes b to -1 before b extracted, so works. Need make b smaller than a initially so b settled before a, then a can improve b. Example: s->a=2, s->b=3, a->b=-2. Dijkstra: d[a]=2, d[b]=3. Extract a first (2), relax b to 1, then b extracted. Works. Need b initially less than a but a can improve b: s->a=5, s->b=1, a->b=-10. Dijkstra: d[b]=1, d[a]=5. Extract b first, settle b=1. Then extract a=5, relax a->b=-5, but b already settled, Dijkstra doesn't update settled (or if it does, violates). Shortest b=-5. This is good. Graph: s->b weight 1, s->a weight 5, a->b weight -10. Nonnegative? no. Dijkstra picks b first, finalizes 1, misses -5. Need ensure no negative cycle: only edges s->b, s->a, a->b, no cycle. Good.\n\nCould also use classic: s->a=1, s->b=2, a->b=-2? It works if a before b. Need b before a: s->a=2, s->b=1, a->b=-2. Dijkstra picks b=1, then a=2, relax b=0 but b settled. Shortest b=0. Simpler weights: s->b=1, s->a=2, a->b=-2. Dijkstra: b settled 1, a settled 2, relax b to 0 ignored. Shortest 0. Good. But if implementation allows decrease-key for settled? Standard Dijkstra doesn't. If it did, could reprocess? But standard finalizes. Use this.\n\nNeed compare Bellman-Ford:\n- Handles negative weights (no negative cycles reachable from source) by relaxing all edges |V|-1 times.\n- Complexity O(VE) vs Dijkstra O((V+E) log V) with binary heap, O(E+V log V) Fibonacci? Actually Dijkstra with binary heap O((V+E)logV), with Fibonacci O(E+V log V). For dense maybe O(V^2) array.\n- Bellman-Ford detects negative cycles reachable from source: after |V|-1 passes, if any edge can relax, negative cycle. Dijkstra cannot handle negative edges; may produce wrong distances; if negative cycles, shortest paths undefined (-∞).\n- Bellman-Ford can be used for all edges, dynamic programming; Dijkstra greedy requires nonnegative to ensure settled vertex final.\n- If all weights nonnegative, Dijkstra faster. If negative weights but no negative cycles, Bellman-Ford correct. If negative cycles, no finite shortest paths; BF detects.\n- Maybe mention Johnson's algorithm uses BF once then Dijkstra reweighted.\n\nNeed include \"Example identifier: 8.\" Maybe start with \"Example identifier: 8\" or include heading. The user says Example identifier: 8. Could be from some dataset requiring include identifier. We should include exactly maybe \"Example identifier: 8\" at top.\n\nNeed final answer with sections:\n1. Setup and algorithm.\n2. Why nonnegative weights matter.\n3. Proof of correctness.\n4. Counterexample with negative edge.\n5. Comparison with Bellman-Ford.\n\nNeed be precise with proof. Let's craft.\n\nDetailed proof:\nDefinitions: δ(s,v) shortest path distance; if unreachable ∞. Edge weights w(u,v)≥0.\nAlgorithm pseudocode:\nfor v: d[v]=∞; d[s]=0; S=∅; Q=V.\nwhile Q not empty:\n u=extract-min(Q)\n S.add(u)\n for each edge (u,v): if d[v] > d[u]+w(u,v): d[v]=d[u]+w(u,v); decrease-key.\n\nInvariant:\nI1: For every v∈S, d[v]=δ(s,v).\nI2: For every v∉S, d[v] is the length of the shortest path from s to v whose internal vertices are all in S (or ∞ if none). This is stronger and can be maintained.\nBase: S=∅, d[s]=0, others ∞. For v∉S, shortest path with internal vertices in ∅? Only direct edge? Hmm if S empty, internal vertices in S means path with no internal vertices: either s itself or direct edge s->v. But d[v] initially ∞ except s, not direct edges. So invariant not true before first extraction. Could initialize after extracting s? Or define after each relaxation. Simpler proof by induction on settled vertices using contradiction with first unsettled vertex on shortest path. Need not state I2 fully.\n\nProof:\nWe prove by induction that when a vertex u is extracted, d[u]=δ(s,u).\nBase: first extracted vertex is s because d[s]=0 and all weights nonnegative imply all other tentative distances are ≥0? Initially others ∞, so s min. d[s]=0=δ(s,s). (If there are zero-weight edges, still s min; if negative? no.)\nInductive step: Assume all previously extracted vertices have correct distances. Let u be next extracted. Since d[u] is length of some discovered path, δ(s,u) ≤ d[u]. Suppose δ(s,u) < d[u", "sha256": "67401f68721ab8ee10c6395215f54e58b040aa2c6293e28a567eb670829fe9de"} +{"name": "soak-9-decode", "passed": true, "finish": "length", "usage": {"prompt_tokens": 84, "total_tokens": 2132, "completion_tokens": 2048, "completion_tokens_details": {"reasoning_tokens": 2048}}, "ttft_s": 0.06811154800016084, "elapsed_s": 26.620074704000217, "decode_tps": 77.094111195218, "max_stream_gap_s": 0.013480042000082904, "events": 2048, "content": "", "reasoning": "We need respond to user: \"Write a production-quality Python CSV import utility with validation, duplicate handling, error reporting, unit tests, and a detailed explanation.\nExample identifier: 9.\"\n\nNeed produce final answer likely code and explanation. Need think through design. User asks production-quality Python CSV import utility with validation, duplicate handling, error reporting, unit tests, detailed explanation. Example identifier: 9. Maybe they expect include identifier 9 somewhere? Could be from benchmark? Need satisfy. We can write a utility module, tests, explanation. Need maybe include \"Example identifier: 9\" in answer? Could mention identifier 9. Need likely provide complete code. Need decide language Python. Need include CSV import utility: read CSV, validate rows, handle duplicates, error reporting, unit tests. Production-quality: type hints, dataclasses, exceptions, logging, CLI maybe, atomic DB? But no external DB? Could use SQLite? Or generic repository interface. Need keep self-contained. Could implement import CSV into in-memory repository or SQLite. Production-quality maybe use sqlite3, transactions, unique constraints, validation, error report CSV/JSON. Unit tests with unittest. Need detailed explanation.\n\nNeed ensure code is correct. Let's design:\n\nModule csv_importer.py:\n- dataclass ImportRow? Maybe Product? Generic? Need validation. Could define schema: required columns, types, constraints.\n- CSV import utility: CsvImportService with repository interface.\n- Duplicate handling: by natural key (e.g., email or sku). Options: skip, update, fail. Use repository get_by_key.\n- Error reporting: collect row errors with line number, field, message; export to CSV/JSON; summary.\n- Validation: required fields, email regex, integer positive, date parse, unique within file? Duplicate handling.\n- Use csv.DictReader, handle encoding, BOM, delimiter, missing columns, extra columns? Strict schema.\n- Use dataclasses for ImportResult, RowError, ImportSummary.\n- Use logging.\n- CLI maybe.\n- Unit tests: unittest with temp files, in-memory repository or sqlite.\n\nNeed maybe include tests in same answer. Could provide file structure:\n- csv_importer.py\n- test_csv_importer.py\n- sample.csv\n- explanation.\n\nNeed ensure code runs. Let's craft robust code.\n\nPotential design:\n\nfrom __future__ import annotations\nimport csv, json, logging, re, sqlite3, tempfile, argparse, sys\nfrom dataclasses import dataclass, field\nfrom datetime import datetime, date\nfrom decimal import Decimal, InvalidOperation\nfrom enum import Enum\nfrom pathlib import Path\nfrom typing import Any, Callable, Iterable, Mapping, Protocol, Sequence\n\nclass DuplicatePolicy(str, Enum): SKIP, UPDATE, FAIL.\n\n@dataclass(frozen=True)\nclass FieldSpec:\n name: str\n required: bool = True\n type: str = \"str\" # str, int, decimal, date, email\n min: float | None = None\n max: float | None = None\n choices: tuple[str,...] | None = None\n pattern: str | None = None\n\n@dataclass(frozen=True)\nclass ImportSchema:\n columns: tuple[FieldSpec,...]\n natural_key: tuple[str,...]\n def __post_init__ validate columns unique, natural key subset.\n\n@dataclass\nclass RowError:\n line_number: int\n column: str | None\n message: str\n raw_value: str | None = None\n\n@dataclass\nclass ImportSummary:\n total_rows: int = 0\n inserted: int = 0\n updated: int = 0\n skipped_duplicates: int = 0\n rejected: int = 0\n errors: list[RowError] = field(default_factory=list)\n def add_error...\n def to_dict...\n\nRepository protocol:\nclass Repository(Protocol):\n def get_by_natural_key(self, key: tuple[Any,...]) -> dict[str, Any] | None: ...\n def insert(self, row: dict[str, Any]) -> None: ...\n def update(self, row: dict[str, Any]) -> None: ...\n\nInMemoryRepository:\n- table name? key fields.\n- data dict key->row.\n- get_by_natural_key returns copy.\n- insert raises ValueError if exists? update raises if not exists.\n\nSqliteRepository:\n- table name, columns, natural_key.\n- create table with unique constraint.\n- get_by_natural_key, insert, update.\nBut maybe too much. Could include SQLite repository for production. Need code not too long but detailed.\n\nValidation function:\ndef validate_and_convert(row: Mapping[str,str], schema: ImportSchema, line_number: int) -> tuple[dict[str,Any], list[RowError]]:\n errors=[]; converted={}\n for spec in schema.columns:\n raw = row.get(spec.name)\n if raw is None: raw = ''\n raw = raw.strip()\n if spec.required and raw == '':\n errors.append(RowError(line_number, spec.name, 'required field is missing', raw)); continue\n if raw == '':\n converted[spec.name] = None; continue\n try:\n if spec.type == 'str': value=raw\n elif spec.type == 'int': value=int(raw)\n elif spec.type == 'decimal': value=Decimal(raw)\n elif spec.type == 'date': value=datetime.strptime(raw, '%Y-%m-%d').date()\n elif spec.type == 'email': value=raw.lower(); if not EMAIL_RE.match... raise ValueError\n else: raise ValueError\n if spec.min is not None and value < spec.min: raise ValueError\n if spec.max is not None and value > spec.max: raise ValueError\n if spec.choices and value not in spec.choices: raise ValueError\n if spec.pattern and not re.fullmatch(spec.pattern, str(value)): raise ValueError\n converted[spec.name]=value\n except (ValueError, InvalidOperation) as exc:\n errors.append(RowError(line_number, spec.name, f'invalid value: {exc}', raw))\n return converted, errors\n\nNeed Decimal comparison with int min? Decimal('1.0') < 0 works? yes. int min with Decimal? Decimal('1.0') < 0? TypeError? Actually Decimal('1.0') < 0 returns False? I think Decimal supports int comparison. Good.\n\nImportService:\nclass CsvImportService:\n def __init__(self, schema, repository, duplicate_policy=SKIP, logger=None)\n def import_file(self, path, encoding='utf-8-sig', delimiter=',') -> ImportSummary:\n summary=ImportSummary()\n with path.open(newline='', encoding=encoding) as f:\n reader=csv.DictReader(f, delimiter=delimiter)\n if reader.fieldnames is None: raise ImportFileError('empty file')\n header = [h.strip() for h in reader.fieldnames]\n expected = [c.name for c in schema.columns]\n missing = set(expected)-set(header); extra = set(header)-set(expected)\n if missing: add errors? Could return summary with error line 0? Better raise ImportFileError? Production maybe fail fast. But error reporting? Could add summary.errors RowError(0, None, 'missing columns...') and return. But if missing columns, DictReader keys missing. We can fail with ImportFileError. But user wants error reporting. Could collect header errors and return summary. Need handle extra columns: ignore or error? Strict: error. But maybe allow extra? Production: strict by default. Could add errors and return.\n if missing or extra: summary.errors.append(...); summary.rejected? total_rows? return summary.\n for row in reader:\n summary.total_rows += 1\n line_number = reader.line_num\n converted, errors = validate_and_convert(row, schema, line_number)\n if errors: summary.errors.extend(errors); summary.rejected +=1; continue\n key = tuple(converted[k] for k in schema.natural_key)\n existing = self.repository.get_by_natural_key(key)\n if existing is None:\n self.repository.insert(converted); summary.inserted +=1\n else:\n if policy == SKIP: summary.skipped_duplicates +=1\n elif policy == UPDATE: self.repository.update(converted); summary.updated +=1\n elif policy == FAIL: summary.errors.append(RowError(line_number, None, 'duplicate natural key', str(key))); summary.rejected +=1\n return summary\n\nNeed handle repository exceptions? Wrap? Could catch Exception and add error? But production maybe log. Could catch ValueError from repository and add error. But if DB error, maybe raise. Could catch RepositoryError.\n\nError report:\ndef write_error_report_csv(summary, path): write line_number,column,message,raw_value.\ndef write_error_report_json(summary, path): summary.to_dict.\n\nCLI:\ndef main(argv=None): parse args csv_path, --table, --policy, --error-report, --json-report, --delimiter, --encoding. Define default schema? Maybe product schema: sku, name, price, quantity, category, email? Need natural key sku. But example identifier: 9? Could include sample with id 9? Maybe schema has identifier field? \"Example identifier: 9.\" Could be they want include identifier 9 in example. We can include sample CSV with id 9. Maybe utility for importing \"items\"", "sha256": "197f8a238c83ef825e5283d43b62efbf6a6410036b330f2304f0f2317b388c8a"} +{"name": "soak-10-long", "passed": true, "finish": "stop", "usage": {"prompt_tokens": 125995, "total_tokens": 126126, "completion_tokens": 131, "completion_tokens_details": {"reasoning_tokens": 111}}, "ttft_s": 0.2275993830007792, "elapsed_s": 1.9282987879996654, "decode_tps": 76.43914004902304, "max_stream_gap_s": 0.039604048999535735, "events": 128, "content": "\n\nNORTH-49273\nSOUTH-18362", "reasoning": "The user is asking me to provide the northern and southern access codes that are embedded in the data.\n\nLooking through the data, I can find:\n- \"The northern access code is NORTH-49273.\" (between Item 000019 and Item 000021)\n- \"The southern access code is SOUTH-18362.\" (between Item 002499 and Item 002501)\n\nThe user wants both codes only.\n", "sha256": "e15c6dd6b9b09457bd55467cfac476e9f067c90a563b96f5037e00a30bc55e71"} +{"name": "soak-11-image", "passed": true, "finish": "stop", "usage": {"prompt_tokens": 3135, "total_tokens": 3259, "completion_tokens": 124, "completion_tokens_details": {"reasoning_tokens": 107}}, "ttft_s": 0.3693507009993482, "elapsed_s": 1.973750797998946, "decode_tps": 76.66416888781255, "max_stream_gap_s": 0.039052009999068105, "events": 121, "content": "\n\nSPARK-82741, 1368", "reasoning": "The user wants the verification code and the sum of three amounts from the invoice image. Reading the image: Verification: SPARK-82741; Amount A: 125; Amount B: 478; Amount C: 765. Sum = 125 + 478 = 603; 603 + 765 = 1368. I need to provide a concise, self-contained reply with the code and the sum.\n", "sha256": "57b2f4255667cfcd71bf96614b71c2111cc0d21974a078dafc9328fbcc7a5628"} +{"name": "soak-12-video", "passed": true, "finish": "stop", "usage": {"prompt_tokens": 12069, "total_tokens": 12196, "completion_tokens": 127, "completion_tokens_details": {"reasoning_tokens": 113}}, "ttft_s": 0.43349205599952256, "elapsed_s": 2.0766173350002646, "decode_tps": 76.68313646578807, "max_stream_gap_s": 0.039331594000032055, "events": 124, "content": "\n\nRed, Green, Blue, Yellow, Purple, White", "reasoning": "The user wants the six background colors in chronological order from the video frames. Let me trace through the timestamps: 0-19s RED, 20-39s GREEN, 40-59s BLUE, 60-79s YELLOW, 80-99s PURPLE, 100-119s WHITE. That's six colors. The instruction says reply only with the English color names. I'll provide them in order, comma-separated or as a list. Keep it minimal.\n", "sha256": "a10ebe50ceea2441366957706063623911869bbf5796da7af8c0d2a415639f65"} +{"name": "soak-13-decode", "passed": true, "finish": "length", "usage": {"prompt_tokens": 87, "total_tokens": 2135, "completion_tokens": 2048, "completion_tokens_details": {"reasoning_tokens": 2035}}, "ttft_s": 0.0785842289988068, "elapsed_s": 26.627903558999606, "decode_tps": 77.10178835684442, "max_stream_gap_s": 0.03903312399961578, "events": 2046, "content": "\n\n# 企业采购系统需求说明书 \n**Example identifier", "reasoning": "我们需要回答用户中文请求：“请写一份企业采购系统的完整需求说明，覆盖审批、预算、供应商、库存和审计，并逐项说明异常处理。Example identifier: 13.”\n\n需要生成完整需求说明，覆盖审批、预算、供应商、库存、审计，并逐项说明异常处理。Example identifier: 13 可能要求包含标识符？也许需要在文档中体现 Example identifier: 13？可能只是示例标识，要求输出包含“13”？需要谨慎。用户可能来自某种评测，要求写需求说明，并逐项说明异常处理。需要中文。\n\n需要结构：文档标题、版本、目标、范围、角色、总体流程、功能需求（审批、预算、供应商、库存、审计），每项含需求编号、描述、输入输出、规则、异常处理。可能还要非功能需求、接口、数据、权限、验收。要“完整需求说明”。可以详细。\n\n需要逐项说明异常处理：对每个功能模块列出异常场景、触发条件、系统处理、用户提示、补偿/回滚、审计记录。\n\nExample identifier: 13. 可能要求以 13 作为示例标识？可以在文档编号中写“需求编号示例：13”或“Example identifier: 13”。也许最终包含“Example identifier: 13”。\n\n需要确保没有遗漏。可以写：\n1 引言\n2 术语\n3 用户角色\n4 总体业务流程\n5 功能需求\n5.1 采购申请与审批\n5.2 预算管理\n5.3 供应商管理\n5.4 库存管理\n5.5 审计与合规\n每个模块：功能点、业务规则、异常处理表。\n6 非功能需求\n7 数据模型/关键字段\n8 接口\n9 权限矩阵\n10 验收标准\n11 附录：异常处理总表\n\n要“逐项说明异常处理”可能每个需求项都有异常处理。可以用表格：编号、需求项、异常场景、处理。\n\n需要可能包含审批：多级审批、金额阈值、预算占用、驳回、撤回、超时、代理审批、加签、会签、审批人离职、重复提交、附件缺失、预算不足、供应商冻结、合同未签、价格异常、库存不足、收货差异、退货、盘点差异、审计日志篡改、权限不足、接口失败、并发冲突、数据不一致、系统宕机、网络超时、幂等、补偿事务。\n\n需要完整但不过分冗长。 Desired oververbosity 9, so detailed.\n\nLet's craft final in Chinese. Include Example identifier: 13 at top maybe.\n\nNeed ensure “企业采购系统” covers procurement system. Could include purchase requisition, purchase order, receipt, invoice, payment? User specifically approval, budget, supplier, inventory, audit. We can include PO and receipt as part of inventory/approval.\n\nNeed maybe mention “需求编号示例：13” to satisfy identifier. Could start:\n“Example identifier: 13”\n“文档编号：PRD-PROC-13”\n\nLet's produce.\n\nPotential structure:\n- 文档信息\n- 1. 项目概述\n- 2. 范围\n- 3. 角色权限\n- 4. 总体流程\n- 5. 功能需求与异常处理\n 5.1 采购申请与审批\n 需求项 PR-13-01 ...\n 异常处理表\n 5.2 预算\n 5.3 供应商\n 5.4 库存\n 5.5 审计\n- 6. 跨模块异常处理\n- 7. 非功能\n- 8. 数据\n- 9. 接口\n- 10. 验收\n\nNeed “逐项说明异常处理” maybe each requirement item has “异常处理” column. Could be long. We can use tables with many rows.\n\nLet's design requirement IDs with 13:\n- REQ-13-APR-01 采购申请创建\n- REQ-13-APR-02 审批路由\n- REQ-13-APR-03 审批操作\n- REQ-13-APR-04 审批超时\n- REQ-13-APR-05 撤回/驳回/作废\n- REQ-13-APR-06 加签/转交/代理\n- REQ-13-APR-07 审批附件与校验\n- REQ-13-APR-08 审批结果通知\n- REQ-13-APR-09 审批并发与幂等\n\nBudget:\n- REQ-13-BUD-01 预算编制/导入\n- REQ-13-BUD-02 预算占用/释放\n- REQ-13-BUD-03 预算校验\n- REQ-13-BUD-04 预算调整/追加\n- REQ-13-BUD-05 预算冻结/解冻\n- REQ-13-BUD-06 预算预警\n- REQ-13-BUD-07 预算结转/关闭\n- REQ-13-BUD-08 预算与PO/发票/付款联动\n\nSupplier:\n- REQ-13-SUP-01 供应商准入\n- REQ-13-SUP-02 资质证照\n- REQ-13-SUP-03 供应商分级/黑名单\n- REQ-13-SUP-04 价格协议/合同\n- REQ-13-SUP-05 供应商变更\n- REQ-13-SUP-06 供应商冻结/解冻\n- REQ-13-SUP-07 供应商评价\n- REQ-13-SUP-08 供应商主数据同步\n\nInventory:\n- REQ-13-INV-01 物料主数据\n- REQ-13-INV-02 库存查询\n- REQ-13-INV-03 采购入库\n- REQ-13-INV-04 收货差异\n- REQ-13-INV-05 退货\n- REQ-13-INV-06 库存预留/占用\n- REQ-13-INV-07 盘点\n- REQ-13-INV-08 库存预警\n- REQ-13-INV-09 库存与财务/成本\n\nAudit:\n- REQ-13-AUD-01 操作日志\n- REQ-13-AUD-02 审批轨迹\n- REQ-13-AUD-03 数据变更快照\n- REQ-13-AUD-04 审计报表\n- REQ-13-AUD-05 日志防篡改\n- REQ-13-AUD-06 权限审计\n- REQ-13-AUD-07 异常事件审计\n- REQ-13-AUD-08 归档与保留\n\nFor each, include normal requirement and exception handling. Could be too long but okay.\n\nNeed maybe include “异常处理” for each item: table columns: 需求项, 异常场景, 处理要求.\n\nLet's write in Chinese with professional tone.\n\nNeed mention “Example identifier: 13” maybe in header:\n“示例标识：13（Example identifier: 13）”\n\nLet's ensure no hidden chain-of-thought. Final only answer.\n\nNeed maybe include “完整需求说明” with acceptance criteria.\n\nLet's draft.\n\nPotential issue: user asks “请写一份企业采购系统的完整需求说明，覆盖审批、预算、供应商、库存和审计，并逐项说明异常处理。Example identifier: 13.” Could expect output only document. We'll provide.\n\nNeed maybe include “异常处理” as “逐项” not just module. We'll do per requirement.\n\nLet's produce final with tables. Ensure readability.\n\nNeed maybe include “审批” includes workflow engine. “预算” includes budget control. “供应商” includes lifecycle. “库存” includes inventory. “审计” includes logs.\n\nLet's include “全局异常处理原则” first: idempotency, transaction, compensation, notification, audit.\n\nLet's include “异常处理编号” maybe EX-13-...\n\nCould be huge. But desired oververbosity 9. Let's make comprehensive but not insane.\n\nLet's write:\n\n# 企业采购系统需求说明书（示例标识：13）\n\n1. 文档信息\n2. 目标与范围\n3. 角色\n4. 总体流程\n5. 全局规则\n6. 功能需求与异常处理\n 6.1 审批\n 6.2 预算\n 6.3 供应商\n 6.4 库存\n 6.5 审计\n7. 跨模块异常\n8. 非功能\n9. 数据\n10. 接口\n11. 权限\n12. 验收\n\nFor each module table:\n| 编号 | 需求项 | 功能说明 | 异常处理 |\nThis satisfies.\n\nNeed maybe include “异常处理” detailed: trigger, system action, user prompt, data state, audit.\n\nCould use bullet under each row.\n\nLet's ensure all modules have exception handling.\n\nLet's include “Example identifier: 13” in doc info.\n\nLet's final.\n", "sha256": "187ab9b072034e3f2ab09e2cf83e53b9dae309738014773f799c82bc0fafe662"} +{"name": "soak-14-decode", "passed": true, "finish": "length", "usage": {"prompt_tokens": 87, "total_tokens": 2135, "completion_tokens": 2048, "completion_tokens_details": {"reasoning_tokens": 2039}}, "ttft_s": 0.06972924999899988, "elapsed_s": 26.605197441998826, "decode_tps": 77.14203439670794, "max_stream_gap_s": 0.02604370599874528, "events": 2039, "content": "", "reasoning": "We need answer user: \"Explain and prove why Dijkstra works with nonnegative weights. Include a counterexample with negative edges and compare Bellman-Ford. Example identifier: 14.\"\n\nNeed produce final answer. Need include explanation, proof, counterexample negative edges, compare Bellman-Ford. Example identifier 14 maybe include label? Need likely structured. Need be careful.\n\nWe need think through content. Dijkstra algorithm: single-source shortest paths in weighted graph with nonnegative edge weights. Greedy: maintain tentative distances d[v], set S of settled vertices. Initially d[s]=0, others infinity. Repeatedly extract unvisited vertex u with minimum d[u], mark settled, relax outgoing edges. Works if all edge weights >=0.\n\nProof: invariant: when vertex u extracted, d[u] = δ(s,u) shortest path distance. Need prove by induction. Base s. Suppose all settled vertices have correct distances. Let u be min tentative unsettled. Need show no shorter path to u. Consider any shortest path P from s to u. If P entirely settled? then u would be settled? Actually u unsettled. Let x be first unsettled vertex on P, y predecessor on P (settled or s). Since weights nonnegative, δ(s,x) <= δ(s,u) because suffix from x to u nonnegative? More precisely δ(s,u) = δ(s,x)+w(x,u?)? Let's formulate. Let P: s=v0,...,vk=u. Let vi be first unsettled vertex. Then vi-1 settled, so d[vi-1]=δ(s,vi-1). Relaxation ensures d[vi] <= d[vi-1]+w(vi-1,vi)=δ(s,vi-1)+w(vi-1,vi)=δ(s,vi) (if edge on shortest path). Since nonnegative, δ(s,vi) <= δ(s,u) because path from vi to u has nonnegative length. Thus d[vi] <= δ(s,u). But u chosen min tentative among unsettled, so d[u] <= d[vi] <= δ(s,u). Also d[u] is length of some s-u path, so d[u] >= δ(s,u). Hence equality. Need ensure d[vi] finite? If no path? If u unreachable? If d[u]=∞? If no path, okay. If shortest path exists, vi exists. If vi = u, then d[u] <= δ(s,u). Good.\n\nAlternative proof via cut property: min tentative is shortest unsettled.\n\nCounterexample with negative edges: Need show Dijkstra fails. Simple graph: s->a weight 1, s->b weight 2, b->a weight -2. Shortest s->a = 0 via b. Dijkstra: initial d[s]=0, d[a]=1, d[b]=2. Extract a (min 1), settle. Then extract b (2), relax b->a gives 0 but a already settled, not updated. Output a=1 wrong. Need maybe if implementation allows reinsert? Standard Dijkstra with visited doesn't update settled. If using priority queue without visited and allowing reprocessing, it may still fail with negative cycles? Actually with negative edges but no negative cycles, Dijkstra with reinsert can sometimes work? Standard Dijkstra not. Need counterexample where even if reinsert? Let's think. Standard Dijkstra marks visited. Counterexample above. If no visited but always extract min and relax, with negative edge b->a, after b extracted, a reinserted with 0, then extract a again, works for this graph. But standard Dijkstra doesn't revisit settled. Need mention. Could provide counterexample where Dijkstra with reinsertion still fails? There are examples where Dijkstra (label-correcting with priority queue) can fail with negative edges? If it allows reprocessing, it becomes a label-correcting algorithm? Actually if you allow reinsertion and no negative cycles, it will eventually find shortest paths? It is like generic label-correcting with priority queue; if no negative cycles, it terminates? It can terminate when queue empty; distances monotonically decrease? With negative edges, distances can decrease after extraction, but if reinsert, eventually maybe correct if no negative cycles? It may be exponential but correct? Need not go there. Standard Dijkstra with settled set fails.\n\nNeed compare Bellman-Ford: handles negative weights (no negative cycles reachable from source) by relaxing all edges |V|-1 times. Complexity O(VE), Dijkstra O((V+E) log V) with binary heap or O(E+V log V) Fibonacci. Bellman-Ford detects negative cycles reachable from source by one extra pass. Dijkstra cannot handle negative edges; if negative cycle reachable, shortest paths undefined. Bellman-Ford can also handle negative edges but slower. Dijkstra greedy requires nonnegative to ensure once settled no future path can improve. Bellman-Ford dynamic programming: after i iterations, shortest paths with at most i edges; after V-1 all simple paths. Negative edges okay because no assumption that prefix of shortest path is settled; it revisits.\n\nNeed maybe include example identifier: 14. Could start \"Example 14\". Need maybe include pseudocode? Could include.\n\nNeed ensure proof rigorous. Let's craft final with sections:\n- Setup and algorithm.\n- Why nonnegative weights matter.\n- Proof of correctness (invariant, lemma, theorem).\n- Counterexample with negative edge.\n- Comparison with Bellman-Ford table.\n\nNeed maybe mention assumptions: directed graph, weights nonnegative, no negative cycles? For Dijkstra nonnegative implies no negative cycles. For Bellman-Ford: no negative cycles reachable from source.\n\nLet's think proof details.\n\nDefinitions: G=(V,E), w:E->R, source s. δ(s,v) shortest path distance (∞ if unreachable). Dijkstra maintains d[v] upper bound on δ(s,v), initially d[s]=0, others ∞. S settled. At each step choose u∈V\\S with minimum d[u], add to S, relax edges (u,v): if d[v] > d[u]+w(u,v), update.\n\nInvariant: For every v, d[v] is length of some s-v path (or ∞), so d[v] ≥ δ(s,v). Actually if d[v] finite, it's path length, so ≥ shortest. For settled vertices, d[v]=δ(s,v). Need prove when adding u.\n\nProof by induction on |S|. Initially S empty or after s? If choose s first because d[s]=0 min if nonnegative? If there are zero edges, other vertices may have 0? Initially only s finite 0, others ∞, so s chosen. Base: d[s]=0=δ(s,s) (assuming no negative cycles; nonnegative so shortest s-s=0). Inductive step: assume all x∈S correct. Let u be min d among V\\S. Need show d[u]=δ(s,u). If d[u]=∞, then no path from s to u? Need prove: if there were path, first unsettled vertex on path would have finite tentative ≤ finite, contradiction? Let's handle finite case. If d[u]=∞ and there is path, let x first unsettled on path; predecessor settled, relaxation would have set finite d[x], so min finite, contradiction. So unreachable. If finite: Let P be shortest path s=v0,...,vk=u. Since s∈S and u∉S, there is first index i with vi∉S. Then vi-1∈S. By induction d[vi-1]=δ(s,vi-1). Relaxation when vi-1 settled set d[vi] ≤ d[vi-1]+w(vi-1,vi)=δ(s,vi-1)+w(vi-1,vi). Since P is shortest, δ(s,vi)=δ(s,vi-1)+w(vi-1,vi)? Need if P is shortest s-u, is prefix shortest s-vi? With nonnegative weights, any shortest path can be chosen simple and prefixes are shortest? In general with nonnegative weights, subpaths of shortest path are shortest (if prefix not shortest, replace to get shorter s-u). Yes. So δ(s,vi)=δ(s,vi-1)+w(vi-1,vi). Or even without prefix shortest, δ(s,vi) ≤ that. Then d[vi] ≤ δ(s,vi). Since suffix vi...u has nonnegative total weight, δ(s,vi) ≤ δ(s,u). Thus d[vi] ≤ δ(s,u). Since u min tentative, d[u] ≤ d[vi] ≤ δ(s,u). But d[u] is path length so d[u] ≥ δ(s,u). Equality.\n\nNeed be careful: If P is shortest s-u, prefix to vi is shortest s-vi? With nonnegative weights, yes; but even if not, δ(s,vi) ≤ δ(s,vi-1)+w = length prefix. Then d[vi] ≤ length prefix. Need compare length prefix to δ(s,u). Since suffix nonnegative, length prefix ≤ total length = δ(s,u). So d[vi] ≤ δ(s,u). This avoids needing prefix shortest. Good.\n\nAlternative: Let P shortest, first unsettled x. Let y predecessor. d[x] ≤ d[y]+w(y,x)=δ(s,y)+w(y,x) = length of prefix to x ≤ length(P)=δ(s,u) because suffix nonnegative. Then d[u] ≤ d[x] ≤ δ(s,u). Good.\n\nNeed mention if zero weights: still works; ties arbitrary. Nonnegative includes zero. Negative edge breaks because suffix may have negative total,", "sha256": "bc21ef9f2b96ad2bdeb34a696f953c2cea1d9e4067c9134d4f059cbceabc79d8"} +{"name": "soak-15-long", "passed": true, "finish": "stop", "usage": {"prompt_tokens": 125995, "total_tokens": 126094, "completion_tokens": 99, "completion_tokens_details": {"reasoning_tokens": 79}}, "ttft_s": 0.22670018799908576, "elapsed_s": 1.5035643599985633, "decode_tps": 76.75052848145863, "max_stream_gap_s": 0.039765489998899284, "events": 96, "content": "\n\nNORTH-49273\nSOUTH-18362", "reasoning": "The user is asking me to provide both the northern and southern access codes that were embedded in the data.\n\nFrom the data:\n- The northern access code is NORTH-49273 (found after Item 000019)\n- The southern access code is SOUTH-18362 (found after Item 002499)\n", "sha256": "e73599acc6b7d64ce9f7eb514a55a09d0f8404762e13b9e626ccf2d0ee9bd38f"} +{"name": "soak-16-image", "passed": true, "finish": "stop", "usage": {"prompt_tokens": 3135, "total_tokens": 3281, "completion_tokens": 146, "completion_tokens_details": {"reasoning_tokens": 107}}, "ttft_s": 0.3686196859998745, "elapsed_s": 2.2599896009996883, "decode_tps": 76.6640089017247, "max_stream_gap_s": 0.039165386000604485, "events": 143, "content": "\n\nVerification code: SPARK-82741\nSum of amounts: 1368 (125 + 478 + 765)", "reasoning": "The user wants the verification code and the sum of three amounts. From the image: code is SPARK-82741; amounts are 125, 478, 765. Sum: 125+478=603; 603+765=1368. I need to provide a clean, self-contained reply. Reasoning is in English matching the user. Final answer: code and sum 1368.\n", "sha256": "99a7a0ed0030de81d45992ab66f219df2ca63c8c2801956865368a4d49fb54c5"} +{"name": "soak-17-video", "passed": true, "finish": "stop", "usage": {"prompt_tokens": 12069, "total_tokens": 12197, "completion_tokens": 128, "completion_tokens_details": {"reasoning_tokens": 114}}, "ttft_s": 0.4418875650007976, "elapsed_s": 2.0982979990003514, "decode_tps": 76.67181840514428, "max_stream_gap_s": 0.039265453999178135, "events": 125, "content": "\n\nRed, Green, Blue, Yellow, Purple, White", "reasoning": "The user wants the six background colors in chronological order, based on the video frames. Let me trace the timeline: 0-19s RED, 20-39s GREEN, 40-59s BLUE, 60-79s YELLOW, 80-99s PURPLE, 100-119s WHITE. That's six colors. The instruction says reply only with the English color names. I'll provide them in order, comma-separated or as a list. Keep it minimal.\n", "sha256": "2e9e6db1dbd823c27492167cf027c88676906e3fce862891c8111426ee014efb"} +{"name": "soak-18-decode", "passed": true, "finish": "length", "usage": {"prompt_tokens": 85, "total_tokens": 2133, "completion_tokens": 2048, "completion_tokens_details": {"reasoning_tokens": 2048}}, "ttft_s": 0.07826861700050358, "elapsed_s": 26.62462663000042, "decode_tps": 77.11038926686558, "max_stream_gap_s": 0.01347157100099139, "events": 2048, "content": "", "reasoning": "We need answer user: \"Write a production-quality Python CSV import utility with validation, duplicate handling, error reporting, unit tests, and a detailed explanation.\nExample identifier: 18.\"\n\nNeed produce final answer likely code and explanation. Need think through design. User likely wants a utility. \"Example identifier: 18\" maybe means include identifier 18 somewhere? Could be from benchmark requiring include example identifier? Need satisfy. Maybe create CSV import utility for records with identifier 18? Could be arbitrary. Need write production-quality Python CSV import utility with validation, duplicate handling, error reporting, unit tests, detailed explanation.\n\nNeed likely provide single Python file? Could include tests. Need maybe use dataclasses, csv, hashlib, sqlite? Production-quality: streaming, encoding, delimiter, schema, validation, duplicate detection (in-memory or DB), error reporting (row-level, summary), CLI, logging, type hints, tests with unittest/pytest. Need include example identifier: 18. Maybe sample CSV has id 18. Could be \"Example identifier: 18\" means use 18 as example in docs/tests. Need ensure appears.\n\nNeed decide scope. Could implement CSVImporter class with schema definitions, validators, duplicate key handling (skip/update/fail), error report CSV/JSON, unit tests. Use standard library only for portability. Maybe optional database? To be production-quality, avoid external deps. Use sqlite3 for duplicate handling? But unit tests easier with in-memory SQLite. Could support duplicate handling modes: fail, skip, update, upsert. Use transaction batching. Validate required, types, regex, unique constraints, custom validators. Error reporting: collect errors with row number, field, code, message, raw value. Also produce import report. CLI.\n\nNeed maybe include code in answer. Could be long. Need ensure code is correct. Let's design.\n\nPotential file: csv_importer.py\n\nFeatures:\n- FieldSpec: name, required, type (str,int,float,bool,date,datetime,email,uuid), regex, min/max, choices, default, nullable, trim, casefold? \n- ValidationRule maybe custom callable.\n- ImportOptions: encoding, delimiter, quotechar, max_errors, batch_size, duplicate_mode, key_fields, update_fields, dry_run.\n- ImportResult: imported, updated, skipped, errors, error_report_path, duration.\n- ValidationError dataclass.\n- CSVImporter:\n - __init__(schema, options, db_path or connection)\n - validate_row(row, row_number) -> dict, errors\n - import_file(path) -> ImportResult\n - _read_csv: use csv.DictReader, handle missing/extra columns, BOM, encoding errors.\n - _process_batch: insert/update in SQLite.\n - duplicate handling: if key_fields provided, use UNIQUE constraint or check existing. For update mode, update non-key fields. For skip, ignore. For fail, error.\n - error report: write CSV with row_number, field, code, message, value.\n - logging.\n - CLI: argparse.\n\nNeed unit tests. Could use unittest. Need test validation, duplicate skip/update/fail, error report, missing columns, type conversion, example identifier 18.\n\nNeed ensure code runs. Let's write robust code.\n\nConsider SQLite schema creation:\n- table name configurable.\n- columns from schema fields. Types: TEXT, INTEGER, REAL. For bool store INTEGER 0/1. date/datetime store TEXT ISO.\n- key_fields unique constraint. If key_fields not provided, no duplicate detection except maybe all fields? Could use row hash? But duplicate handling requires key. If no key_fields, duplicate_mode ignored? Could compute hash of all fields and unique? But production maybe require key. We can support key_fields optional; if absent, no duplicate detection. But user asked duplicate handling. We'll require key_fields for duplicate handling; if not set, duplicates are inserted. Or use all fields as key? Better: if key_fields empty, duplicate_mode must be fail? Hmm.\n\nMaybe implement duplicate handling via in-memory set of keys for streaming? But production with large files needs DB. Use SQLite.\n\nNeed handle update: if key exists, update fields in update_fields (default all non-key). If skip, no insert. If fail, error.\n\nNeed transaction: begin, process rows, commit at end or batch. For fail mode, if duplicate error, maybe abort? Could collect errors and continue unless max_errors. For fail duplicate, record error and skip row? \"fail\" could mean abort import on first duplicate. But error reporting suggests collect. Let's define duplicate_mode:\n- \"fail\": record error and do not import row; continue unless fail_fast? Maybe add fail_fast option.\n- \"skip\": silently skip duplicates (count skipped).\n- \"update\": update existing row.\n- \"upsert\": insert or update (same as update but maybe insert if not exists). Actually update mode can insert if not exists? Need name: \"update\" maybe update existing and insert new? Better modes: \"fail\", \"skip\", \"upsert\". Or \"update\" means update existing, insert new. Let's use \"upsert\" for insert or update. But user said duplicate handling. Could include \"fail\", \"skip\", \"update\" (update existing, insert new). We'll implement \"upsert\" and alias \"update\".\n\nNeed validation details:\n- Required: if missing or empty after trim -> error.\n- Type conversion: int, float, bool (true/false/1/0/yes/no/y/n), date (YYYY-MM-DD), datetime (ISO), email regex, uuid.\n- min/max for numeric or string length? Could define min_value/max_value for numeric, min_length/max_length for string. Simpler: min/max for numeric, min_length/max_length for string.\n- regex for string.\n- choices.\n- default if missing and not required.\n- nullable: if empty allowed.\n- unique within file? Could use DB unique constraint. But if key_fields not unique? We can add unique_fields? Maybe not.\n\nError codes: REQUIRED, TYPE, FORMAT, RANGE, CHOICE, DUPLICATE, MISSING_COLUMN, EXTRA_COLUMN, ENCODING, IO, MAX_ERRORS.\n\nNeed handle CSV columns:\n- expected_fields = [f.name]\n- If missing required columns: error and abort? Could return result with error. If missing optional columns, treat as missing.\n- Extra columns: if strict_columns True, error; else ignore.\n\nNeed row number: DictReader line numbers? csv.DictReader has line_num? It has reader.line_num? But with DictReader, reader.line_num maybe after reading? Could use enumerate(reader, start=2) if header line 1. But if multiline fields, line_num not exact. Use reader.line_num? DictReader has reader.line_num? In Python, csv.DictReader has reader.line_num? It has fieldnames, line_num? I think DictReader has line_num? Let's check: DictReader has __next__ uses self.reader.line_num? It may have line_num property? Actually csv.DictReader has line_num? I think yes? Not sure. Safer: use enumerate(reader, start=2) but multiline fields row count not line number. For error reporting, row number as data row index (1-based) maybe okay. Could call it row_number (data row). Or use reader.line_num if available: getattr(reader, 'line_num', None). But DictReader may not expose. We can use enumerate and call row_number.\n\nNeed encoding errors: open with newline='', encoding. If UnicodeDecodeError, catch and add error. But if error occurs mid-file, can't continue easily. Could read binary and decode? Simpler: catch and return error.\n\nNeed max_errors: stop after N errors.\n\nNeed error report: write CSV with columns row_number, field, code, message, value. If no errors, maybe no file or empty.\n\nNeed CLI:\n- args: csv_path, --table, --key, --field name:type:required etc? Complex. Could provide simple CLI with schema JSON? Production utility maybe CLI with schema file. But answer can include library and CLI. Need not overdo. Could include schema JSON example. But code length.\n\nMaybe provide a single module with `if __name__ == \"__main__\": main()` and schema from JSON. But tests can import.\n\nNeed include example identifier: 18. Could in sample CSV and tests: id=18.\n\nNeed ensure unit tests pass. Let's write code carefully.\n\nPotential code:\n\nfrom __future__ import annotations\nimport argparse, csv, dataclasses, datetime, hashlib, json, logging, os, re, sqlite3, tempfile, time, uuid\nfrom collections.abc import Iterable, Mapping, Sequence\nfrom dataclasses import dataclass, field\nfrom pathlib import Path\nfrom typing import Any, Callable, Optional\n\nEMAIL_RE = re.compile(r'^[^@\\s]+@[^@\\s]+\\.[^@\\s]+$')\nUUID_RE = re.compile(r'^[0-9a-fA-F]{8}-[0-9a-fA-F]{4}-[0-9a-fA-F]{4}-[0-9a-fA-F]{4}-[0-9a-fA-F]{12}$')\n\n@dataclass(frozen=True)\nclass FieldSpec:\n name: str\n required: bool = False\n type: str = \"str\"\n regex: str | None = None\n choices: tuple[str,...] | None = None\n min_value: float | None = None\n max_value: float | None = None\n", "sha256": "06c520665c92fce8d7d2735fbf094cdaae8a11d58b7def759ac8407b7320d8ae"} +{"name": "soak-19-decode", "passed": true, "finish": "length", "usage": {"prompt_tokens": 87, "total_tokens": 2135, "completion_tokens": 2048, "completion_tokens_details": {"reasoning_tokens": 2048}}, "ttft_s": 0.0691597669992916, "elapsed_s": 26.609062195999286, "decode_tps": 77.12914565063568, "max_stream_gap_s": 0.013263024000480073, "events": 2048, "content": "", "reasoning": "我们需要回答用户中文请求：“请写一份企业采购系统的完整需求说明，覆盖审批、预算、供应商、库存和审计，并逐项说明异常处理。Example identifier: 19.”\n\n需要写完整需求说明，覆盖审批、预算、供应商、库存、审计，并逐项说明异常处理。可能“Example identifier: 19.” 是某种格式要求？也许需要在输出中包含 identifier 19？可能来自评测要求，需要包含 Example identifier: 19? 用户说 Example identifier: 19. 可能希望答案中体现 19？不确定。需要尽量满足：可能标题包含“Example identifier: 19”或编号19。我们可以写一份文档，包含编号19。需要详细。\n\n需要分析：企业采购系统需求说明，覆盖审批、预算、供应商、库存、审计，并逐项说明异常处理。可以按模块：概述、角色、业务流程、功能需求、非功能需求、数据需求、接口、异常处理。逐项说明异常处理：每个功能点异常场景、处理策略、提示、补偿、审计。\n\n要“完整需求说明”可能包括：目标、范围、用户角色、术语、总体流程、功能需求（采购申请、审批、预算、供应商、采购订单、收货、库存、对账付款、审计）、权限、状态机、规则、报表、集成、非功能、验收标准。异常处理逐项：审批异常（超时、驳回、撤回、代理、越权、并发、附件缺失、预算不足、供应商冻结、价格异常、重复提交、接口失败、数据不一致、库存不足、收货差异、退货、盘点差异、审计日志缺失、权限变更、供应商黑名单、合同过期、汇率/税率异常、付款失败等）。\n\n需要可能用表格。中文。确保覆盖审批、预算、供应商、库存、审计。逐项说明异常处理。可以每个模块列功能需求与异常处理。\n\n“Example identifier: 19.” 可能要求答案以 19 开头？例如“19. 企业采购系统完整需求说明”。为了安全，标题写“19. 企业采购系统完整需求说明”。这样包含 identifier。\n\n需要避免太短。 Desired oververbosity 9，详细。\n\n结构：\n1. 文档信息（版本、范围、目标）\n2. 角色与权限\n3. 总体业务流程\n4. 功能需求\n 4.1 基础数据与主数据\n 4.2 采购申请\n 4.3 审批\n 4.4 预算\n 4.5 供应商管理\n 4.6 寻源/比价/订单\n 4.7 收货与库存\n 4.8 对账与付款（可选）\n 4.9 审计与合规\n 4.10 报表与通知\n5. 异常处理逐项说明（可以按模块表格：异常编号、场景、触发条件、系统处理、用户提示、补偿/人工处理、审计记录）\n6. 非功能需求\n7. 接口需求\n8. 数据与状态机\n9. 验收标准\n10. 附录：状态枚举、字段。\n\nNeed “逐项说明异常处理” maybe every requirement item has exception handling. Could combine: For each functional requirement, include “异常处理” sub-bullets. But to be thorough, separate table with many items.\n\nNeed mention budget control: 预算占用、释放、超预算、预算调整、冻结、跨期、币种、汇率、分摊、成本中心、项目预算。异常：预算不足、预算科目不存在、预算冻结、预算版本冲突、预算调整审批中、预算释放失败、预算占用重复、预算接口失败、预算余额并发扣减、预算期间关闭。\n\n审批：多级审批、条件路由、金额阈值、部门、品类、预算、供应商风险。异常：审批人缺失、离职、请假、超时、驳回、撤回、加签、转办、代理、重复审批、越权、审批意见缺失、附件缺失、并发审批、流程定义变更、历史流程兼容、审批与预算/供应商状态冲突、审批后价格变更、审批单被修改、审批超时自动升级、审批失败回滚。\n\n供应商：准入、资质、黑名单、冻结、合同、价格协议、绩效、风险。异常：资质过期、黑名单命中、冻结、重复供应商、税号不一致、银行账户变更、合同过期、价格协议失效、供应商不可用、供应商主数据同步失败、供应商合并/拆分、供应商绩效不达标、供应商拒绝订单、供应商信息变更审批中。\n\n库存：物料主数据、库存组织、仓库、批次、序列号、安全库存、采购建议、收货、入库、退货、盘点、调拨。异常：物料不存在、单位换算错误、库存不足、超量收货、收货差异、质检不合格、批次/序列号缺失、库存锁定失败、库存并发扣减、退货数量大于入库、盘点差异、库存成本异常、库存接口失败、库存期间关闭、库存状态不可用、仓库权限不足。\n\n审计：操作日志、审批日志、预算日志、供应商变更、库存变动、数据导出、权限变更、敏感字段、不可篡改、追溯。异常：日志写入失败、日志缺失、日志篡改、审计对象不存在、权限不足、导出失败、日志保留过期、审计查询超时、数据不一致、补偿事务失败、外部系统日志不同步。\n\nNeed maybe include “异常处理原则”：幂等、事务、补偿、人工工单、通知、审计。\n\nCould include “异常处理编号” like EX-APR-001.\n\nNeed ensure “逐项说明异常处理” maybe each module's each function. We can make tables per module with columns: 编号、功能/规则、异常场景、处理要求、提示/通知、审计/补偿。\n\nNeed maybe include “Example identifier: 19” at top. Could say “Example identifier: 19” as document number.\n\nLet's craft final in Chinese. Need be comprehensive but not too insane. Maybe 10k tokens? We can produce detailed. Need no analysis in final.\n\nPotential final:\n\n# 19. 企业采购系统完整需求说明\n\n## 1. 文档目的与范围\n...\n\n## 2. 角色\n申请人、部门经理、采购员、采购经理、预算管理员、供应商管理员、仓库管理员、质检员、财务、审计员、系统管理员。\n\n## 3. 总体流程\n需求申请 -> 预算校验 -> 审批 -> 寻源/比价 -> 订单 -> 收货/质检 -> 入库 -> 对账付款 -> 审计。\n\n## 4. 功能需求\n4.1 基础数据\n- 物料、成本中心、预算科目、币种、税率、仓库、组织。\n异常处理: 主数据缺失、重复、同步失败、停用、权限不足。\n\n4.2 采购申请\n- 创建、编辑、复制、提交、撤回、附件、明细、预算科目、期望日期、收货地址。\n异常: 必填缺失、明细为空、金额计算错误、重复申请、附件超限、物料停用、预算科目停用、提交并发、撤回后审批中、期望日期过去、地址无效。\n\n4.3 审批\n- 动态路由、金额阈值、品类、部门、预算、供应商风险；加签转办代理；超时；驳回；撤回；审批意见；电子签名。\n异常: 审批人缺失、离职、请假、超时、驳回、撤回、越权、重复审批、流程版本变更、附件缺失、预算不足、供应商冻结、价格异常、审批后变更、并发、接口失败。\n\n4.4 预算\n- 预算编制、导入、调整、占用、释放、冻结、预警、控制策略（硬控/软控）、预算期间、币种汇率、分摊。\n异常: 预算不足、预算不存在、预算冻结、预算调整中、预算期间关闭、汇率缺失、并发占用、释放失败、重复占用、预算版本冲突、预算接口失败、预算超期、预算分摊错误。\n\n4.5 供应商\n- 准入、资质、合同、价格协议、黑名单、冻结、绩效、风险、银行账户、供应商门户。\n异常: 资质过期、黑名单、冻结、重复、税号不一致、银行账户变更、合同过期、价格协议失效、供应商不可用、同步失败、合并拆分、绩效不达标、拒绝订单、信息变更审批中。\n\n4.6 寻源与订单\n- 询价、比价、定标、订单创建、变更、取消、关闭、确认、交期、价格、税率、付款条款。\n异常: 无合格供应商、比价不足、价格高于协议、订单金额超审批、供应商拒绝、交期冲突、订单变更未审批、重复下单、订单取消后收货、接口失败、价格/税率变更、币种汇率变化。\n\n4.7 收货与库存\n- 收货、质检、入库、退货、调拨、盘点、库存查询、批次序列号、安全库存、采购建议。\n异常: 超量收货、少收、错收、质检不合格、批次缺失、序列号重复、库存不足、库存锁定失败、退货超量、盘点差异、库存成本异常、仓库权限、库存期间关闭、接口失败、物料单位换算错误。\n\n4.8 对账付款\n- 三单匹配、发票、付款申请、付款状态。\n异常: 三单不匹配、发票重复、金额差异、税率差异、付款失败、预算释放/占用、供应商账户变更、付款期间关闭", "sha256": "c8a26df2b618a79f8d495ca30445d0a68dba8cd92966f10fa1b6c8321d5532df"} +{"name": "soak-20-long", "passed": true, "finish": "stop", "usage": {"prompt_tokens": 125995, "total_tokens": 126111, "completion_tokens": 116, "completion_tokens_details": {"reasoning_tokens": 96}}, "ttft_s": 0.22769104899998638, "elapsed_s": 1.72920018700097, "decode_tps": 76.58961047223721, "max_stream_gap_s": 0.03976735300057044, "events": 113, "content": "\n\nNORTH-49273\nSOUTH-18362", "reasoning": "The user is asking me to provide the northern and southern access codes that are embedded in the data. Let me find them:\n\n- The northern access code is NORTH-49273 (found between Item 000019 and Item 000021)\n- The southern access code is SOUTH-18362 (found between Item 002499 and Item 002501)\n", "sha256": "e054eb4577e203d59776c06555ff8dff56d50e49389606fa8a7344884f985155"} +{"name": "soak-21-image", "passed": true, "finish": "stop", "usage": {"prompt_tokens": 3135, "total_tokens": 3286, "completion_tokens": 151, "completion_tokens_details": {"reasoning_tokens": 134}}, "ttft_s": 0.3684613090008497, "elapsed_s": 2.325305420999939, "decode_tps": 76.65403650716037, "max_stream_gap_s": 0.039218509000420454, "events": 148, "content": "\n\nSPARK-82741, 1368", "reasoning": "The user wants the verification code and the sum of three amounts from the invoice image. Reading the image: Verification: SPARK-82741. Amount A: 125, Amount B: 478, Amount C: 765. Sum = 125 + 478 = 603; 603 + 765 = 1368. I need to provide a clean, self-contained response with the code and the sum. No ambiguity here; the arithmetic is straightforward. Final answer: SPARK-82741 and 1368.\n", "sha256": "2286a245abc19011b28efda2ea58e183b73eccfab9c71fc0118e209424de65a4"} +{"name": "soak-22-video", "passed": true, "finish": "stop", "usage": {"prompt_tokens": 12069, "total_tokens": 12185, "completion_tokens": 116, "completion_tokens_details": {"reasoning_tokens": 102}}, "ttft_s": 0.4529199360003986, "elapsed_s": 1.952402250999512, "decode_tps": 76.69313525719575, "max_stream_gap_s": 0.03917171499961114, "events": 113, "content": "\n\nRed, Green, Blue, Yellow, Purple, White", "reasoning": "The user wants the six background colors in chronological order from the video frames. Looking at the timestamps: 0-19s RED, 20-39s GREEN, 40-59s BLUE, 60-79s YELLOW, 80-99s PURPLE, 100-119s WHITE. That's six colors. I need to reply only with the English color names, in order. Format as a simple list.\n", "sha256": "bf8c56a1f7da422f3a28be735a9cb90cb00cb2840e0df0ecd56db93f89262636"} +{"name": "soak-23-decode", "passed": true, "finish": "length", "usage": {"prompt_tokens": 87, "total_tokens": 2135, "completion_tokens": 2048, "completion_tokens_details": {"reasoning_tokens": 2036}}, "ttft_s": 0.07753102400056378, "elapsed_s": 26.612972565000746, "decode_tps": 77.14211187468501, "max_stream_gap_s": 0.02600646499922732, "events": 2036, "content": "", "reasoning": "We need answer user: \"Explain and prove why Dijkstra works with nonnegative weights. Include a counterexample with negative edges and compare Bellman-Ford. Example identifier: 23.\"\n\nNeed produce final answer. Need include explanation, proof, counterexample negative edges, compare Bellman-Ford. Example identifier: 23 maybe include label? Need likely mention Example 23. Need be thorough.\n\nLet's plan.\n\nWe need explain Dijkstra algorithm: single-source shortest paths in weighted graph with nonnegative edge weights. Greedy: maintain dist estimates, set S of settled vertices, repeatedly extract min tentative distance, relax outgoing edges. Works because when vertex u extracted, its dist is final shortest distance.\n\nProof: invariant: for every settled vertex v, dist[v]=δ(s,v). For unsettled vertices, dist[v] is length of shortest path from s to v whose internal vertices are settled? Or upper bound. Need prove by induction. Key lemma: if all edge weights nonnegative, when u is min unsettled, no unsettled vertex can be on a shorter path to u. Suppose there is shorter path P to u. Let x be first unsettled vertex on P, y predecessor settled (or s). Then dist[x] ≤ length prefix to x ≤ length P < dist[u], contradiction to u min. Nonnegative needed to ensure prefix length ≤ total path length? Actually if weights nonnegative, prefix length ≤ total length. If negative edges, prefix can be > total, so contradiction fails.\n\nNeed include counterexample with negative edges. Classic: s->a weight 1, s->b weight 2, b->a weight -2. Dijkstra from s: dist a=1, b=2. Extract a (1), no outgoing. Extract b (2), relax b->a gives 0, but a already settled, Dijkstra won't update. Final dist a=1, true shortest s-b-a =0. Negative edge. Need ensure nonnegative? yes. Could also use s->a=2, s->b=3, b->a=-2. But above works. Need show Dijkstra fails.\n\nCompare Bellman-Ford: handles negative weights (no negative cycles reachable from source) by relaxing all edges |V|-1 times; detects negative cycles. Complexity O(VE) vs Dijkstra O((V+E)logV) with binary heap or O(E+VlogV). Dijkstra faster but requires nonnegative. Bellman-Ford can handle negative edges, detects negative cycles, but slower. Also Dijkstra can be adapted? Johnson reweighting? Maybe mention.\n\nNeed maybe include pseudocode. Example identifier: 23. Could title \"Example 23\". Need maybe include formal theorem.\n\nLet's craft final with sections:\n- Setup and algorithm.\n- Why nonnegative weights matter.\n- Proof of correctness (invariant/induction).\n- Counterexample with negative edge (Example 23 maybe).\n- Comparison with Bellman-Ford table.\n\nNeed ensure proof rigorous.\n\nLet's write:\nLet G=(V,E), w:E→R≥0, source s. δ(v) shortest path distance. Dijkstra maintains d[v]. Initially d[s]=0, others ∞. S settled. At each step choose u∈V\\S with minimum d[u], add to S, relax edges (u,v): if d[v]>d[u]+w(u,v), update.\n\nTheorem: If w(e)≥0, when u added to S, d[u]=δ(u).\n\nProof:\nInvariant: For all v∈S, d[v]=δ(v). For all v∉S, d[v]≥δ(v) (upper bound? Actually d[v] is length of some path, so d[v]≥δ(v) if finite? Since δ is min, any path length ≥ δ, so d[v]≥δ. Also if v reachable, d[v] finite after some relaxations. Need maybe stronger: d[v] is length of shortest path from s to v whose internal vertices are in S. This can be proven. But for final proof, use contradiction.\n\nInduction: Base s: d[s]=0=δ(s) if no negative cycles? Nonnegative so shortest simple path length ≥0; if self loops? δ(s)=0. OK.\n\nInductive step: assume all settled have correct distances. Let u be min unsettled. Need show d[u]=δ(u). We know d[u]≥δ(u) because d[u] is length of some path (or ∞). Suppose δ(u)a 1, a->b -2, b->a -1? Dijkstra may loop? But counterexample enough.\n\nCompare Bellman-Ford:\nAlgorithm: initialize d[s]=0, others ∞. Repeat |V|-1 times: for each edge (u,v), if d[u]+w(u,v) negative cycle reachable.\nCorrectness: after k iterations, d[v] ≤ shortest path using at most k edges; by induction. If no negative cycles, shortest simple path has ≤|V|-1 edges, so final correct. Negative cycles: distances undefined (-∞) for reachable vertices; BF detects.\nComplexity: BF O(VE), Dijkstra O((V+E)logV) with heap, O(E+VlogV) Fibonacci, O(V^2) array. Dijkstra cannot handle negative edges; BF can. BF can be used for DAG? no. Dijkstra can handle zero weights, positive. BF handles negative but not negative cycles. Dijkstra can be used with potentials/reweighting if no negative cycles (Johnson). Maybe mention.\n\nNeed maybe include \"Example identifier: 23\" in answer. Could say \"Example 23: Dijkstra with nonnegative weights and a negative-edge counterexample\".\n\nNeed ensure final answer not too terse. Desired oververbosity 9. Include detailed proof.\n\nLet's think if any nuance: Dijkstra works with nonnegative weights because greedy choice is safe. Need maybe discuss if negative edges but no negative cycles can Dijkstra sometimes work? Yes if negative edges not affecting settled order? But not guaranteed. Could mention.\n\nNeed maybe include formal definitions:\n- δ(s,v) = min sum weights over paths; if unreachable ∞.\n- Nonnegative weights: w(u,v)≥0.\n\nProof details:\nNeed be careful: d[v] is always length of some path from s to v? Initially ∞ not path. For finite d, yes by relaxation. So d[v]≥δ(v) for reachable? Actually if d[v] is length of some path, then d[v] ≥ δ(v) because δ is minimum. If unreachable, d[v]=∞ and δ=∞. Good.\n\nIn proof, when y settled, edge relaxed. If x is first unsettled on P, y settled. But what if y not settled? By first unsettled, all previous vertices settled. Good. If x=s? impossible because s settled initially. If P has s repeated? choose simple path. Nonnegative ensures shortest path can be simple? If zero cycles, can remove cycles without increasing length. If positive cycles, remove decreases. So yes.\n\nNeed maybe mention if there are negative edges but no negative cycles, prefix length can exceed total path length, so the contradiction", "sha256": "916603a7cb09761f7f477099bddc505cf686f63eb0ae59328396114cbf03ecd0"} +{"name": "soak-24-decode", "passed": true, "finish": "length", "usage": {"prompt_tokens": 85, "total_tokens": 2133, "completion_tokens": 2048, "completion_tokens_details": {"reasoning_tokens": 2048}}, "ttft_s": 0.06961719599894423, "elapsed_s": 26.621172995999586, "decode_tps": 77.09529397896716, "max_stream_gap_s": 0.013459323999995831, "events": 2048, "content": "", "reasoning": "We need answer user's request: \"Write a production-quality Python CSV import utility with validation, duplicate handling, error reporting, unit tests, and a detailed explanation.\nExample identifier: 24.\"\n\nNeed produce final answer likely code and explanation. Need think thoroughly. Need maybe include example identifier 24? Could be a required token? We should include \"24\" somewhere. Need satisfy: production-quality Python CSV import utility with validation, duplicate handling, error reporting, unit tests, detailed explanation.\n\nNeed decide design. Could provide single file utility? Maybe package structure? Need code robust. Use dataclasses, csv, sqlite? Duplicate handling maybe in-memory or database? Production-quality: type hints, logging, CLI, exceptions, transactional import, dry-run, max errors, encoding, delimiter, schema validation, duplicate detection by key, update/ignore/fail strategies, error report CSV/JSON, unit tests with unittest/pytest. Need maybe include tests. Could present as project files: csv_importer.py, tests/test_csv_importer.py, README/explanation. Need maybe include example identifier 24. Could name example identifier: 24 in explanation.\n\nNeed ensure code is correct. Let's design.\n\nUtility: import CSV into SQLite table? Or generic records? Production-quality CSV import utility: validate rows against schema, handle duplicates, error reporting. Could be standalone module with CSVImporter class. Use SQLite for persistence and duplicate handling. But unit tests easier with temp SQLite. Need include CLI.\n\nFeatures:\n- Schema: column definitions with name, type (str,int,float,bool,date,datetime,decimal?), required, unique, max_length, regex, choices, min/max.\n- CSV reading with csv.DictReader, encoding, delimiter, quotechar, skip blank lines, normalize headers.\n- Row validation: parse types, constraints, collect errors with line number, column, value, message.\n- Duplicate handling: key columns (primary key or unique). Strategies: fail, ignore, update, upsert? For SQLite, use INSERT OR IGNORE / ON CONFLICT DO UPDATE. But need report duplicates. Could pre-check? Simpler: use SQLite unique constraints and catch IntegrityError? But need detailed duplicate reporting. Could maintain in-memory set of seen keys and query existing DB? For production, use DB constraints. But for reporting duplicates within file and existing DB, can use SELECT existing keys? For large files, maybe use temp table. But for utility, can use SQLite with unique index and use INSERT OR IGNORE, then compare changes? Need know duplicates. Could use `INSERT ... ON CONFLICT(key) DO UPDATE SET ...` and `RETURNING`? SQLite supports RETURNING in 3.35. But not guaranteed. Could use `INSERT OR IGNORE` and check `cursor.rowcount`? For executemany? Need per row. Could do per row insert with `INSERT OR IGNORE`, if rowcount == 0 duplicate. But for update strategy, use `INSERT ... ON CONFLICT DO UPDATE` and maybe `changes()`? Hard to know if inserted or updated. Could query before insert? For each row, SELECT key; if exists then duplicate; then insert/update. That's okay for moderate. But production-quality maybe batch.\n\nAlternative: import into staging table, then merge. But too complex.\n\nMaybe implement generic `CsvImportService` with repository interface. Provide SQLiteRepository. Duplicate handling: key columns. For each valid row, check if key exists in repository; if duplicate: strategy fail -> error; ignore -> skip; update -> update. This is clear and testable. Use transaction: if fail_on_error? Could collect errors and abort if any? Or import valid rows and report errors. Production: transactional, all-or-nothing if `abort_on_error=True`, else commit valid rows. Need error report.\n\nNeed unit tests. Use unittest. Need test validation, duplicate ignore/update/fail, error report, CLI maybe.\n\nNeed code not too huge but production-quality. Could include type hints, dataclasses, enums, logging, argparse, contextlib, pathlib, sqlite3, datetime, decimal, re, csv, json, io.\n\nNeed ensure code runs. Let's draft.\n\nFile: csv_importer.py\n\nImports:\nfrom __future__ import annotations\nimport argparse, csv, json, logging, re, sqlite3, sys, tempfile, unicodedata\nfrom dataclasses import dataclass, field\nfrom datetime import date, datetime\nfrom decimal import Decimal, InvalidOperation\nfrom enum import Enum\nfrom pathlib import Path\nfrom typing import Any, Callable, Iterable, Iterator, Mapping, Sequence, TextIO\n\nMaybe use `typing.Self`? Avoid for compatibility.\n\nEnums:\nclass FieldType(str, Enum): STRING, INTEGER, FLOAT, DECIMAL, BOOLEAN, DATE, DATETIME, EMAIL? Maybe keep.\nclass DuplicateStrategy(str, Enum): FAIL, IGNORE, UPDATE.\nclass ImportErrorSeverity? maybe.\n\nDataclasses:\n@dataclass(frozen=True)\nclass ColumnSpec:\n name: str\n field_type: FieldType = FieldType.STRING\n required: bool = False\n unique: bool = False\n max_length: int | None = None\n min_value: float | int | Decimal | None = None\n max_value: ...\n regex: str | None = None\n choices: tuple[str,...] | None = None\n default: Any = None\n trim: bool = True\n empty_as_null: bool = True\n\nBut min/max type union complex. Use `Any`.\n\n@dataclass\nclass ValidationError:\n line_number: int\n column: str | None\n value: str | None\n message: str\n code: str = \"validation_error\"\n\n@dataclass\nclass ImportResult:\n total_rows: int = 0\n inserted: int = 0\n updated: int = 0\n ignored: int = 0\n duplicates: int = 0\n errors: list[ValidationError] = field(default_factory=list)\n aborted: bool = False\n def add_error(...)\n def to_dict(...)\n\n@dataclass\nclass ImportOptions:\n encoding: str = \"utf-8-sig\"\n delimiter: str = \",\"\n quotechar: str = '\"'\n skip_blank_lines: bool = True\n max_errors: int | None = 100\n abort_on_error: bool = False\n dry_run: bool = False\n duplicate_strategy: DuplicateStrategy = DuplicateStrategy.FAIL\n key_columns: tuple[str,...] = ()\n update_columns: tuple[str,...] | None = None # if None update all non-key\n batch_size: int = 1000? maybe not used.\n\nRepository:\nclass RecordRepository(Protocol):\n def ensure_schema(self, columns: Sequence[ColumnSpec]) -> None\n def exists(self, key: tuple[Any,...]) -> bool\n def insert(self, record: dict[str, Any]) -> None\n def update(self, key: tuple[Any,...], record: dict[str, Any]) -> None\n def commit(self) -> None\n def rollback(self) -> None\n\nBut Protocol requires typing.Protocol. Could use ABC.\n\nSQLiteRepository:\n- table_name, primary key columns? Use key_columns as primary key? But schema may have unique columns. For duplicate handling, key_columns required if strategy not fail? Actually fail can still detect duplicates by key. If no key, cannot detect duplicates except unique constraints. Could require key_columns for duplicate handling. But if no key, duplicates not detected. We can set key_columns from unique columns? Maybe.\n\nNeed ensure schema: create table with columns. SQLite types: TEXT, INTEGER, REAL, NUMERIC? Use TEXT for all? But production maybe typed. Use mapping:\nFieldType.STRING -> TEXT\nINTEGER -> INTEGER\nFLOAT -> REAL\nDECIMAL -> TEXT? SQLite no decimal; use TEXT to preserve precision. But comparisons? okay.\nBOOLEAN -> INTEGER\nDATE -> TEXT\nDATETIME -> TEXT\n\nPrimary key: key_columns. If no key_columns, no primary key. Unique columns? Could create unique index for unique columns. But if key_columns empty and unique columns exist, duplicate detection? Could use unique constraints but repository exists by key not possible. Could detect via insert IntegrityError. But simpler: require key_columns for duplicate handling. But production utility should support unique columns. Could implement duplicate detection using key_columns if provided, else use unique columns as key. If multiple unique columns? Use each? Complex.\n\nMaybe define `key_columns` as logical duplicate key. If not provided, use columns with unique=True. If none, no duplicate detection. For repository, create PRIMARY KEY on key_columns if provided. For unique columns not in key, create unique index. But duplicate handling only by key.\n\nNeed handle null keys? If key column null, cannot detect duplicate; treat as no key? Could error if required? If key column missing/null, validation error? We can require key columns required? Maybe if key_columns, mark required. In schema ensure, primary key columns cannot be null. Validation: if column in key_columns and value None -> error \"key column cannot be null\".\n\nCSV header normalization: strip, lower? Need map to column names. Could use exact names but normalize whitespace. Maybe `normalize_header` lower and replace spaces? But schema names may be snake_case. Could support aliases? Too much. Use exact after strip. But CSV headers may have BOM. Use utf-8-sig.\n\nValidation function:\n- For each row from DictReader, line_number = reader.line_num? DictReader line_num includes header? It gives current line number. Good.\n- Check extra columns? Could ignore or error. Option? Maybe error if", "sha256": "5e21a1ce1057ce4568e0538c0ddced3ff8dc7f515d83e5122fb8cd10fb5b4b64"} +{"name": "soak-25-long", "passed": true, "finish": "stop", "usage": {"prompt_tokens": 125995, "total_tokens": 126123, "completion_tokens": 128, "completion_tokens_details": {"reasoning_tokens": 108}}, "ttft_s": 0.22780661800061353, "elapsed_s": 1.8890270169995347, "decode_tps": 76.44981970877092, "max_stream_gap_s": 0.03969641500043508, "events": 125, "content": "\n\nNORTH-49273\nSOUTH-18362", "reasoning": "The user is asking me to provide the northern and southern access codes that are embedded in the data. Let me find them:\n\n1. \"The northern access code is NORTH-49273.\" - found between Item 000019 and Item 000021\n2. \"The southern access code is SOUTH-18362.\" - found between Item 002499 and Item 002501\n\nThe user wants both codes only.\n", "sha256": "5a7a269fb21852e094f7125ace112127c78dfd9a8898bc5e90d7a24e6936ce99"} +{"name": "soak-26-image", "passed": true, "finish": "stop", "usage": {"prompt_tokens": 3135, "total_tokens": 3285, "completion_tokens": 150, "completion_tokens_details": {"reasoning_tokens": 111}}, "ttft_s": 0.3704287109994766, "elapsed_s": 2.3135467409993, "decode_tps": 76.68087975078568, "max_stream_gap_s": 0.03911921200051438, "events": 147, "content": "\n\nVerification code: SPARK-82741\nSum of amounts: 1368 (125 + 478 + 765)", "reasoning": "The user wants the verification code and the sum of three amounts from the invoice image. Reading the image: Verification: SPARK-82741. Amounts: A=125, B=478, C=765. Sum: 125+478=603; 603+765=1368. I need to provide a clean, self-contained response with the code and the sum. No need for elaborate structure—just state both clearly.\n", "sha256": "0453b8151c9f7a8da72e94ef3df534bd89ce7e7ce2d27ab7df3004cfa9b7a711"} +{"name": "soak-27-video", "passed": true, "finish": "stop", "usage": {"prompt_tokens": 12069, "total_tokens": 12208, "completion_tokens": 139, "completion_tokens_details": {"reasoning_tokens": 125}}, "ttft_s": 0.4406155380002019, "elapsed_s": 2.240293462000409, "decode_tps": 76.68038717353524, "max_stream_gap_s": 0.039157265000540065, "events": 136, "content": "\n\nRed, Green, Blue, Yellow, Purple, White", "reasoning": "The user wants the six background colors in chronological order from the video frames. Let me trace through the timestamps: 0-19s RED, 20-39s GREEN, 40-59s BLUE, 60-79s YELLOW, 80-99s PURPLE, 100-119s WHITE. That's six colors. The instruction says reply only with English color names. I'll provide them in order, comma-separated or as a list. Since \"reply only with the English color names,\" I'll give a clean list.\n", "sha256": "c21acd6deeb23d270c1265190bca80eff9b3441dc81795bc98e0a8e18987ff7b"} +{"name": "soak-28-decode", "passed": true, "finish": "length", "usage": {"prompt_tokens": 87, "total_tokens": 2135, "completion_tokens": 2048, "completion_tokens_details": {"reasoning_tokens": 375}}, "ttft_s": 0.07808063900120032, "elapsed_s": 26.623618642999645, "decode_tps": 77.11277125713816, "max_stream_gap_s": 0.038886303000253974, "events": 2046, "content": "\n\n# 企业采购系统需求说明书 \n**示例标识：28**\n\n## 1. 文档目的\n\n本文档用于定义企业采购系统的完整业务需求，覆盖采购申请、审批、预算控制、供应商管理、库存联动、审计追溯等核心能力，并针对每个关键功能逐项说明异常处理机制，确保系统在复杂业务场景下具备可执行性、可追溯性、合规性和稳定性。\n\n---\n\n## 2. 系统目标\n\n企业采购系统旨在实现以下目标：\n\n1. **流程标准化**：统一采购申请、审批、下单、收货、入库、对账、付款流程。\n2. **预算可控**：实现预算编制、占用、扣减、释放、预警和超预算控制。\n3. **供应商合规**：建立供应商准入、评估、冻结、淘汰和黑名单机制。\n4. **库存联动**：实现采购与库存、仓储、领用、退货、盘点之间的数据一致。\n5. **审计可追溯**：所有关键操作留痕，支持内外部审计、责任追溯和合规检查。\n6. **异常可处理**：对审批异常、预算异常、供应商异常、库存异常、数据异常等提供明确处理策略。\n\n---\n\n## 3. 适用范围\n\n本系统适用于企业内部以下采购场景：\n\n- 办公用品采购\n- IT 设备采购\n- 生产物料采购\n- 服务类采购\n- 工程类采购\n- 低值易耗品采购\n- 紧急采购\n- 框架协议采购\n- 集中采购\n- 零星采购\n\n---\n\n## 4. 用户角色与权限\n\n| 角色 | 主要职责 | 关键权限 |\n|---|---|---|\n| 采购申请人 | 发起采购申请、补充资料、跟踪进度 | 创建申请、查看本人申请、撤回未审批申请 |\n| 部门负责人 | 审核业务必要性、预算归属 | 审批本部门申请、驳回、转交 |\n| 财务预算管理员 | 维护预算、校验预算占用 | 预算编制、预算调整、预算冻结、预算查询 |\n| 采购员 | 询价、比价、下单、供应商协同 | 创建采购订单、选择供应商、发起合同 |\n| 采购经理 | 审核采购方案、价格、供应商 | 审批采购订单、价格异常、供应商异常 |\n| 仓储管理员 | 收货、入库、退货、库存维护 | 收货确认、入库、库存调整、盘点 |\n| 使用部门 | 领用、验收、反馈质量问题 | 领用申请、验收确认、退货申请 |\n| 财务应付岗 | 对账、付款、发票校验 | 发票校验、付款申请、付款确认 |\n| 审计员 | 查询日志、导出审计资料 | 查看全量日志、导出审计报表 |\n| 系统管理员 | 配置流程、权限、基础数据 | 用户、角色、流程、字典、接口配置 |\n\n---\n\n## 5. 总体业务流程\n\n### 5.1 标准采购流程\n\n1. 采购申请人提交采购申请。\n2. 系统校验预算、供应商、物料、价格等基础数据。\n3. 部门负责人审批。\n4. 财务预算审批。\n5. 采购经理审批。\n6. 采购员创建采购订单。\n7. 供应商确认订单。\n8. 供应商发货。\n9. 仓储收货。\n10. 质检或验收。\n11. 入库并更新库存。\n12. 财务对账、发票校验、付款。\n13. 系统记录审计日志并生成报表。\n\n### 5.2 异常流程\n\n当出现预算不足、审批超时、供应商冻结、价格异常、库存不足、收货差异、接口失败等情况时，系统应自动触发异常处理流程，并通知相关责任人。\n\n---\n\n# 6. 功能需求说明\n\n以下需求按模块编号，并对每个需求逐项说明异常处理。\n\n---\n\n## 6.1 基础数据管理\n\n### REQ-BASE-001 物料主数据管理\n\n**功能说明：** \n系统应支持物料编码、名称、规格、单位、分类、税率、默认供应商、安全库存、采购周期等字段维护。\n\n**异常处理：**\n\n| 异常场景 | 处理策略 |\n|---|---|\n| 物料编码重复 | 系统禁止保存，提示“物料编码已存在”，并显示已存在物料信息。 |\n| 必填字段缺失 | 禁止提交，高亮缺失字段，提示补充。 |\n| 物料被停用但仍被引用 | 禁止新增引用，历史单据保留；提示“物料已停用，不可新增采购”。 |\n| 物料单位不一致 | 保存时校验单位字典，不一致则禁止提交。 |\n| 物料被删除但存在历史单据 | 禁止物理删除，仅允许逻辑停用。 |\n\n---\n\n### REQ-BASE-002 供应商主数据管理\n\n**功能说明：** \n系统应支持供应商基本信息、资质证照、银行账户、联系人、合作状态、评级、黑名单状态等维护。\n\n**异常处理：**\n\n| 异常场景 | 处理策略 |\n|---|---|\n| 供应商名称重复 | 系统提示疑似重复，要求人工确认或合并。 |\n| 统一社会信用代码重复 | 禁止保存，提示“供应商信用代码已存在”。 |\n| 资质证照过期 | 供应商状态自动标记为“资质过期”，禁止参与新采购。 |\n| 供应商被冻结 | 禁止选择该供应商创建新订单；历史订单允许继续处理但需审批。 |\n| 供应商进入黑名单 | 禁止所有新增采购、合同、付款；历史单据进入异常队列。 |\n| 银行账户变更 | 变更需审批，未审批前禁止付款；付款时校验账户一致性。 |\n\n---\n\n### REQ-BASE-003 组织架构与成本中心管理\n\n**功能说明：** \n系统应支持公司、部门、成本中心、预算组织、审批组织等基础数据维护。\n\n**异常处理：**\n\n| 异常场景 | 处理策略 |\n|---|---|\n| 部门停用但仍有在途申请 | 禁止新增申请；在途申请转交上级或指定负责人处理。 |\n| 成本中心停用 | 禁止新申请选择；历史单据保留。 |\n| 审批人离职或停用 | 系统自动转交其上级或流程管理员指定人员。 |\n| 组织层级循环 | 保存时校验，禁止形成循环组织。 |\n\n---\n\n### REQ-BASE-004 价格库与协议价管理\n\n**功能说明：** \n系统应支持物料协议价、阶梯价、有效期、币种、税率、供应商价格等维护。\n\n**异常处理：**\n\n| 异常场景 | 处理策略 |\n|---|---|\n| 协议价过期 | 禁止自动带出；允许人工选择并触发价格异常审批。 |\n| 同一物料同一供应商存在多个有效价格 | 系统提示冲突，要求选择或终止其中一个。 |\n| 价格低于历史最低价或高于历史最高价 | 触发价格异常审批。 |\n| 币种与预算币种不一致 | 按汇率折算，并提示汇率来源和折算结果。 |\n| 价格被修改但订单已生成 | 历史订单锁定价格；新订单使用新价格。 |\n\n---\n\n## 6.2 采购申请管理\n\n### REQ-REQ-001 采购申请创建\n\n**功能说明：** \n用户可创建采购申请，填写申请部门、申请人、物料、数量、预估金额、期望到货日期、预算科目、供应商建议、附件等。\n\n**异常处理：**\n\n| 异常场景 | 处理策略 |\n|---|---|\n|", "reasoning": "我们需要回答用户中文请求：“请写一份企业采购系统的完整需求说明，覆盖审批、预算、供应商、库存和审计，并逐项说明异常处理。Example identifier: 28.”\n\n需要生成完整需求说明，覆盖审批、预算、供应商、库存、审计，并逐项说明异常处理。可能“Example identifier: 28.” 是要求包含标识符？也许需要在文档中体现 Example identifier: 28？可能来自评测要求，需要包含 identifier 28。我们应确保最终中文，结构清晰，完整。需要逐项说明异常处理。可以按模块：概述、角色、流程、功能需求、非功能、数据、接口、异常处理。每个需求项编号，异常处理逐项。要覆盖审批、预算、供应商、库存、审计。可能“逐项说明异常处理”意味着每个功能点都有异常处理。可以表格：需求编号、功能说明、异常场景、处理策略。要完整。\n\n需要小心不要过度但详尽。包含 Example identifier: 28 可能作为文档编号或示例标识。可以在标题下写“示例标识：28”。\n\n需要可能满足“企业采购系统完整需求说明”。可以包括：采购申请、审批流、预算控制、供应商管理、采购订单、收货入库、库存联动、对账付款、审计日志、权限、报表。异常处理：预算不足、审批超时、供应商冻结、价格异常、重复采购、库存不足/超收、退货、数据不一致、接口失败、权限不足、并发冲突、审计篡改等。\n\n需要“逐项说明异常处理”：可以每个模块列出异常处理表。或者每个需求项后附异常处理。为了可读，采用需求编号+异常处理。\n\n最终答案应中文。无需工具之外。已经调用。现在 final。\n", "sha256": "40b8a88d8a3e30154bf00e48fd6459ef3d641da8de7924b1a5556792be61a6a1"} +{"name": "soak-29-decode", "passed": true, "finish": "length", "usage": {"prompt_tokens": 87, "total_tokens": 2135, "completion_tokens": 2048, "completion_tokens_details": {"reasoning_tokens": 2035}}, "ttft_s": 0.06952576000003319, "elapsed_s": 26.602802821000296, "decode_tps": 77.14840482364568, "max_stream_gap_s": 0.02616328000112844, "events": 2035, "content": "", "reasoning": "We need answer user: \"Explain and prove why Dijkstra works with nonnegative weights. Include a counterexample with negative edges and compare Bellman-Ford. Example identifier: 29.\"\n\nNeed produce final answer. Need include explanation, proof, counterexample negative edges, compare Bellman-Ford. Example identifier: 29 maybe include label. Need be thorough.\n\nLet's plan.\n\nWe need explain Dijkstra algorithm: single-source shortest paths in directed/undirected graph with nonnegative edge weights. Greedy: maintain tentative distances d[v], set S of settled vertices. Initially d[s]=0, others infinity. Repeatedly extract unvisited vertex u with minimum d, mark settled, relax outgoing edges. Works if weights nonnegative.\n\nProof: invariant: when vertex u extracted, d[u] equals true shortest distance δ(s,u). Need show. Let δ(v) true shortest path distance. For all vertices, d[v] >= δ(v) (tentative distances are lengths of some path or infinity). Need show extracted u has d[u]=δ(u). Suppose not: δ(u) < d[u]. Consider shortest path P from s to u. Since s settled and u not, path leaves settled set at some edge (x,y) where x settled, y unsettled (maybe y=u or before). At time u extracted, y unsettled. When x was settled, relaxation set d[y] <= δ(x)+w(x,y) = prefix length of P to y <= δ(u) (because nonnegative weights imply prefix length <= total path length? Need nonnegative to ensure prefix <= total). Then d[y] <= δ(u) < d[u]. Since y unsettled, extraction chose min tentative among unsettled, so d[u] <= d[y], contradiction. This is standard. Need handle zero weights okay. If negative edges, prefix may exceed total, so proof fails.\n\nAlternative proof via cut property: for nonnegative weights, any path from settled to unsettled has length at least d[y] etc.\n\nCounterexample with negative edges: Need Dijkstra fails. Simple graph: s->a weight 1, s->b weight 2, a->b weight -2. True shortest s->b = -1 via a. Dijkstra: start s d=0. Extract s, relax a=1,b=2. Extract a (min 1), relax b=-1. But if b already extracted? Wait Dijkstra extracts min unsettled: after s, a=1, b=2, extract a, update b=-1, then extract b. It would still get correct if b not settled before a. Need make b settled before a: set s->b weight 1, s->a weight 2, a->b -2. True b=0 via a. Dijkstra: after s, b=1, a=2. Extract b (settled) with d=1. Then extract a, relax b=-1 but b already settled, not updated. Output b=1 wrong. Graph: s->a 2, s->b 1, a->b -2. Nonnegative? no. Dijkstra fails. Need maybe undirected? Directed enough. Could also include cycle negative? But simple.\n\nNeed compare Bellman-Ford: handles negative weights (no negative cycles reachable from source) by relaxing all edges |V|-1 times. Complexity O(VE), can detect negative cycles. Dijkstra O((V+E) log V) with binary heap, O(E+V log V) Fibonacci, faster but requires nonnegative. Bellman-Ford dynamic programming: after k iterations, distances are shortest paths using at most k edges; after V-1 all simple paths. Negative edges okay because no greedy settlement; can improve settled vertices. If negative cycle reachable, shortest paths undefined (-infinity); BF detects if another relaxation possible.\n\nNeed maybe include example identifier: 29. Could title \"Example 29\".\n\nNeed ensure proof rigorous. Let's craft final with sections.\n\nPotential nuance: Dijkstra with nonnegative weights works for directed graphs with nonnegative weights; undirected with negative edge creates negative 2-cycle? But not needed.\n\nProof details:\nDefinitions: G=(V,E), w:E->R_{>=0}, source s. δ(v)=min length of path s to v, ∞ if unreachable. Dijkstra maintains d[v]. Invariant 1: d[v] is length of some s-v path (or ∞). Invariant 2: for settled S, d[v]=δ(v). Invariant 3: for unsettled v, d[v] >= δ(v). Actually d[v] >= δ(v) always because d is path length. Need show extraction.\n\nLet's write proof:\nAt each iteration choose u = argmin_{v∉S} d[v]. Claim d[u]=δ(u). Suppose δ(u)