{"case_id": "challenge-coding-001", "suite": "coding", "case_version": "0.2.0", "tags": ["python", "parser", "state"], "scorer": "code_python_tests", "expected": {"tests": ["lines = ['# comment', '[db]', ' host = localhost ', 'port=5432', '', '[feature]', 'enabled=true', 'enabled = false']\nassert solution.solve(lines) == {'db': {'host': 'localhost', 'port': '5432'}, 'feature': {'enabled': 'false'}}", "assert solution.solve(['[x]', 'a=1', 'a=2', '[y]', 'b = hi']) == {'x': {'a': '2'}, 'y': {'b': 'hi'}}"], "timeout_seconds": 3}, "model": "minimax/minimax-m3", "provider": null, "prompt": "只输出 Python 代码，不要解释。实现 solve(lines: list[str]) -> dict。lines 是简化 INI：空行和以 # 开头的行忽略；[section] 开始新 section；key=value 写入当前 section；key/value 两侧空白要去掉；同一 section 内重复 key 后者覆盖前者。返回嵌套 dict。", "parameters": {"temperature": 0, "max_tokens": 16384, "seed": null}, "prompt_hash": "2ca132befd6923601a17f24e5efb427ea787e3ae63ae265dd45f0c700cd895d6", "started_at": "2026-06-16T11:14:57.320964+00:00", "latency_ms": 4921, "output": "```python\ndef solve(lines: list[str]) -> dict:\n result = {}\n current_section = None\n for line in lines:\n stripped = line.strip()\n if not stripped or stripped.startswith('#'):\n continue\n if stripped.startswith('[') and stripped.endswith(']'):\n current_section = stripped[1:-1].strip()\n if current_section not in result:\n result[current_section] = {}\n elif '=' in stripped and current_section is not None:\n key, _, value = stripped.partition('=')\n result[current_section][key.strip()] = value.strip()\n return result\n```", "usage": {"prompt_tokens": 263, "completion_tokens": 234, "total_tokens": 497, "cost": 0.00033234, "is_byok": false, "prompt_tokens_details": {"cached_tokens": 114, "cache_write_tokens": 0, "audio_tokens": 0, "video_tokens": 0}, "cost_details": {"upstream_inference_cost": 0.00033234, "upstream_inference_prompt_cost": 5.154e-05, "upstream_inference_completions_cost": 0.0002808}, "completion_tokens_details": {"reasoning_tokens": 121, "image_tokens": 0, "audio_tokens": 0}}, "response_id": "gen-1781608499-Zq4nv06MitRvIYVS2Nwv", "finish_reason": "stop", "score": {"passed": true, "score": 1.0, "details": {"returncode": 0, "stdout": "", "stderr": ""}}, "error": null, "rescored_from": "/Users/dracohu/REPO/dracobench/runs/v0.3-challenge100-minimax-minimax-m3-ability16384.jsonl", "scoring_policy": "text_rules_length_diagnostic_only+v03_alias_relaxations_round2"} {"case_id": "challenge-coding-002", "suite": "coding", "case_version": "0.2.0", "tags": ["python", "ledger", "state"], "scorer": "code_python_tests", "expected": {"tests": ["events = ['a credit 100', 'b debit 40', 'a credit 999', 'rev b', 'c debit 10']\nassert solution.solve(events) == 90", "events = ['x debit 5', 'rev y', 'rev x', 'rev x', 'z credit 7']\nassert solution.solve(events) == 7"], "timeout_seconds": 3}, "model": "minimax/minimax-m3", "provider": null, "prompt": "只输出 Python 代码，不要解释。实现 solve(events: list[str]) -> int。事件格式：\" credit \" 增加余额，\" debit \" 减少余额，\"rev \" 撤销此前尚未撤销过的同 id 事件。重复交易 id 只保留第一次，未知撤销忽略。返回最终余额。", "parameters": {"temperature": 0, "max_tokens": 16384, "seed": null}, "prompt_hash": "3d2d5a46a600444476b5ddfd04634035ff394bcb76fcd230d785597c9cf295ae", "started_at": "2026-06-16T11:15:02.284854+00:00", "latency_ms": 117009, "output": "```python\ndef solve(events: list[str]) -> int:\n balance = 0\n seen = set()\n effects = {}\n reversed_set = set()\n \n for event in events:\n parts = event.split()\n if parts[0] == \"rev\":\n eid = parts[1]\n if eid in effects and eid not in reversed_set:\n balance -= effects[eid]\n reversed_set.add(eid)\n else:\n eid = parts[0]\n if eid not in seen:\n seen.add(eid)\n amount = int(parts[2])\n if parts[1] == \"credit\":\n effects[eid] = amount\n balance += amount\n else:\n effects[eid] = -amount\n balance -= amount\n \n return balance\n```", "usage": {"prompt_tokens": 262, "completion_tokens": 1310, "total_tokens": 1572, "cost": 0.0033012, "is_byok": false, "prompt_tokens_details": {"cached_tokens": 0, "cache_write_tokens": 0, "audio_tokens": 0, "video_tokens": 0}, "cost_details": {"upstream_inference_cost": 0.0033012, "upstream_inference_prompt_cost": 0.0001572, "upstream_inference_completions_cost": 0.003144}, "completion_tokens_details": {"reasoning_tokens": 1255, "image_tokens": 0, "audio_tokens": 0}}, "response_id": "gen-1781608504-o0LHBy4rIJYgE0vjSeg6", "finish_reason": "stop", "score": {"passed": true, "score": 1.0, "details": {"returncode": 0, "stdout": "", "stderr": ""}}, "error": null, "rescored_from": "/Users/dracohu/REPO/dracobench/runs/v0.3-challenge100-minimax-minimax-m3-ability16384.jsonl", "scoring_policy": "text_rules_length_diagnostic_only+v03_alias_relaxations_round2"} {"case_id": "challenge-coding-003", "suite": "coding", "case_version": "0.2.0", "tags": ["python", "topological-sort"], "scorer": "code_python_tests", "expected": {"tests": ["deps = {'build': ['lint', 'test'], 'test': ['install'], 'lint': ['install']}\nassert solution.solve(deps) == [['install'], ['lint', 'test'], ['build']]", "assert solution.solve({'a': ['b'], 'b': ['a']}) == []", "assert solution.solve({'deploy': ['build'], 'build': []}) == [['build'], ['deploy']]"], "timeout_seconds": 3}, "model": "minimax/minimax-m3", "provider": null, "prompt": "只输出 Python 代码，不要解释。实现 solve(deps: dict[str, list[str]]) -> list[list[str]]。deps 表示 task -> prerequisites。返回可并行执行的拓扑层，每层任务按字典序排序；如果存在环，返回空列表。所有只出现在 prerequisites 中的任务也要包含。", "parameters": {"temperature": 0, "max_tokens": 16384, "seed": null}, "prompt_hash": "413c5b602b0fd1ae1e9c3a49b09ecf03c61769fce4b34dbd96b5d8c6a2c68f90", "started_at": "2026-06-16T11:16:59.347501+00:00", "latency_ms": 13617, "output": "```python\ndef solve(deps: dict[str, list[str]]) -> list[list[str]]:\n all_tasks = set(deps.keys())\n for prereqs in deps.values():\n all_tasks.update(prereqs)\n \n in_degree = {task: 0 for task in all_tasks}\n adjacency = {task: [] for task in all_tasks}\n \n for task, prereqs in deps.items():\n in_degree[task] = len(prereqs)\n for prereq in prereqs:\n adjacency[prereq].append(task)\n \n layers = []\n remaining = set(all_tasks)\n \n while remaining:\n current_layer = sorted(t for t in remaining if in_degree[t] == 0)\n if not current_layer:\n return []\n layers.append(current_layer)\n for t in current_layer:\n remaining.discard(t)\n for neighbor in adjacency[t]:\n in_degree[neighbor] -= 1\n \n return layers\n```", "usage": {"prompt_tokens": 253, "completion_tokens": 921, "total_tokens": 1174, "cost": 0.00115374, "is_byok": false, "prompt_tokens_details": {"cached_tokens": 114, "cache_write_tokens": 0, "audio_tokens": 0, "video_tokens": 0}, "cost_details": {"upstream_inference_cost": 0.00115374, "upstream_inference_prompt_cost": 4.854e-05, "upstream_inference_completions_cost": 0.0011052}, "completion_tokens_details": {"reasoning_tokens": 704, "image_tokens": 0, "audio_tokens": 0}}, "response_id": "gen-1781608621-T3zsDTIRONiybtaxAkps", "finish_reason": "stop", "score": {"passed": true, "score": 1.0, "details": {"returncode": 0, "stdout": "", "stderr": ""}}, "error": null, "rescored_from": "/Users/dracohu/REPO/dracobench/runs/v0.3-challenge100-minimax-minimax-m3-ability16384.jsonl", "scoring_policy": "text_rules_length_diagnostic_only+v03_alias_relaxations_round2"} {"case_id": "challenge-coding-004", "suite": "coding", "case_version": "0.2.0", "tags": ["python", "lru"], "scorer": "code_python_tests", "expected": {"tests": ["ops = ['put a 1', 'put b 2', 'get a', 'put c 3', 'get b', 'get a', 'get c']\nassert solution.solve(2, ops) == [1, -1, 1, 3]", "assert solution.solve(0, ['put a 1', 'get a']) == [-1]", "assert solution.solve(1, ['put a 1', 'put a 2', 'get a']) == [2]"], "timeout_seconds": 3}, "model": "minimax/minimax-m3", "provider": null, "prompt": "只输出 Python 代码，不要解释。实现 solve(capacity: int, ops: list[str]) -> list[int]，模拟 LRU cache。ops 包含 \"put key value\" 和 \"get key\"。get 返回 value，不存在返回 -1。put 已存在 key 要更新并变成最近使用。容量为 0 时所有 get 都返回 -1。", "parameters": {"temperature": 0, "max_tokens": 16384, "seed": null}, "prompt_hash": "840e7b4d0c404f3d7b34ebc9931e87612608d6339755d5f1066fda55bd957375", "started_at": "2026-06-16T11:17:13.014638+00:00", "latency_ms": 58085, "output": "```python\nfrom collections import OrderedDict\n\ndef solve(capacity: int, ops: list[str]) -> list[int]:\n cache = OrderedDict()\n result = []\n \n for op in ops:\n parts = op.split()\n if parts[0] == \"put\":\n if capacity == 0:\n continue\n key = int(parts[1])\n value = int(parts[2])\n if key in cache:\n cache[key] = value\n cache.move_to_end(key)\n else:\n if len(cache) >= capacity:\n cache.popitem(last=False)\n cache[key] = value\n else:\n key = int(parts[1])\n if capacity == 0 or key not in cache:\n result.append(-1)\n else:\n cache.move_to_end(key)\n result.append(cache[key])\n \n return result\n```", "usage": {"prompt_tokens": 264, "completion_tokens": 1123, "total_tokens": 1387, "cost": 0.0028536, "is_byok": false, "prompt_tokens_details": {"cached_tokens": 0, "cache_write_tokens": 0, "audio_tokens": 0, "video_tokens": 0}, "cost_details": {"upstream_inference_cost": 0.0028536, "upstream_inference_prompt_cost": 0.0001584, "upstream_inference_completions_cost": 0.0026952}, "completion_tokens_details": {"reasoning_tokens": 1099, "image_tokens": 0, "audio_tokens": 0}}, "response_id": "gen-1781608635-Ye6vuK9OVDESQtzkCpIc", "finish_reason": "stop", "score": {"passed": false, "score": 0.0, "details": {"returncode": 1, "stdout": "", "stderr": "Traceback (most recent call last):\n File \"/var/folders/x_/4cht1kfj1_j31gdv5w01sgmw0000gn/T/dracobench-code-g1kcg8yb/test_solution.py\", line 4, in \n assert solution.solve(2, ops) == [1, -1, 1, 3]\n ^^^^^^^^^^^^^^^^^^^^^^\n File \"/private/var/folders/x_/4cht1kfj1_j31gdv5w01sgmw0000gn/T/dracobench-code-g1kcg8yb/solution.py\", line 12, in solve\n key = int(parts[1])\n ^^^^^^^^^^^^^\nValueError: invalid literal for int() with base 10: 'a'\n"}}, "error": null, "rescored_from": "/Users/dracohu/REPO/dracobench/runs/v0.3-challenge100-minimax-minimax-m3-ability16384.jsonl", "scoring_policy": "text_rules_length_diagnostic_only+v03_alias_relaxations_round2"} {"case_id": "challenge-coding-005", "suite": "coding", "case_version": "0.2.0", "tags": ["python", "json", "copy"], "scorer": "code_python_tests", "expected": {"tests": ["base = {'a': 1, 'b': {'x': 1, 'y': 2}, 'c': 3}\npatch = {'b': {'y': None, 'z': 4}, 'c': None}\nassert solution.solve(base, patch) == {'a': 1, 'b': {'x': 1, 'z': 4}}\nassert base == {'a': 1, 'b': {'x': 1, 'y': 2}, 'c': 3}", "assert solution.solve({'a': {'b': 1}}, {'a': 5}) == {'a': 5}"], "timeout_seconds": 3}, "model": "minimax/minimax-m3", "provider": null, "prompt": "只输出 Python 代码，不要解释。实现 solve(base: dict, patch: dict) -> dict，执行 JSON Merge Patch：patch 中值为 None 表示删除该 key；两个值都是 dict 时递归合并；其他值覆盖。不能修改输入对象。", "parameters": {"temperature": 0, "max_tokens": 16384, "seed": null}, "prompt_hash": "8dc22406b418d74ea74b50692fe086d4f80748258269dc93f82290e6c344b5a3", "started_at": "2026-06-16T11:18:11.129130+00:00", "latency_ms": 6295, "output": "```python\ndef solve(base: dict, patch: dict) -> dict:\n def _merge(t, p):\n if not isinstance(p, dict):\n return p\n if not isinstance(t, dict):\n t = {}\n out = dict(t)\n for k, v in p.items():\n if v is None:\n out.pop(k, None)\n else:\n out[k] = _merge(out.get(k), v)\n return out\n return _merge(base, patch)\n```", "usage": {"prompt_tokens": 243, "completion_tokens": 298, "total_tokens": 541, "cost": 0.00040314, "is_byok": false, "prompt_tokens_details": {"cached_tokens": 114, "cache_write_tokens": 0, "audio_tokens": 0, "video_tokens": 0}, "cost_details": {"upstream_inference_cost": 0.00040314, "upstream_inference_prompt_cost": 4.554e-05, "upstream_inference_completions_cost": 0.0003576}, "completion_tokens_details": {"reasoning_tokens": 230, "image_tokens": 0, "audio_tokens": 0}}, "response_id": "gen-1781608693-KE6MR9Bj24W5YCAbXD2f", "finish_reason": "stop", "score": {"passed": true, "score": 1.0, "details": {"returncode": 0, "stdout": "", "stderr": ""}}, "error": null, "rescored_from": "/Users/dracohu/REPO/dracobench/runs/v0.3-challenge100-minimax-minimax-m3-ability16384.jsonl", "scoring_policy": "text_rules_length_diagnostic_only+v03_alias_relaxations_round2"} {"case_id": "challenge-coding-006", "suite": "coding", "case_version": "0.2.0", "tags": ["python", "regex", "duration"], "scorer": "code_python_tests", "expected": {"tests": ["assert solution.solve('1h 30m 500ms') == 5400500", "assert solution.solve('2d 1h 2s') == 176402000", "assert solution.solve('100ms 2s 3ms') == 2103"], "timeout_seconds": 3}, "model": "minimax/minimax-m3", "provider": null, "prompt": "只输出 Python 代码，不要解释。实现 solve(text: str) -> int，把时长字符串转换成毫秒。支持整数单位 d、h、m、s、ms，可有空格，单位可重复，顺序任意。例如 \"1h 30m 500ms\"。", "parameters": {"temperature": 0, "max_tokens": 16384, "seed": null}, "prompt_hash": "ce87366a2f6f080a35c0fdd0872b6a8304a8233e0c5c9361c19fec614c0d1fcb", "started_at": "2026-06-16T11:18:17.467805+00:00", "latency_ms": 18655, "output": "```python\nimport re\n\ndef solve(text: str) -> int:\n units = {\n 'd': 86400000,\n 'h': 3600000,\n 'm': 60000,\n 's': 1000,\n 'ms': 1\n }\n total = 0\n pattern = r'(-?\\d+)\\s*(ms|[dhms])'\n for match in re.finditer(pattern, text):\n value = int(match.group(1))\n unit = match.group(2)\n total += value * units[unit]\n return total\n```", "usage": {"prompt_tokens": 247, "completion_tokens": 1204, "total_tokens": 1451, "cost": 0.00149154, "is_byok": false, "prompt_tokens_details": {"cached_tokens": 114, "cache_write_tokens": 0, "audio_tokens": 0, "video_tokens": 0}, "cost_details": {"upstream_inference_cost": 0.00149154, "upstream_inference_prompt_cost": 4.674e-05, "upstream_inference_completions_cost": 0.0014448}, "completion_tokens_details": {"reasoning_tokens": 999, "image_tokens": 0, "audio_tokens": 0}}, "response_id": "gen-1781608699-qI6ysKXwueKpxVxrKtXw", "finish_reason": "stop", "score": {"passed": true, "score": 1.0, "details": {"returncode": 0, "stdout": "", "stderr": ""}}, "error": null, "rescored_from": "/Users/dracohu/REPO/dracobench/runs/v0.3-challenge100-minimax-minimax-m3-ability16384.jsonl", "scoring_policy": "text_rules_length_diagnostic_only+v03_alias_relaxations_round2"} {"case_id": "challenge-coding-007", "suite": "coding", "case_version": "0.2.0", "tags": ["python", "parser", "quotes"], "scorer": "code_python_tests", "expected": {"tests": ["assert solution.solve('{a:[(1)]}') is True", "assert solution.solve(\"{'x': '(]'}\") is True", "assert solution.solve(\"['unterminated]\") is False", "assert solution.solve('([)]') is False", "assert solution.solve(\"\\\"paren \\\\\\\" ) still quoted\\\"\") is True"], "timeout_seconds": 3}, "model": "minimax/minimax-m3", "provider": null, "prompt": "只输出 Python 代码，不要解释。实现 solve(s: str) -> bool，检查括号 ()[]{} 是否配对。单引号或双引号内部的括号忽略；反斜杠会转义引号。输入字符串可以包含普通字符。", "parameters": {"temperature": 0, "max_tokens": 16384, "seed": null}, "prompt_hash": "2f2e8f01533244c8104f74b3f9c9bc261154f2ff4c46c03a031258f6a5706f9f", "started_at": "2026-06-16T11:18:36.163609+00:00", "latency_ms": 53022, "output": "```python\ndef solve(s: str) -> bool:\n stack = []\n i = 0\n n = len(s)\n quote = None\n while i < n:\n c = s[i]\n if quote:\n if c == '\\\\':\n i += 2\n continue\n if c == quote:\n quote = None\n elif c in ('\"', \"'\"):\n quote = c\n elif c in '([{':\n stack.append(c)\n elif c in ')]}':\n if not stack:\n return False\n top = stack.pop()\n if (c == ')' and top != '(') or (c == ']' and top != '[') or (c == '}' and top != '{'):\n return False\n i += 1\n return not stack and quote is None\n```", "usage": {"prompt_tokens": 244, "completion_tokens": 4203, "total_tokens": 4447, "cost": 0.00508944, "is_byok": false, "prompt_tokens_details": {"cached_tokens": 114, "cache_write_tokens": 0, "audio_tokens": 0, "video_tokens": 0}, "cost_details": {"upstream_inference_cost": 0.00508944, "upstream_inference_prompt_cost": 4.584e-05, "upstream_inference_completions_cost": 0.0050436}, "completion_tokens_details": {"reasoning_tokens": 3988, "image_tokens": 0, "audio_tokens": 0}}, "response_id": "gen-1781608718-djTgm6GzTNShilyKWgMn", "finish_reason": "stop", "score": {"passed": true, "score": 1.0, "details": {"returncode": 0, "stdout": "", "stderr": ""}}, "error": null, "rescored_from": "/Users/dracohu/REPO/dracobench/runs/v0.3-challenge100-minimax-minimax-m3-ability16384.jsonl", "scoring_policy": "text_rules_length_diagnostic_only+v03_alias_relaxations_round2"} {"case_id": "challenge-coding-008", "suite": "coding", "case_version": "0.2.0", "tags": ["python", "dedupe", "sorting"], "scorer": "code_python_tests", "expected": {"tests": ["rows = [{'user':'b','ts':1,'value':'old'}, {'user':'a','ts':2,'value':'x'}, {'user':'b','ts':3,'value':'new'}]\nassert solution.solve(rows) == [{'user':'a','ts':2,'value':'x'}, {'user':'b','ts':3,'value':'new'}]", "rows = [{'user':'a','ts':1,'value':'first'}, {'user':'a','ts':1,'value':'second'}]\nassert solution.solve(rows) == [{'user':'a','ts':1,'value':'second'}]"], "timeout_seconds": 3}, "model": "minimax/minimax-m3", "provider": null, "prompt": "只输出 Python 代码，不要解释。实现 solve(rows: list[dict]) -> list[dict]。每行有 user、ts、value。对每个 user 保留 ts 最大的一行；若 ts 相同，保留在输入中更靠后的行。输出按 user 字典序排序。", "parameters": {"temperature": 0, "max_tokens": 16384, "seed": null}, "prompt_hash": "2a9a2d40a756627e38d28a8ac6afc98311acbafa718d7b3aaaecbedb9f020179", "started_at": "2026-06-16T11:19:29.229953+00:00", "latency_ms": 8547, "output": "```python\ndef solve(rows):\n best = {}\n for row in rows:\n u = row['user']\n t = row['ts']\n if u not in best or t >= best[u]['ts']:\n best[u] = row\n return [best[u] for u in sorted(best)]\n``````python\ndef solve(rows):\n best = {}\n for row in rows:\n u = row['user']\n t = row['ts']\n if u not in best or t >= best[u]['ts']:\n best[u] = row\n return [best[u] for u in sorted(best)]\n```", "usage": {"prompt_tokens": 253, "completion_tokens": 361, "total_tokens": 614, "cost": 0.000674436, "is_byok": false, "prompt_tokens_details": {"cached_tokens": 114, "cache_write_tokens": 0, "audio_tokens": 0, "video_tokens": 0}, "cost_details": {"upstream_inference_cost": 0.000674436, "upstream_inference_prompt_cost": 6.7956e-05, "upstream_inference_completions_cost": 0.00060648}, "completion_tokens_details": {"reasoning_tokens": 292, "image_tokens": 0, "audio_tokens": 0}}, "response_id": "gen-1781608771-pBXnjaIFolcV412mIRwW", "finish_reason": "stop", "score": {"passed": true, "score": 1.0, "details": {"returncode": 0, "stdout": "", "stderr": ""}}, "error": null, "rescored_from": "/Users/dracohu/REPO/dracobench/runs/v0.3-challenge100-minimax-minimax-m3-ability16384.jsonl", "scoring_policy": "text_rules_length_diagnostic_only+v03_alias_relaxations_round2"} {"case_id": "challenge-debugging-001", "suite": "debugging", "case_version": "0.2.0", "tags": ["python", "iterator-default", "debugging"], "scorer": "regex", "expected": {"pattern": "FIX:\\s*`?\\s*def\\s+summarize\\(items=None\\)\\s*:", "flags": "is"}, "model": "minimax/minimax-m3", "provider": null, "prompt": "下面代码在 total 之后再次读取数据会变成空列表。请指出根因，并给出最小修复。最后单独一行写 FIX: <修复后的第一行函数定义>。\n\n```python\ndef summarize(items=iter([1, 2, 3])):\n return sum(items)\n```", "parameters": {"temperature": 0, "max_tokens": 16384, "seed": null}, "prompt_hash": "7b02a617eebe49b179cee84c9b8347c6bebe382f42a2b199d92b3aa9ed844102", "started_at": "2026-06-16T11:19:37.821816+00:00", "latency_ms": 24993, "output": "**根因**\n\nPython 的默认参数在函数定义时**只求值一次**。`iter([1, 2, 3])` 在 `def` 执行时创建了一个迭代器对象，并被绑定到形参 `items`。之后每次无参调用 `summarize()`，复用的都是**同一个**迭代器实例——第一次调用后它已被 `sum` 耗尽，再次调用时 `sum` 拿到的是空迭代器，返回 0。\n\n这是经典的\"可变/一次性默认参数\"陷阱（与 `def f(x=[])` 同理）。\n\n**最小修复**\n\n用哨兵 `None`，把迭代器的创建挪进函数体，每次调用都得到新迭代器：\n\n```python\ndef summarize(items=None):\n if items is None:\n items = iter([1, 2, 3])\n return sum(items)\n```\n\nFIX: def summarize(items=None):", "usage": {"prompt_tokens": 254, "completion_tokens": 654, "total_tokens": 908, "cost": 0.00082212, "is_byok": false, "prompt_tokens_details": {"cached_tokens": 162, "cache_write_tokens": 0, "audio_tokens": 0, "video_tokens": 0}, "cost_details": {"upstream_inference_cost": 0.00082212, "upstream_inference_prompt_cost": 3.732e-05, "upstream_inference_completions_cost": 0.0007848}, "completion_tokens_details": {"reasoning_tokens": 548, "image_tokens": 0, "audio_tokens": 0}}, "response_id": "gen-1781608780-2hMHkxUZhbIHpJC4DZeb", "finish_reason": "stop", "score": {"passed": true, "score": 1.0, "details": {"pattern": "FIX:\\s*`?\\s*def\\s+summarize\\(items=None\\)\\s*:"}}, "error": null, "rescored_from": "/Users/dracohu/REPO/dracobench/runs/v0.3-challenge100-minimax-minimax-m3-ability16384.jsonl", "scoring_policy": "text_rules_length_diagnostic_only+v03_alias_relaxations_round2"} {"case_id": "challenge-debugging-002", "suite": "debugging", "case_version": "0.2.0", "tags": ["python", "defaultdict", "debugging"], "scorer": "regex", "expected": {"pattern": "FIX:\\s*`?\\s*counts\\s*=\\s*defaultdict\\(lambda\\s*:\\s*\\{'seen'\\s*:\\s*\\[\\]\\}\\)\\s*`?", "flags": "is"}, "model": "minimax/minimax-m3", "provider": null, "prompt": "下面代码期望统计每个用户的事件数，但所有用户计数会互相影响。请指出根因，并给出最小修复。最后单独一行写 FIX: <修复后的 defaultdict 初始化行>。\n\n```python\nfrom collections import defaultdict\nshared = {'seen': []}\ncounts = defaultdict(lambda: shared)\n\ndef add(user, event):\n counts[user]['seen'].append(event)\n return counts[user]\n```", "parameters": {"temperature": 0, "max_tokens": 16384, "seed": null}, "prompt_hash": "fc1eb426cf811456432a61a306862f8bf804417edb8c8fca6dd3b86c1f9a9940", "started_at": "2026-06-16T11:20:02.825663+00:00", "latency_ms": 8370, "output": "**根因**\n\n`defaultdict(lambda: shared)` 的 factory 每次都返回**同一个** `shared` 字典对象（包括其中那个 `seen` 列表），所以无论 key 是哪个 user，`counts[user]` 都指向同一个 dict / 同一个 list。`append` 操作因此在所有用户间共享，于是计数互相\"污染\"。\n\n**最小修复**\n\n让 factory 每次返回**新的** dict：\n\n```python\ncounts = defaultdict(lambda: {'seen': []})\n```\n\nFIX: counts = defaultdict(lambda: {'seen': []})", "usage": {"prompt_tokens": 277, "completion_tokens": 364, "total_tokens": 641, "cost": 0.00049254, "is_byok": false, "prompt_tokens_details": {"cached_tokens": 114, "cache_write_tokens": 0, "audio_tokens": 0, "video_tokens": 0}, "cost_details": {"upstream_inference_cost": 0.00049254, "upstream_inference_prompt_cost": 5.574e-05, "upstream_inference_completions_cost": 0.0004368}, "completion_tokens_details": {"reasoning_tokens": 297, "image_tokens": 0, "audio_tokens": 0}}, "response_id": "gen-1781608805-3Td7UWZqt09aPRgzUelz", "finish_reason": "stop", "score": {"passed": true, "score": 1.0, "details": {"pattern": "FIX:\\s*`?\\s*counts\\s*=\\s*defaultdict\\(lambda\\s*:\\s*\\{'seen'\\s*:\\s*\\[\\]\\}\\)\\s*`?"}}, "error": null, "rescored_from": "/Users/dracohu/REPO/dracobench/runs/v0.3-challenge100-minimax-minimax-m3-ability16384.jsonl", "scoring_policy": "text_rules_length_diagnostic_only+v03_alias_relaxations_round2"} {"case_id": "challenge-debugging-003", "suite": "debugging", "case_version": "0.2.0", "tags": ["python", "off-by-one", "debugging"], "scorer": "regex", "expected": {"pattern": "FIX:\\s*`?\\s*for\\s+start\\s+in\\s+range\\(0,\\s*len\\(items\\),\\s*page_size\\)\\s*:", "flags": "is"}, "model": "minimax/minimax-m3", "provider": null, "prompt": "下面分页代码会漏掉最后不足 page_size 的一页。请指出 bug，并给出最小修复。最后单独一行写 FIX: <修复后的 range 行>。\n\n```python\ndef pages(items, page_size):\n out = []\n for start in range(0, len(items) - page_size, page_size):\n out.append(items[start:start + page_size])\n return out\n```", "parameters": {"temperature": 0, "max_tokens": 16384, "seed": null}, "prompt_hash": "c6fe209d60968a3b2d253a065c8ce8255865f73575665a685d957cb52ce82011", "started_at": "2026-06-16T11:20:11.207308+00:00", "latency_ms": 23829, "output": "# Bug 分析\n\n原代码用 `range(0, len(items) - page_size, page_size)`，当 `len(items)` 不是 `page_size` 的整数倍时，最后那页不满 `page_size`，但循环已经结束，导致遗漏。\n\n例如 `len(items)=10, page_size=3`：\n- 原 range 得到 `0, 3, 6`\n- 起始 9 那一页（仅 1 个元素）被漏掉\n\n# 最小修复\n\n去掉 `- page_size`。这样当 `len(items)` 正好是 `page_size` 倍数时，最后一个 `start` 恰好是 `len(items) - page_size`，切片非空，不会产生空页；而有余数时，`start = len(items) - r`（r 为余数）会被包含进来。\n\n```python\ndef pages(items, page_size):\n out = []\n for start in range(0, len(items), page_size):\n out.append(items[start:start + page_size])\n return out\n```\n\nFIX: `for start in range(0, len(items), page_size):`", "usage": {"prompt_tokens": 276, "completion_tokens": 1598, "total_tokens": 1874, "cost": 0.00197304, "is_byok": false, "prompt_tokens_details": {"cached_tokens": 114, "cache_write_tokens": 0, "audio_tokens": 0, "video_tokens": 0}, "cost_details": {"upstream_inference_cost": 0.00197304, "upstream_inference_prompt_cost": 5.544e-05, "upstream_inference_completions_cost": 0.0019176}, "completion_tokens_details": {"reasoning_tokens": 1178, "image_tokens": 0, "audio_tokens": 0}}, "response_id": "gen-1781608813-i1mSdLIyQfLWQMoDe8CQ", "finish_reason": "stop", "score": {"passed": true, "score": 1.0, "details": {"pattern": "FIX:\\s*`?\\s*for\\s+start\\s+in\\s+range\\(0,\\s*len\\(items\\),\\s*page_size\\)\\s*:"}}, "error": null, "rescored_from": "/Users/dracohu/REPO/dracobench/runs/v0.3-challenge100-minimax-minimax-m3-ability16384.jsonl", "scoring_policy": "text_rules_length_diagnostic_only+v03_alias_relaxations_round2"} {"case_id": "challenge-debugging-004", "suite": "debugging", "case_version": "0.2.0", "tags": ["python", "sorting", "debugging"], "scorer": "regex", "expected": {"pattern": "FIX:\\s*`?\\s*return\\s+sorted\\(ids,\\s*key\\s*=\\s*int\\)\\s*`?", "flags": "is"}, "model": "minimax/minimax-m3", "provider": null, "prompt": "下面代码期望把数字字符串按数值排序，但结果是字典序。请指出 bug，并给出最小修复。最后单独一行写 FIX: <修复后的 return 行>。\n\n```python\ndef order(ids):\n return sorted(ids)\n```", "parameters": {"temperature": 0, "max_tokens": 16384, "seed": null}, "prompt_hash": "b3f10943f43eaa04524058b0dd7fb30cbd21740003559e79de619ae210a697e4", "started_at": "2026-06-16T11:20:35.052922+00:00", "latency_ms": 8926, "output": "## Bug 分析\n\n`sorted(ids)` 在没有指定 `key` 时，会对字符串进行**字典序**（逐字符比较）排序：\n\n- `\"10\"` 和 `\"2\"` 比较时，比较的是首字符 `'1'` 和 `'2'`，由于 `'1' < '2'`，所以 `\"10\"` 排在 `\"2\"` 前面。\n\n## 最小修复\n\n传入 `key=int`，让排序时把每个元素先转成整数再比较：\n\n```python\ndef order(ids):\n return sorted(ids, key=int)\n```\n\n## 验证\n\n```python\norder([\"10\", \"2\", \"1\", \"21\"])\n# 字典序：['1', '10', '2', '21'] ❌\n# 数值序：['1', '2', '10', '21'] ✅\n```\n\nFIX: ` return sorted(ids, key=int)`## Bug 分析\n\n`sorted(ids)` 在没有指定 `key` 时，会对字符串进行**字典序**（逐字符比较）排序：\n\n- `\"10\"` 和 `\"2\"` 比较时，比较的是首字符 `'1'` 和 `'2'`，由于 `'1' < '2'`，所以 `\"10\"` 排在 `\"2\"` 前面。\n\n## 最小修复\n\n传入 `key=int`，让排序时把每个元素先转成整数再比较：\n\n```python\ndef order(ids):\n return sorted(ids, key=int)\n```\n\n## 验证\n\n```python\norder([\"10\", \"2\", \"1\", \"21\"])\n# 字典序：['1', '10', '2', '21'] ❌\n# 数值序：['1', '2', '10', '21'] ✅\n```\n\nFIX: ` return sorted(ids, key=int)`", "usage": {"prompt_tokens": 240, "completion_tokens": 394, "total_tokens": 634, "cost": 0.000724416, "is_byok": false, "prompt_tokens_details": {"cached_tokens": 114, "cache_write_tokens": 0, "audio_tokens": 0, "video_tokens": 0}, "cost_details": {"upstream_inference_cost": 0.000724416, "upstream_inference_prompt_cost": 6.2496e-05, "upstream_inference_completions_cost": 0.00066192}, "completion_tokens_details": {"reasoning_tokens": 223, "image_tokens": 0, "audio_tokens": 0}}, "response_id": "gen-1781608837-8fBh4bNPcjLH235JSb2D", "finish_reason": "stop", "score": {"passed": true, "score": 1.0, "details": {"pattern": "FIX:\\s*`?\\s*return\\s+sorted\\(ids,\\s*key\\s*=\\s*int\\)\\s*`?"}}, "error": null, "rescored_from": "/Users/dracohu/REPO/dracobench/runs/v0.3-challenge100-minimax-minimax-m3-ability16384.jsonl", "scoring_policy": "text_rules_length_diagnostic_only+v03_alias_relaxations_round2"} {"case_id": "challenge-debugging-005", "suite": "debugging", "case_version": "0.2.0", "tags": ["python", "memoization", "debugging"], "scorer": "regex", "expected": {"pattern": "FIX:\\s*`?\\s*key\\s*=\\s*\\(fn\\.__name__,\\s*args,\\s*(?:tuple\\(sorted\\(kwargs\\.items\\(\\)\\)\\)|frozenset\\(kwargs\\.items\\(\\)\\))\\)\\s*`?", "flags": "is"}, "model": "minimax/minimax-m3", "provider": null, "prompt": "下面缓存装饰器会把不同参数的调用混在一起。请指出 bug，并给出最小修复。最后单独一行写 FIX: <修复后的 key 赋值行>。\n\n```python\ncache = {}\ndef memo(fn):\n def wrapper(*args, **kwargs):\n key = fn.__name__\n if key not in cache:\n cache[key] = fn(*args, **kwargs)\n return cache[key]\n return wrapper\n```", "parameters": {"temperature": 0, "max_tokens": 16384, "seed": null}, "prompt_hash": "fdaf2df9bffeacf442086f01d24eb1fce336e874cebec386f24acd9175c42fc6", "started_at": "2026-06-16T11:20:43.993284+00:00", "latency_ms": 11867, "output": "**Bug 分析**\n\n`key` 只用了函数名 `fn.__name__`，所以 `fn(1)` 之后 `fn(2)` 也会命中 `fn(1)` 的缓存——所有实参的调用结果被混在同一个槽里。args / kwargs 都没有进入 key，且 `kwargs` 是 dict，本身不可哈希。\n\n**最小修复**\n\n把 `args` 和排序后的 `kwargs` 项一起纳入 key（这样元组可哈希）：\n\n```python\ndef wrapper(*args, **kwargs):\n key = (fn.__name__, args, tuple(sorted(kwargs.items())))\n if key not in cache:\n cache[key] = fn(*args, **kwargs)\n return cache[key]\n```\n\n注意：`cache` 仍然是一个普通 dict，key 现在是元组（`fn.__name__` + `args` + 排序后的 kwargs 项），不同实参组合会产生不同 key，不再串味。\n\nFIX: `key = (fn.__name__, args, tuple(sorted(kwargs.items())))`", "usage": {"prompt_tokens": 281, "completion_tokens": 625, "total_tokens": 906, "cost": 0.00080694, "is_byok": false, "prompt_tokens_details": {"cached_tokens": 114, "cache_write_tokens": 0, "audio_tokens": 0, "video_tokens": 0}, "cost_details": {"upstream_inference_cost": 0.00080694, "upstream_inference_prompt_cost": 5.694e-05, "upstream_inference_completions_cost": 0.00075}, "completion_tokens_details": {"reasoning_tokens": 459, "image_tokens": 0, "audio_tokens": 0}}, "response_id": "gen-1781608846-2W9bbh6PzMBb9QU9JJat", "finish_reason": "stop", "score": {"passed": true, "score": 1.0, "details": {"pattern": "FIX:\\s*`?\\s*key\\s*=\\s*\\(fn\\.__name__,\\s*args,\\s*(?:tuple\\(sorted\\(kwargs\\.items\\(\\)\\)\\)|frozenset\\(kwargs\\.items\\(\\)\\))\\)\\s*`?"}}, "error": null, "rescored_from": "/Users/dracohu/REPO/dracobench/runs/v0.3-challenge100-minimax-minimax-m3-ability16384.jsonl", "scoring_policy": "text_rules_length_diagnostic_only+v03_alias_relaxations_round2"} {"case_id": "challenge-debugging-006", "suite": "debugging", "case_version": "0.2.0", "tags": ["python", "aliasing", "debugging"], "scorer": "regex", "expected": {"pattern": "FIX:\\s*`?\\s*copied\\s*=\\s*(?:items\\.copy\\(\\)|items\\[:\\]|list\\(items\\))\\s*`?", "flags": "is"}, "model": "minimax/minimax-m3", "provider": null, "prompt": "下面代码期望浅拷贝列表后独立追加，但原列表也被修改。请指出 bug，并给出最小修复。最后单独一行写 FIX: <修复后的 copy 行>。\n\n```python\ndef append_copy(items, value):\n copied = items\n copied.append(value)\n return copied\n```", "parameters": {"temperature": 0, "max_tokens": 16384, "seed": null}, "prompt_hash": "d79d9daa2c034ff1badc1d0951de62e17f2421bac20433e520a48c2992cae000", "started_at": "2026-06-16T11:20:55.875296+00:00", "latency_ms": 8022, "output": "**Bug 分析：**\n\n`copied = items` 并没有创建新列表，只是让 `copied` 和 `items` 指向**同一个**列表对象。后续的 `copied.append(value)` 实际上修改的是这一共享对象，因此原列表 `items` 也会被一起改动。\n\n要得到一份独立的浅拷贝，需要显式复制列表内容。\n\n**最小修复：**\n\n```python\ndef append_copy(items, value):\n copied = items.copy()\n copied.append(value)\n return copied\n```\n\n验证：\n\n```python\noriginal = [1, 2, 3]\nnew = append_copy(original, 99)\nprint(original) # [1, 2, 3] 原列表未受影响\nprint(new) # [1, 2, 3, 99]\n```\n\n另外，等价的写法还有 `list(items)` 或 `items[:]`，效果相同（浅拷贝）。\n\nFIX: copied = items.copy()", "usage": {"prompt_tokens": 253, "completion_tokens": 399, "total_tokens": 652, "cost": 0.00052734, "is_byok": false, "prompt_tokens_details": {"cached_tokens": 114, "cache_write_tokens": 0, "audio_tokens": 0, "video_tokens": 0}, "cost_details": {"upstream_inference_cost": 0.00052734, "upstream_inference_prompt_cost": 4.854e-05, "upstream_inference_completions_cost": 0.0004788}, "completion_tokens_details": {"reasoning_tokens": 214, "image_tokens": 0, "audio_tokens": 0}}, "response_id": "gen-1781608858-vYXoIyHmOPuKnYvN5tZR", "finish_reason": "stop", "score": {"passed": true, "score": 1.0, "details": {"pattern": "FIX:\\s*`?\\s*copied\\s*=\\s*(?:items\\.copy\\(\\)|items\\[:\\]|list\\(items\\))\\s*`?"}}, "error": null, "rescored_from": "/Users/dracohu/REPO/dracobench/runs/v0.3-challenge100-minimax-minimax-m3-ability16384.jsonl", "scoring_policy": "text_rules_length_diagnostic_only+v03_alias_relaxations_round2"} {"case_id": "challenge-debugging-007", "suite": "debugging", "case_version": "0.2.0", "tags": ["python", "json", "debugging"], "scorer": "regex", "expected": {"pattern": "FIX:\\s*`?\\s*data\\s*=\\s*(payload\\s+if\\s+isinstance\\(payload,\\s*dict\\)\\s+else\\s+json\\.loads\\(payload\\)|json\\.loads\\(payload\\)\\s+if\\s+isinstance\\(payload,\\s*\\(str,\\s*bytes,\\s*bytearray\\)\\)\\s+else\\s+payload|json\\.loads\\(payload\\)\\s+if\\s+isinstance\\(payload,\\s*str\\)\\s+else\\s+payload)\\s*`?", "flags": "is"}, "model": "minimax/minimax-m3", "provider": null, "prompt": "下面代码期望解析 JSON 字符串，但传入已经是 dict 时会报错。请指出 bug，并给出最小修复。最后单独一行写 FIX: <修复后的 data 赋值行>。\n\n```python\nimport json\n\ndef load(payload):\n data = json.loads(payload)\n return data['id']\n```", "parameters": {"temperature": 0, "max_tokens": 16384, "seed": null}, "prompt_hash": "dae0481dbfca35623cf6eaf5f44910cc59204edc238411f6ec1221586215b6a3", "started_at": "2026-06-16T11:21:03.912353+00:00", "latency_ms": 6000, "output": "**Bug 分析**\n\n`json.loads()` 严格要求入参为 `str`/`bytes` 等可解析类型，当传入 `dict` 时会抛出 `TypeError: the JSON object must be str, bytes or bytearray, not dict`。\n\n**最小修复**\n\n```python\nimport json\n\ndef load(payload):\n data = payload if isinstance(payload, dict) else json.loads(payload)\n return data['id']\n```\n\nFIX: `data = payload if isinstance(payload, dict) else json.loads(payload)`", "usage": {"prompt_tokens": 254, "completion_tokens": 228, "total_tokens": 482, "cost": 0.00032244, "is_byok": false, "prompt_tokens_details": {"cached_tokens": 114, "cache_write_tokens": 0, "audio_tokens": 0, "video_tokens": 0}, "cost_details": {"upstream_inference_cost": 0.00032244, "upstream_inference_prompt_cost": 4.884e-05, "upstream_inference_completions_cost": 0.0002736}, "completion_tokens_details": {"reasoning_tokens": 139, "image_tokens": 0, "audio_tokens": 0}}, "response_id": "gen-1781608866-n6x5GufTUuX0SZwGclFc", "finish_reason": "stop", "score": {"passed": true, "score": 1.0, "details": {"pattern": "FIX:\\s*`?\\s*data\\s*=\\s*(payload\\s+if\\s+isinstance\\(payload,\\s*dict\\)\\s+else\\s+json\\.loads\\(payload\\)|json\\.loads\\(payload\\)\\s+if\\s+isinstance\\(payload,\\s*\\(str,\\s*bytes,\\s*bytearray\\)\\)\\s+else\\s+payload|json\\.loads\\(payload\\)\\s+if\\s+isinstance\\(payload,\\s*str\\)\\s+else\\s+payload)\\s*`?"}}, "error": null, "rescored_from": "/Users/dracohu/REPO/dracobench/runs/v0.3-challenge100-minimax-minimax-m3-ability16384.jsonl", "scoring_policy": "text_rules_length_diagnostic_only+v03_alias_relaxations_round2"} {"case_id": "challenge-debugging-008", "suite": "debugging", "case_version": "0.2.0", "tags": ["python", "finally", "debugging"], "scorer": "contains_any", "expected": {"answers": ["删除 finally 中的 return None", "去掉 finally 的 return None", "不要在 finally 里 return None", "remove return None", "去掉 finally 中的 return None", "删除或替换 finally 中的 return None", "去掉 finally 中的 `return None`", "删除或替换 finally 中的 `return None`", "删除 finally 块中的 `return None`", "删除 finally 块中的 return None", "删除 finally 中的 `return None`", "删除 `finally` 块中的 `return None`", "删除 `finally` 中的 `return None`", "删除 `finally: return None`", "将 `finally:` 中的 `return None` 删除", "finally 中的 return None 删除", "finally: 中的 return None 删除", "删除 finally 块中的 'return None'", "删除 finally 子句中的 `return None`", "删除 finally 子句中的 return None", "delete the line `return None` inside the finally block", "delete the line return None inside the finally block", "finally 块不应包含 return 语句", "finally 块不应包含 return", "finally 不应包含 return"]}, "model": "minimax/minimax-m3", "provider": null, "prompt": "下面代码期望 finally 后仍保留 try 中返回值，但实际总是返回 None。请指出 bug，并给出最小修复。最后单独一行写 FIX: <要删除或替换的 finally 行说明>。\n\n```python\ndef read_value():\n try:\n return 42\n finally:\n return None\n```", "parameters": {"temperature": 0, "max_tokens": 16384, "seed": null}, "prompt_hash": "3cc6ae8fe4f15528225f040b759f5535e31e0ee3dab226c836ed3b9c19707a57", "started_at": "2026-06-16T11:21:09.926549+00:00", "latency_ms": 16383, "output": "## Bug 分析\n\n在 Python 中，`finally` 块里的 `return` 语句会**覆盖** `try` 块中的返回值。即使 `try` 已经执行了 `return 42`，控制流仍会进入 `finally`，而 `finally` 中的 `return None` 会把返回值替换为 `None`。\n\n## 最小修复\n\n```python\ndef read_value():\n try:\n return 42\n finally:\n pass\n```\n\n`finally` 块通常用于释放资源（关闭文件、锁等），不应包含 `return`。如果确实没有清理工作要做，可以直接省略 `finally`。\n\nFIX: 删除 `return None` 这一行（finally 块不应包含 return 语句，否则会覆盖 try 中的返回值）", "usage": {"prompt_tokens": 253, "completion_tokens": 307, "total_tokens": 560, "cost": 0.0008886, "is_byok": false, "prompt_tokens_details": {"cached_tokens": 0, "cache_write_tokens": 0, "audio_tokens": 0, "video_tokens": 0}, "cost_details": {"upstream_inference_cost": 0.0008886, "upstream_inference_prompt_cost": 0.0001518, "upstream_inference_completions_cost": 0.0007368}, "completion_tokens_details": {"reasoning_tokens": 181, "image_tokens": 0, "audio_tokens": 0}}, "response_id": "gen-1781608872-ZpSGX8SUrkqNfie8IaAx", "finish_reason": "stop", "score": {"passed": true, "score": 1.0, "details": {"matched": ["finally 块不应包含 return 语句", "finally 块不应包含 return"]}}, "error": null, "rescored_from": "/Users/dracohu/REPO/dracobench/runs/v0.3-challenge100-minimax-minimax-m3-ability16384.jsonl", "scoring_policy": "text_rules_length_diagnostic_only+v03_alias_relaxations_round2"} {"case_id": "challenge-reasoning-001", "suite": "reasoning", "case_version": "0.2.0", "tags": ["arithmetic", "multi-step"], "scorer": "exact", "expected": {"answer": "125", "normalize": true}, "model": "minimax/minimax-m3", "provider": null, "prompt": "只回答最终数字：有 A、B、C、D 四个服务。A 每分钟处理 6 个请求，B 比 A 少 2 个，C 是 B 的 2 倍，D 是 A 和 C 的平均值。四个服务 5 分钟一共处理多少请求？", "parameters": {"temperature": 0, "max_tokens": 16384, "seed": null}, "prompt_hash": "06f9a3eb3666e5854b2b742553674373622a42e326be166231a39c67b5604810", "started_at": "2026-06-16T11:21:26.323731+00:00", "latency_ms": 2874, "output": "125", "usage": {"prompt_tokens": 247, "completion_tokens": 126, "total_tokens": 373, "cost": 0.00019794, "is_byok": false, "prompt_tokens_details": {"cached_tokens": 114, "cache_write_tokens": 0, "audio_tokens": 0, "video_tokens": 0}, "cost_details": {"upstream_inference_cost": 0.00019794, "upstream_inference_prompt_cost": 4.674e-05, "upstream_inference_completions_cost": 0.0001512}, "completion_tokens_details": {"reasoning_tokens": 83, "image_tokens": 0, "audio_tokens": 0}}, "response_id": "gen-1781608888-Lg1jNcGJ42GtbCxTGex8", "finish_reason": "stop", "score": {"passed": true, "score": 1.0, "details": {"expected": "125", "actual": "125"}}, "error": null, "rescored_from": "/Users/dracohu/REPO/dracobench/runs/v0.3-challenge100-minimax-minimax-m3-ability16384.jsonl", "scoring_policy": "text_rules_length_diagnostic_only+v03_alias_relaxations_round2"} {"case_id": "challenge-reasoning-002", "suite": "reasoning", "case_version": "0.2.0", "tags": ["constraint-solving"], "scorer": "exact", "expected": {"answer": "D", "normalize": true}, "model": "minimax/minimax-m3", "provider": null, "prompt": "只回答一个字母：五个任务 A、B、C、D、E 排队。A 必须在 B 前，B 必须在 C 前；D 不能在两端；E 必须紧挨在 C 后面。在所有满足条件的队列中，按字典序最小的队列第三个任务是谁？", "parameters": {"temperature": 0, "max_tokens": 16384, "seed": null}, "prompt_hash": "e273e5a0cd57aef19af9e4ccfcd445a7523180f5cab62526c14aa0e62d5c1cc9", "started_at": "2026-06-16T11:21:29.210682+00:00", "latency_ms": 13373, "output": "D", "usage": {"prompt_tokens": 247, "completion_tokens": 723, "total_tokens": 970, "cost": 0.00091434, "is_byok": false, "prompt_tokens_details": {"cached_tokens": 114, "cache_write_tokens": 0, "audio_tokens": 0, "video_tokens": 0}, "cost_details": {"upstream_inference_cost": 0.00091434, "upstream_inference_prompt_cost": 4.674e-05, "upstream_inference_completions_cost": 0.0008676}, "completion_tokens_details": {"reasoning_tokens": 532, "image_tokens": 0, "audio_tokens": 0}}, "response_id": "gen-1781608891-fLVhI0KBYN5Fo0gXvJVC", "finish_reason": "stop", "score": {"passed": true, "score": 1.0, "details": {"expected": "D", "actual": "D"}}, "error": null, "rescored_from": "/Users/dracohu/REPO/dracobench/runs/v0.3-challenge100-minimax-minimax-m3-ability16384.jsonl", "scoring_policy": "text_rules_length_diagnostic_only+v03_alias_relaxations_round2"} {"case_id": "challenge-reasoning-003", "suite": "reasoning", "case_version": "0.2.0", "tags": ["binary", "symbolic"], "scorer": "exact", "expected": {"answer": "0", "normalize": true}, "model": "minimax/minimax-m3", "provider": null, "prompt": "只回答最终数字：一个 4 位二进制数从 1011 开始，先左移一位并只保留低 4 位，再与 0110 做异或，最后转成十进制。结果是多少？", "parameters": {"temperature": 0, "max_tokens": 16384, "seed": null}, "prompt_hash": "813f5aead205f4aa7a9d2a1e7b7085daae002a54592edd8d8aad2e7d7551897c", "started_at": "2026-06-16T11:21:42.597453+00:00", "latency_ms": 13611, "output": "0", "usage": {"prompt_tokens": 236, "completion_tokens": 127, "total_tokens": 363, "cost": 0.00018432, "is_byok": false, "prompt_tokens_details": {"cached_tokens": 162, "cache_write_tokens": 0, "audio_tokens": 0, "video_tokens": 0}, "cost_details": {"upstream_inference_cost": 0.00018432, "upstream_inference_prompt_cost": 3.192e-05, "upstream_inference_completions_cost": 0.0001524}, "completion_tokens_details": {"reasoning_tokens": 103, "image_tokens": 0, "audio_tokens": 0}}, "response_id": "gen-1781608904-FQGt80sSGLy1owjEgdxB", "finish_reason": "stop", "score": {"passed": true, "score": 1.0, "details": {"expected": "0", "actual": "0"}}, "error": null, "rescored_from": "/Users/dracohu/REPO/dracobench/runs/v0.3-challenge100-minimax-minimax-m3-ability16384.jsonl", "scoring_policy": "text_rules_length_diagnostic_only+v03_alias_relaxations_round2"} {"case_id": "challenge-reasoning-004", "suite": "reasoning", "case_version": "0.2.0", "tags": ["set", "operations"], "scorer": "exact", "expected": {"answer": "18", "normalize": true}, "model": "minimax/minimax-m3", "provider": null, "prompt": "只回答最终数字：一个集合最初为 {1,2,3,4,5}。依次执行：删除所有偶数；加入 6 和 7；把每个元素乘以 2；删除大于 10 的元素。最终集合元素之和是多少？", "parameters": {"temperature": 0, "max_tokens": 16384, "seed": null}, "prompt_hash": "3077e41c160e4ebe361234b76e647f42583619ef446b73e7c1e25858312f328e", "started_at": "2026-06-16T11:21:56.221180+00:00", "latency_ms": 3698, "output": "**18**", "usage": {"prompt_tokens": 249, "completion_tokens": 157, "total_tokens": 406, "cost": 0.00023574, "is_byok": false, "prompt_tokens_details": {"cached_tokens": 114, "cache_write_tokens": 0, "audio_tokens": 0, "video_tokens": 0}, "cost_details": {"upstream_inference_cost": 0.00023574, "upstream_inference_prompt_cost": 4.734e-05, "upstream_inference_completions_cost": 0.0001884}, "completion_tokens_details": {"reasoning_tokens": 87, "image_tokens": 0, "audio_tokens": 0}}, "response_id": "gen-1781608918-IkCt5YlimTy8AUnb4K6p", "finish_reason": "stop", "score": {"passed": false, "score": 0.0, "details": {"expected": "18", "actual": "**18**"}}, "error": null, "rescored_from": "/Users/dracohu/REPO/dracobench/runs/v0.3-challenge100-minimax-minimax-m3-ability16384.jsonl", "scoring_policy": "text_rules_length_diagnostic_only+v03_alias_relaxations_round2"} {"case_id": "challenge-reasoning-005", "suite": "reasoning", "case_version": "0.2.0", "tags": ["logic", "sat"], "scorer": "exact", "expected": {"answer": "可满足", "normalize": true}, "model": "minimax/minimax-m3", "provider": null, "prompt": "只回答“可满足”或“不可满足”：有三个布尔变量 x、y、z。约束为：x 或 y 为真；如果 x 为真则 z 为假；如果 y 为真则 z 为真；x 和 y 不能同时为真。是否存在赋值满足全部约束？", "parameters": {"temperature": 0, "max_tokens": 16384, "seed": null}, "prompt_hash": "1e534bd21af5e75f0c9fd61e3d5a3f660355d4555e34e16a06744234da47e006", "started_at": "2026-06-16T11:21:59.932274+00:00", "latency_ms": 3860, "output": "可满足", "usage": {"prompt_tokens": 251, "completion_tokens": 172, "total_tokens": 423, "cost": 0.00025434, "is_byok": false, "prompt_tokens_details": {"cached_tokens": 114, "cache_write_tokens": 0, "audio_tokens": 0, "video_tokens": 0}, "cost_details": {"upstream_inference_cost": 0.00025434, "upstream_inference_prompt_cost": 4.794e-05, "upstream_inference_completions_cost": 0.0002064}, "completion_tokens_details": {"reasoning_tokens": 147, "image_tokens": 0, "audio_tokens": 0}}, "response_id": "gen-1781608922-uE0JJ6PvuvGX9XcEg8rr", "finish_reason": "stop", "score": {"passed": true, "score": 1.0, "details": {"expected": "可满足", "actual": "可满足"}}, "error": null, "rescored_from": "/Users/dracohu/REPO/dracobench/runs/v0.3-challenge100-minimax-minimax-m3-ability16384.jsonl", "scoring_policy": "text_rules_length_diagnostic_only+v03_alias_relaxations_round2"} {"case_id": "challenge-rag-001", "suite": "rag_long_context", "case_version": "0.2.0", "tags": ["rag", "grounded"], "scorer": "text_rules", "expected": {"required": ["人工抽检"], "required_any": [["默认使用", "默认判分", "默认采用", "作为默认判分"]], "forbidden": ["因为成本太高", "因为不支持中文"], "max_chars": 160}, "model": "minimax/minimax-m3", "provider": null, "prompt": "只根据资料回答。\n\n资料 A：DracoBench v0.2 的目标是提高区分度，优先增加 coding、debugging、RAG 和 instruction_following 题。\n资料 B：DracoBench v0.2 不会默认使用 LLM-as-judge；开放中文写作题只做少量规则校验和人工抽检。\n资料 C：v0.2 的 HTML 报告必须展示通过率、成本、reasoning tokens、失败样例和逐题明细。\n\n问题：v0.2 为什么没有把 LLM-as-judge 作为默认判分？", "parameters": {"temperature": 0, "max_tokens": 16384, "seed": null}, "prompt_hash": "7a40b395b42a59b621957d70755d39fca26dba23f4e0c46743018935350319d3", "started_at": "2026-06-16T11:22:03.806003+00:00", "latency_ms": 14029, "output": "根据现有资料，**没有直接说明原因**，只能从相关材料中做出推断：\n\n1. **资料 B** 仅陈述了事实——v0.2 不默认使用 LLM-as-judge，开放中文写作题只做少量规则校验和人工抽检，但未解释背后的理由。\n\n2. 可结合 **资料 A** 做合理推断：v0.2 的重点是提升区分度，新增题型集中在 coding、debugging、RAG、instruction_following——这些任务**大多可通过规则或确定性方式判分**（如代码是否通过测试、指令是否被遵从），不需要 LLM-as-judge 介入；而对于开放中文写作题，仅采用规则校验 + 人工抽检已足够。\n\n综上，资料未显式给出\"为什么不用 LLM-as-judge\"的原因，但可以看出 v0.2 的题型设计和判分策略都朝着**可机器化、可规则化**的方向倾斜，这可能是放弃 LLM-as-judge 默认判分的原因之一。若需确切原因，需补充其他来源资料。", "usage": {"prompt_tokens": 311, "completion_tokens": 726, "total_tokens": 1037, "cost": 0.00093714, "is_byok": false, "prompt_tokens_details": {"cached_tokens": 114, "cache_write_tokens": 0, "audio_tokens": 0, "video_tokens": 0}, "cost_details": {"upstream_inference_cost": 0.00093714, "upstream_inference_prompt_cost": 6.594e-05, "upstream_inference_completions_cost": 0.0008712}, "completion_tokens_details": {"reasoning_tokens": 634, "image_tokens": 0, "audio_tokens": 0}}, "response_id": "gen-1781608926-DI0qtaRE5P0MkPqlczHM", "finish_reason": "stop", "score": {"passed": true, "score": 1.0, "details": {"missing": [], "missing_any": [], "present_forbidden": [], "char_count": 419, "length_ok": false, "length_scored": false}}, "error": null, "rescored_from": "/Users/dracohu/REPO/dracobench/runs/v0.3-challenge100-minimax-minimax-m3-ability16384.jsonl", "scoring_policy": "text_rules_length_diagnostic_only+v03_alias_relaxations_round2"} {"case_id": "challenge-rag-002", "suite": "rag_long_context", "case_version": "0.2.0", "tags": ["rag", "tradeoff", "abstain"], "scorer": "text_rules", "expected": {"required": ["Beta", "成本", "延迟", "判断"], "forbidden": ["Alpha 更适合", "多模态更强"], "max_chars": 180}, "model": "minimax/minimax-m3", "provider": null, "prompt": "只根据资料回答，不要自行补充。\n\n资料：模型 Alpha 在 30 道 challenge 题中通过 24 道，成本 0.08 美元，平均延迟 9 秒。模型 Beta 通过 22 道，成本 0.02 美元，平均延迟 3 秒。资料没有给出两者在多模态任务上的表现。\n\n问题：如果要写低成本高吞吐摘要服务，资料更支持选择哪个模型？另外，多模态表现能否判断？", "parameters": {"temperature": 0, "max_tokens": 16384, "seed": null}, "prompt_hash": "b25dfce277314b219ab8ccef6f02fb644e23cd169765867993ae58ccd7258c4a", "started_at": "2026-06-16T11:22:17.849785+00:00", "latency_ms": 8687, "output": "## 选择建议\n\n资料更支持选择 **模型 Beta**，理由如下：\n\n- **成本更低**：Beta 0.02 美元 vs Alpha 0.08 美元，约为 Alpha 的 1/4\n- **延迟更低**：3 秒 vs 9 秒，更有利于高吞吐\n- **质量差距不大**：Beta 通过 22 道，Alpha 通过 24 道，相差 2/30，对摘要这类任务而言，成本与吞吐收益可能超过这点质量差距\n\n## 多模态表现\n\n**无法判断**。资料明确表示\"没有给出两者在多模态任务上的表现\"，因此既不能比较也不能据此选型。", "usage": {"prompt_tokens": 286, "completion_tokens": 391, "total_tokens": 677, "cost": 0.00052764, "is_byok": false, "prompt_tokens_details": {"cached_tokens": 114, "cache_write_tokens": 0, "audio_tokens": 0, "video_tokens": 0}, "cost_details": {"upstream_inference_cost": 0.00052764, "upstream_inference_prompt_cost": 5.844e-05, "upstream_inference_completions_cost": 0.0004692}, "completion_tokens_details": {"reasoning_tokens": 290, "image_tokens": 0, "audio_tokens": 0}}, "response_id": "gen-1781608940-SP4471RsgYoWSj6iC9fm", "finish_reason": "stop", "score": {"passed": true, "score": 1.0, "details": {"missing": [], "missing_any": [], "present_forbidden": [], "char_count": 258, "length_ok": false, "length_scored": false}}, "error": null, "rescored_from": "/Users/dracohu/REPO/dracobench/runs/v0.3-challenge100-minimax-minimax-m3-ability16384.jsonl", "scoring_policy": "text_rules_length_diagnostic_only+v03_alias_relaxations_round2"} {"case_id": "challenge-rag-003", "suite": "rag_long_context", "case_version": "0.2.0", "tags": ["rag", "nuance"], "scorer": "text_rules", "expected": {"required": ["题面歧义"], "required_any": [["47/50", "47 题", "47题"], ["空输出", "输出为空", "未输出"]], "forbidden": ["满分", "完全失败"], "max_chars": 240}, "model": "minimax/minimax-m3", "provider": null, "prompt": "只根据资料回答。\n\n资料：一次评测中，K 模型在 smoke set 上 7/7，通过率 100%；在 hard set 上原始结果 47/50，其中 2 题后来被认定为题面歧义，单独复测通过。另有 1 题因 reasoning tokens 用尽而空输出。\n\n问题：更公平的文字结论应该如何描述 K 模型这次 hard set 表现？", "parameters": {"temperature": 0, "max_tokens": 16384, "seed": null}, "prompt_hash": "12db30955792c48368632f01aa0a9c06e9fc27004c4dbf12ce602cb8488494e6", "started_at": "2026-06-16T11:22:26.550509+00:00", "latency_ms": 12536, "output": "更公平的文字结论可以这样写：\n\n> 在 hard set（50 题）上，K 模型原始通过 47 题（94%）。3 道未通过的题中，**2 道属于题面歧义**，经认定后 K 模型在复测中均能给出正确解，可视为题库瑕疵而非模型能力缺陷；**剩余 1 道是因 reasoning tokens 耗尽导致空输出**，属于模型端的真实失败。剔除题面歧义的 2 题后，K 模型在有效题目上的通过率为 **47 / 48 ≈ 97.9%**，并暴露出在长链推理下输出预算不足这一问题。\n\n这样既保留了原始数据（47/50），又区分了\"题目原因\"和\"模型原因\"两类未通过项，避免把题面歧义算到模型头上。", "usage": {"prompt_tokens": 276, "completion_tokens": 584, "total_tokens": 860, "cost": 0.00075624, "is_byok": false, "prompt_tokens_details": {"cached_tokens": 114, "cache_write_tokens": 0, "audio_tokens": 0, "video_tokens": 0}, "cost_details": {"upstream_inference_cost": 0.00075624, "upstream_inference_prompt_cost": 5.544e-05, "upstream_inference_completions_cost": 0.0007008}, "completion_tokens_details": {"reasoning_tokens": 460, "image_tokens": 0, "audio_tokens": 0}}, "response_id": "gen-1781608948-0RbdzCP2G6MH0qvZYQVz", "finish_reason": "stop", "score": {"passed": true, "score": 1.0, "details": {"missing": [], "missing_any": [], "present_forbidden": [], "char_count": 293, "length_ok": false, "length_scored": false}}, "error": null, "rescored_from": "/Users/dracohu/REPO/dracobench/runs/v0.3-challenge100-minimax-minimax-m3-ability16384.jsonl", "scoring_policy": "text_rules_length_diagnostic_only+v03_alias_relaxations_round2"} {"case_id": "challenge-rag-004", "suite": "rag_long_context", "case_version": "0.2.0", "tags": ["rag", "abstain"], "scorer": "text_rules", "expected": {"required": ["不能", "不包含", "利润率"], "forbidden": ["可以判断", "训练数据"], "max_chars": 100}, "model": "minimax/minimax-m3", "provider": null, "prompt": "只根据资料回答，不要使用资料外知识。\n\n资料：报告字段包括 pass_rate、avg_latency_ms、total_cost、prompt_tokens、completion_tokens、reasoning_tokens。报告不包含模型训练数据来源，也不包含供应商真实利润率。\n\n问题：根据资料，能否从报告判断供应商利润率？", "parameters": {"temperature": 0, "max_tokens": 16384, "seed": null}, "prompt_hash": "cb2d9baeb98b0b4836972058965359b461f03aaba6cd096f40ffc813ce331880", "started_at": "2026-06-16T11:22:39.100052+00:00", "latency_ms": 5700, "output": "不能。\n\n根据资料，报告**不包含供应商真实利润率**。报告中的字段（pass_rate、avg_latency_ms、total_cost、prompt_tokens、completion_tokens、reasoning_tokens）均为性能与计费相关指标，无法从中推断出供应商的利润率。", "usage": {"prompt_tokens": 253, "completion_tokens": 207, "total_tokens": 460, "cost": 0.00029694, "is_byok": false, "prompt_tokens_details": {"cached_tokens": 114, "cache_write_tokens": 0, "audio_tokens": 0, "video_tokens": 0}, "cost_details": {"upstream_inference_cost": 0.00029694, "upstream_inference_prompt_cost": 4.854e-05, "upstream_inference_completions_cost": 0.0002484}, "completion_tokens_details": {"reasoning_tokens": 211, "image_tokens": 0, "audio_tokens": 0}}, "response_id": "gen-1781608961-eE4Tiq1RIPhTOF82Q2Dj", "finish_reason": "stop", "score": {"passed": true, "score": 1.0, "details": {"missing": [], "missing_any": [], "present_forbidden": [], "char_count": 147, "length_ok": false, "length_scored": false}}, "error": null, "rescored_from": "/Users/dracohu/REPO/dracobench/runs/v0.3-challenge100-minimax-minimax-m3-ability16384.jsonl", "scoring_policy": "text_rules_length_diagnostic_only+v03_alias_relaxations_round2"} {"case_id": "challenge-if-001", "suite": "instruction_following", "case_version": "0.2.0", "tags": ["json", "nested"], "scorer": "json_schema_lite", "expected": {"schema": {"type": "object", "required": ["status", "scores", "note"], "additionalProperties": false, "properties": {"status": {"type": "string", "const": "review"}, "scores": {"type": "object", "required": ["coding", "debugging", "rag"], "additionalProperties": false, "properties": {"coding": {"type": "integer"}, "debugging": {"type": "integer"}, "rag": {"type": "integer"}}}, "note": {"type": "string"}}}}, "model": "minimax/minimax-m3", "provider": null, "prompt": "只输出 JSON 对象，不要 Markdown。字段 status 必须是 \"review\"；字段 scores 必须是对象，包含 coding、debugging、rag 三个整数；字段 note 必须是不超过 12 个汉字的中文字符串。", "parameters": {"temperature": 0, "max_tokens": 16384, "seed": null}, "prompt_hash": "7f670ae329c67bf1957a977815256b403ae7965cb15f2d2db520256cc3f2eb77", "started_at": "2026-06-16T11:22:44.814081+00:00", "latency_ms": 5758, "output": "{\"status\":\"review\",\"scores\":{\"coding\":85,\"debugging\":80,\"rag\":75},\"note\":\"评估中，待审核\"}{\"status\":\"review\",\"scores\":{\"coding\":85,\"debugging\":80,\"rag\":75},\"note\":\"评估中，待审核\"}", "usage": {"prompt_tokens": 239, "completion_tokens": 195, "total_tokens": 434, "cost": 0.000389676, "is_byok": false, "prompt_tokens_details": {"cached_tokens": 114, "cache_write_tokens": 0, "audio_tokens": 0, "video_tokens": 0}, "cost_details": {"upstream_inference_cost": 0.000389676, "upstream_inference_prompt_cost": 6.2076e-05, "upstream_inference_completions_cost": 0.0003276}, "completion_tokens_details": {"reasoning_tokens": 159, "image_tokens": 0, "audio_tokens": 0}}, "response_id": "gen-1781608967-jrIVeGdi1jwqdXGmLak1", "finish_reason": "stop", "score": {"passed": false, "score": 0.0, "details": {"error": "invalid JSON object: Extra data: line 1 column 84 (char 83)"}}, "error": null, "rescored_from": "/Users/dracohu/REPO/dracobench/runs/v0.3-challenge100-minimax-minimax-m3-ability16384.jsonl", "scoring_policy": "text_rules_length_diagnostic_only+v03_alias_relaxations_round2"} {"case_id": "challenge-if-002", "suite": "instruction_following", "case_version": "0.2.0", "tags": ["format", "line-control"], "scorer": "regex", "expected": {"pattern": "^\\s*BEGIN\\s*\\n[^\\n]*模型评测[^\\n]*\\n[^\\n]*HTML报告[^\\n]*\\nEND\\s*$", "flags": "is"}, "model": "minimax/minimax-m3", "provider": null, "prompt": "只输出 4 行，不要解释。第 1 行是 BEGIN，第 4 行是 END。第 2 行必须包含“模型评测”，第 3 行必须包含“HTML报告”。不能出现任何项目符号。", "parameters": {"temperature": 0, "max_tokens": 16384, "seed": null}, "prompt_hash": "773b2e7398be979e738e08c3c8486ca69bae0f71d9a2abe3430cef775f5a59fb", "started_at": "2026-06-16T11:22:50.586423+00:00", "latency_ms": 11201, "output": "BEGIN\n本模型评测涵盖多维度能力测试\n生成可视化HTML报告用于结果展示\nEND", "usage": {"prompt_tokens": 239, "completion_tokens": 84, "total_tokens": 323, "cost": 0.00013362, "is_byok": false, "prompt_tokens_details": {"cached_tokens": 162, "cache_write_tokens": 0, "audio_tokens": 0, "video_tokens": 0}, "cost_details": {"upstream_inference_cost": 0.00013362, "upstream_inference_prompt_cost": 3.282e-05, "upstream_inference_completions_cost": 0.0001008}, "completion_tokens_details": {"reasoning_tokens": 61, "image_tokens": 0, "audio_tokens": 0}}, "response_id": "gen-1781608979-JGy4QU8bV5F87mtkKyjC", "finish_reason": "stop", "score": {"passed": true, "score": 1.0, "details": {"pattern": "^\\s*BEGIN\\s*\\n[^\\n]*模型评测[^\\n]*\\n[^\\n]*HTML报告[^\\n]*\\nEND\\s*$"}}, "error": null, "rescored_from": "/Users/dracohu/REPO/dracobench/runs/v0.3-challenge100-minimax-minimax-m3-ability16384.jsonl", "scoring_policy": "text_rules_length_diagnostic_only+v03_alias_relaxations_round2"} {"case_id": "challenge-if-003", "suite": "instruction_following", "case_version": "0.2.0", "tags": ["constraint", "zh"], "scorer": "text_rules", "expected": {"required": ["难题", "区分度"], "forbidden": ["最强", "吊打", "遥遥领先"], "max_chars": 28}, "model": "minimax/minimax-m3", "provider": null, "prompt": "只输出一个中文句子，不能超过 28 个汉字。必须同时包含“难题”和“区分度”，不能包含“最强”“吊打”“遥遥领先”。", "parameters": {"temperature": 0, "max_tokens": 16384, "seed": null}, "prompt_hash": "63d393d3332e09c8d34bfa5fceb4812734bf76c2333f7e6d1585cf977944f0db", "started_at": "2026-06-16T11:23:01.802117+00:00", "latency_ms": 7429, "output": "这场考试的难题设置合理，区分度较高。这场考试的难题设置合理，区分度较高。", "usage": {"prompt_tokens": 227, "completion_tokens": 180, "total_tokens": 407, "cost": 0.000359436, "is_byok": false, "prompt_tokens_details": {"cached_tokens": 114, "cache_write_tokens": 0, "audio_tokens": 0, "video_tokens": 0}, "cost_details": {"upstream_inference_cost": 0.000359436, "upstream_inference_prompt_cost": 5.7036e-05, "upstream_inference_completions_cost": 0.0003024}, "completion_tokens_details": {"reasoning_tokens": 160, "image_tokens": 0, "audio_tokens": 0}}, "response_id": "gen-1781608984-1f8mgLihTHDyT6nBZKKH", "finish_reason": "stop", "score": {"passed": true, "score": 1.0, "details": {"missing": [], "missing_any": [], "present_forbidden": [], "char_count": 36, "length_ok": false, "length_scored": false}}, "error": null, "rescored_from": "/Users/dracohu/REPO/dracobench/runs/v0.3-challenge100-minimax-minimax-m3-ability16384.jsonl", "scoring_policy": "text_rules_length_diagnostic_only+v03_alias_relaxations_round2"} {"case_id": "challenge-zh-writing-001", "suite": "chinese_writing", "case_version": "0.2.0", "tags": ["wechat", "conclusion"], "scorer": "text_rules", "expected": {"required": ["简单题", "真实能力", "失败样例"], "forbidden": ["吊打"], "max_chars": 120}, "model": "minimax/minimax-m3", "provider": null, "prompt": "写一段不超过 120 个汉字的公众号结论，主题是“模型在简单题满分不代表真实强”。必须包含“简单题”“真实能力”“失败样例”，不能出现“吊打”。", "parameters": {"temperature": 0.2, "max_tokens": 16384, "seed": null}, "prompt_hash": "f330d4a7913b830c15df0d2a36cf0a9e5c07628ef6fa52408cc1bd2e90074762", "started_at": "2026-06-16T11:23:09.243505+00:00", "latency_ms": 14269, "output": "简单题拿满分≠模型真强。真正的真实能力，要在长文本、多轮对话、复杂推理里见分晓。一旦脱离舒适区，失败样例立刻暴露短板。评测别只看热闹，要看硬仗。", "usage": {"prompt_tokens": 236, "completion_tokens": 981, "total_tokens": 1217, "cost": 0.00122064, "is_byok": false, "prompt_tokens_details": {"cached_tokens": 114, "cache_write_tokens": 0, "audio_tokens": 0, "video_tokens": 0}, "cost_details": {"upstream_inference_cost": 0.00122064, "upstream_inference_prompt_cost": 4.344e-05, "upstream_inference_completions_cost": 0.0011772}, "completion_tokens_details": {"reasoning_tokens": 813, "image_tokens": 0, "audio_tokens": 0}}, "response_id": "gen-1781608991-67RBCJs0pQCTlVAz3Mhi", "finish_reason": "stop", "score": {"passed": true, "score": 1.0, "details": {"missing": [], "missing_any": [], "present_forbidden": [], "char_count": 72, "length_ok": true, "length_scored": false}}, "error": null, "rescored_from": "/Users/dracohu/REPO/dracobench/runs/v0.3-challenge100-minimax-minimax-m3-ability16384.jsonl", "scoring_policy": "text_rules_length_diagnostic_only+v03_alias_relaxations_round2"} {"case_id": "challenge-zh-writing-002", "suite": "chinese_writing", "case_version": "0.2.0", "tags": ["rewrite", "zh"], "scorer": "text_rules", "expected": {"required": ["复杂调试", "稳定性"], "forbidden": ["猛", "掉链子"], "max_chars": 80}, "model": "minimax/minimax-m3", "provider": null, "prompt": "把下面口语句改写成专业表达，不超过 80 个汉字：这个模型看起来很猛，但一遇到复杂调试就开始掉链子。必须包含“复杂调试”和“稳定性”，不能包含“猛”“掉链子”。", "parameters": {"temperature": 0.2, "max_tokens": 16384, "seed": null}, "prompt_hash": "1628d5e9cbea22d0750b547f0818eafa82d503dcad8fdc41ef2f523f9fbb55a1", "started_at": "2026-06-16T11:23:23.532482+00:00", "latency_ms": 9937, "output": "该模型在常规任务中表现优异，但在复杂调试场景下稳定性显著下降，难以满足实际应用需求。该模型在常规任务中表现优异，但在复杂调试场景下稳定性显著下降，难以满足实际应用需求。", "usage": {"prompt_tokens": 241, "completion_tokens": 335, "total_tokens": 576, "cost": 0.000625716, "is_byok": false, "prompt_tokens_details": {"cached_tokens": 114, "cache_write_tokens": 0, "audio_tokens": 0, "video_tokens": 0}, "cost_details": {"upstream_inference_cost": 0.000625716, "upstream_inference_prompt_cost": 6.2916e-05, "upstream_inference_completions_cost": 0.0005628}, "completion_tokens_details": {"reasoning_tokens": 299, "image_tokens": 0, "audio_tokens": 0}}, "response_id": "gen-1781609005-ktq2MxCiMJQBP33WNVCq", "finish_reason": "stop", "score": {"passed": true, "score": 1.0, "details": {"missing": [], "missing_any": [], "present_forbidden": [], "char_count": 84, "length_ok": false, "length_scored": false}}, "error": null, "rescored_from": "/Users/dracohu/REPO/dracobench/runs/v0.3-challenge100-minimax-minimax-m3-ability16384.jsonl", "scoring_policy": "text_rules_length_diagnostic_only+v03_alias_relaxations_round2"} {"case_id": "challenge-coding-009", "suite": "coding", "case_version": "0.2.0", "tags": ["python", "aggregation", "dirty-input"], "scorer": "code_python_tests", "expected": {"tests": ["events=[{'user':'b','type':'add','amount':'5'},{'user':'a','type':'add','amount':3},{'user':'b','type':'remove','amount':2},{'user':'a','type':'remove','amount':3}]\nassert solution.solve(events) == {'b': 3}", "assert solution.solve([]) == {}", "assert solution.solve([{'user':'x','type':'remove','amount':'2'}]) == {'x': -2}"], "timeout_seconds": 3}, "model": "minimax/minimax-m3", "provider": null, "prompt": "只输出 Python 代码，不要解释。实现 solve(events: list[dict]) -> dict[str, int]。每个事件包含 user、type、amount；type 为 \"add\" 或 \"remove\"。按 user 汇总 add 减 remove 后的库存，结果中去掉库存为 0 的用户，按 key 字典序返回普通 dict。amount 可能是字符串数字。", "parameters": {"temperature": 0, "max_tokens": 16384, "seed": null}, "prompt_hash": "9b81c7f8e34dbf12cfabd35ebb618802fe09200deca1d146558b4e1c12e6fd1e", "started_at": "2026-06-16T11:23:33.482459+00:00", "latency_ms": 6728, "output": "```python\ndef solve(events: list[dict]) -> dict[str, int]:\n inventory = {}\n for event in events:\n user = event['user']\n amount = int(event['amount'])\n if event['type'] == 'add':\n inventory[user] = inventory.get(user, 0) + amount\n else:\n inventory[user] = inventory.get(user, 0) - amount\n inventory = {k: v for k, v in inventory.items() if v != 0}\n return dict(sorted(inventory.items()))\n```", "usage": {"prompt_tokens": 268, "completion_tokens": 456, "total_tokens": 724, "cost": 0.00060024, "is_byok": false, "prompt_tokens_details": {"cached_tokens": 114, "cache_write_tokens": 0, "audio_tokens": 0, "video_tokens": 0}, "cost_details": {"upstream_inference_cost": 0.00060024, "upstream_inference_prompt_cost": 5.304e-05, "upstream_inference_completions_cost": 0.0005472}, "completion_tokens_details": {"reasoning_tokens": 361, "image_tokens": 0, "audio_tokens": 0}}, "response_id": "gen-1781609015-HIXXzGDpC00AUZhK38st", "finish_reason": "stop", "score": {"passed": true, "score": 1.0, "details": {"returncode": 0, "stdout": "", "stderr": ""}}, "error": null, "rescored_from": "/Users/dracohu/REPO/dracobench/runs/v0.3-challenge100-minimax-minimax-m3-ability16384.jsonl", "scoring_policy": "text_rules_length_diagnostic_only+v03_alias_relaxations_round2"} {"case_id": "challenge-coding-010", "suite": "coding", "case_version": "0.2.0", "tags": ["python", "parser", "expression"], "scorer": "code_python_tests", "expected": {"tests": ["assert solution.solve('1 + 2 - 3') == 0", "assert solution.solve('10 - (2 + 3) + 4') == 9", "assert solution.solve('(1+(4+5+2)-3)+(6+8)') == 23"], "timeout_seconds": 3}, "model": "minimax/minimax-m3", "provider": null, "prompt": "只输出 Python 代码，不要解释。实现 solve(expr: str) -> int。expr 只包含非负整数、+、-、空格和括号，计算表达式结果。不要使用 eval。", "parameters": {"temperature": 0, "max_tokens": 16384, "seed": null}, "prompt_hash": "662cadd9b4e3fdcfdf38e1d7338201fe3049d948576c89303695cff58950b3b4", "started_at": "2026-06-16T11:23:40.263147+00:00", "latency_ms": 39744, "output": "```python\ndef solve(expr: str) -> int:\n expr = expr.replace(' ', '')\n\n def parse_expression(pos):\n result, pos = parse_term(pos)\n while pos < len(expr) and expr[pos] in '+-':\n op = expr[pos]\n pos += 1\n right, pos = parse_term(pos)\n if op == '+':\n result += right\n else:\n result -= right\n return result, pos\n\n def parse_term(pos):\n return parse_factor(pos)\n\n def parse_factor(pos):\n if expr[pos] == '(':\n pos += 1\n result, pos = parse_expression(pos)\n pos += 1\n return result, pos\n else:\n start = pos\n while pos < len(expr) and expr[pos].isdigit():\n pos += 1\n return int(expr[start:pos]), pos\n\n result, _ = parse_expression(0)\n return result\n```", "usage": {"prompt_tokens": 228, "completion_tokens": 1524, "total_tokens": 1752, "cost": 0.0037944, "is_byok": false, "prompt_tokens_details": {"cached_tokens": 0, "cache_write_tokens": 0, "audio_tokens": 0, "video_tokens": 0}, "cost_details": {"upstream_inference_cost": 0.0037944, "upstream_inference_prompt_cost": 0.0001368, "upstream_inference_completions_cost": 0.0036576}, "completion_tokens_details": {"reasoning_tokens": 1473, "image_tokens": 0, "audio_tokens": 0}}, "response_id": "gen-1781609022-SfpP2Wx0Ej2q4HY9FBDL", "finish_reason": "stop", "score": {"passed": true, "score": 1.0, "details": {"returncode": 0, "stdout": "", "stderr": ""}}, "error": null, "rescored_from": "/Users/dracohu/REPO/dracobench/runs/v0.3-challenge100-minimax-minimax-m3-ability16384.jsonl", "scoring_policy": "text_rules_length_diagnostic_only+v03_alias_relaxations_round2"} {"case_id": "challenge-coding-011", "suite": "coding", "case_version": "0.2.0", "tags": ["python", "sorting", "dedupe"], "scorer": "code_python_tests", "expected": {"tests": ["assert solution.solve(['alice 10','bob 12','alice 15','','bob 11']) == [('alice',15),('bob',12)]", "assert solution.solve(['x -1','a -1','b 0']) == [('b',0),('a',-1),('x',-1)]"], "timeout_seconds": 3}, "model": "minimax/minimax-m3", "provider": null, "prompt": "只输出 Python 代码，不要解释。实现 solve(rows: list[str]) -> list[tuple[str, int]]。每行格式为 \"name score\"，空白数量不定；同名保留最高分；输出按分数降序、同分按 name 升序。忽略空行。", "parameters": {"temperature": 0, "max_tokens": 16384, "seed": null}, "prompt_hash": "332bccf5652506856c633d7093431279279a22b2d3a99762f6cba55df7d884af", "started_at": "2026-06-16T11:24:20.047538+00:00", "latency_ms": 5671, "output": "```python\ndef solve(rows: list[str]) -> list[tuple[str, int]]:\n best = {}\n for row in rows:\n row = row.strip()\n if not row:\n continue\n parts = row.split()\n name = parts[0]\n score = int(parts[1])\n if name not in best or score > best[name]:\n best[name] = score\n return sorted(best.items(), key=lambda x: (-x[1], x[0]))\n```", "usage": {"prompt_tokens": 251, "completion_tokens": 345, "total_tokens": 596, "cost": 0.00046194, "is_byok": false, "prompt_tokens_details": {"cached_tokens": 114, "cache_write_tokens": 0, "audio_tokens": 0, "video_tokens": 0}, "cost_details": {"upstream_inference_cost": 0.00046194, "upstream_inference_prompt_cost": 4.794e-05, "upstream_inference_completions_cost": 0.000414}, "completion_tokens_details": {"reasoning_tokens": 270, "image_tokens": 0, "audio_tokens": 0}}, "response_id": "gen-1781609062-fnOjZ580jcTow6RKPEdp", "finish_reason": "stop", "score": {"passed": true, "score": 1.0, "details": {"returncode": 0, "stdout": "", "stderr": ""}}, "error": null, "rescored_from": "/Users/dracohu/REPO/dracobench/runs/v0.3-challenge100-minimax-minimax-m3-ability16384.jsonl", "scoring_policy": "text_rules_length_diagnostic_only+v03_alias_relaxations_round2"} {"case_id": "challenge-coding-012", "suite": "coding", "case_version": "0.2.0", "tags": ["python", "wildcard", "dp"], "scorer": "code_python_tests", "expected": {"tests": ["assert solution.solve('a*b?d', 'axxxbc d'.replace(' ','')) is True", "assert solution.solve('a*c', 'ab') is False", "assert solution.solve('*', '') is True", "assert solution.solve('file-??.txt', 'file-ab.txt') is True"], "timeout_seconds": 3}, "model": "minimax/minimax-m3", "provider": null, "prompt": "只输出 Python 代码，不要解释。实现 solve(pattern: str, text: str) -> bool。pattern 支持 ? 匹配任意单字符，* 匹配任意长度字符串。必须匹配完整 text。", "parameters": {"temperature": 0, "max_tokens": 16384, "seed": null}, "prompt_hash": "0ac24584e5856726a6e962c43819ac539910b115bdabb48f973f04258dd562d6", "started_at": "2026-06-16T11:24:25.750807+00:00", "latency_ms": 4884, "output": "```python\ndef solve(pattern: str, text: str) -> bool:\n m, n = len(pattern), len(text)\n dp = [[False] * (n + 1) for _ in range(m + 1)]\n dp[0][0] = True\n for i in range(1, m + 1):\n if pattern[i - 1] == '*':\n dp[i][0] = dp[i - 1][0]\n for i in range(1, m + 1):\n for j in range(1, n + 1):\n p = pattern[i - 1]\n if p == '*':\n dp[i][j] = dp[i - 1][j] or dp[i][j - 1]\n elif p == '?' or p == text[j - 1]:\n dp[i][j] = dp[i - 1][j - 1]\n return dp[m][n]\n```", "usage": {"prompt_tokens": 235, "completion_tokens": 280, "total_tokens": 515, "cost": 0.00037914, "is_byok": false, "prompt_tokens_details": {"cached_tokens": 114, "cache_write_tokens": 0, "audio_tokens": 0, "video_tokens": 0}, "cost_details": {"upstream_inference_cost": 0.00037914, "upstream_inference_prompt_cost": 4.314e-05, "upstream_inference_completions_cost": 0.000336}, "completion_tokens_details": {"reasoning_tokens": 96, "image_tokens": 0, "audio_tokens": 0}}, "response_id": "gen-1781609067-tOkKzma0zCCM7wTo09qO", "finish_reason": "stop", "score": {"passed": true, "score": 1.0, "details": {"returncode": 0, "stdout": "", "stderr": ""}}, "error": null, "rescored_from": "/Users/dracohu/REPO/dracobench/runs/v0.3-challenge100-minimax-minimax-m3-ability16384.jsonl", "scoring_policy": "text_rules_length_diagnostic_only+v03_alias_relaxations_round2"} {"case_id": "challenge-coding-013", "suite": "coding", "case_version": "0.2.0", "tags": ["python", "recursion", "flatten"], "scorer": "code_python_tests", "expected": {"tests": ["assert solution.solve({'a': {'b': 1, 'c': 'x'}, 'd': 2}) == ['a.b=1','a.c=x','d=2']", "assert solution.solve({'z': {'a': {'b': 3}}}) == ['z.a.b=3']", "assert solution.solve({}) == []"], "timeout_seconds": 3}, "model": "minimax/minimax-m3", "provider": null, "prompt": "只输出 Python 代码，不要解释。实现 solve(tree: dict) -> list[str]。tree 是嵌套字典，叶子是字符串或数字。返回所有叶子的路径，路径用点连接，按字典序排序。例如 {'a': {'b': 1}} -> ['a.b=1']。", "parameters": {"temperature": 0, "max_tokens": 16384, "seed": null}, "prompt_hash": "e658784bcc7118c9f5e1375c7f1f92aa8147c38fb8790138603de7f2b3722c98", "started_at": "2026-06-16T11:24:30.669830+00:00", "latency_ms": 34587, "output": "```python\ndef solve(tree: dict) -> list[str]:\n result = []\n def dfs(node, path):\n if not isinstance(node, dict):\n return\n for key, value in node.items():\n new_path = path + [str(key)]\n if isinstance(value, dict):\n dfs(value, new_path)\n else:\n result.append('.'.join(new_path) + '=' + str(value))\n dfs(tree, [])\n result.sort()\n return result\n``````python\ndef solve(tree: dict) -> list[str]:\n result = []\n def dfs(node, path):\n if not isinstance(node, dict):\n return\n for key, value in node.items():\n new_path = path + [str(key)]\n if isinstance(value, dict):\n dfs(value, new_path)\n else:\n result.append('.'.join(new_path) + '=' + str(value))\n dfs(tree, [])\n result.sort()\n return result\n```", "usage": {"prompt_tokens": 253, "completion_tokens": 1763, "total_tokens": 2016, "cost": 0.003029796, "is_byok": false, "prompt_tokens_details": {"cached_tokens": 114, "cache_write_tokens": 0, "audio_tokens": 0, "video_tokens": 0}, "cost_details": {"upstream_inference_cost": 0.003029796, "upstream_inference_prompt_cost": 6.7956e-05, "upstream_inference_completions_cost": 0.00296184}, "completion_tokens_details": {"reasoning_tokens": 1760, "image_tokens": 0, "audio_tokens": 0}}, "response_id": "gen-1781609072-cibUJO1DyuhADpmo3b6q", "finish_reason": "stop", "score": {"passed": true, "score": 1.0, "details": {"returncode": 0, "stdout": "", "stderr": ""}}, "error": null, "rescored_from": "/Users/dracohu/REPO/dracobench/runs/v0.3-challenge100-minimax-minimax-m3-ability16384.jsonl", "scoring_policy": "text_rules_length_diagnostic_only+v03_alias_relaxations_round2"} {"case_id": "challenge-coding-014", "suite": "coding", "case_version": "0.2.0", "tags": ["python", "dynamic-programming"], "scorer": "code_python_tests", "expected": {"tests": ["assert solution.solve([1,-2,0,3]) == 4", "assert solution.solve([1,-2,-2,3]) == 3", "assert solution.solve([-1,-1,-1]) == -1", "assert solution.solve([2,1,-2,3]) == 6"], "timeout_seconds": 3}, "model": "minimax/minimax-m3", "provider": null, "prompt": "只输出 Python 代码，不要解释。实现 solve(nums: list[int]) -> int。返回删除恰好一个元素后，剩余数组的最大连续子数组和。nums 长度至少为 2，可以全为负数。", "parameters": {"temperature": 0, "max_tokens": 16384, "seed": null}, "prompt_hash": "a7a5de11dacd1864c7d44a3ce983c2f127d1bcff88bec3b15f8727c513f56ea2", "started_at": "2026-06-16T11:25:05.302614+00:00", "latency_ms": 139363, "output": "```python\ndef solve(nums: list[int]) -> int:\n n = len(nums)\n \n prefix_max = [0] * n\n max_ending = nums[0]\n prefix_max[0] = nums[0]\n for i in range(1, n):\n max_ending = max(nums[i], max_ending + nums[i])\n prefix_max[i] = max(prefix_max[i-1], max_ending)\n \n suffix_max = [0] * n\n max_starting = nums[n-1]\n suffix_max[n-1] = nums[n-1]\n for i in range(n-2, -1, -1):\n max_starting = max(nums[i], max_starting + nums[i])\n suffix_max[i] = max(suffix_max[i+1], max_starting)\n \n max_ending_at = [0] * n\n max_ending_at[0] = nums[0]\n for i in range(1, n):\n max_ending_at[i] = max(nums[i], max_ending_at[i-1] + nums[i])\n \n max_starting_at = [0] * n\n max_starting_at[n-1] = nums[n-1]\n for i in range(n-2, -1, -1):\n max_starting_at[i] = max(nums[i], max_starting_at[i+1] + nums[i])\n \n result = float('-inf')\n for i in range(n):\n if i == 0:\n result = max(result, suffix_max[1])\n elif i == n - 1:\n result = max(result, prefix_max[n-2])\n else:\n result = max(result, prefix_max[i-1], suffix_max[i+1], max_ending_at[i-1] + max_starting_at[i+1])\n \n return result\n```", "usage": {"prompt_tokens": 239, "completion_tokens": 2984, "total_tokens": 3223, "cost": 0.00361362, "is_byok": false, "prompt_tokens_details": {"cached_tokens": 162, "cache_write_tokens": 0, "audio_tokens": 0, "video_tokens": 0}, "cost_details": {"upstream_inference_cost": 0.00361362, "upstream_inference_prompt_cost": 3.282e-05, "upstream_inference_completions_cost": 0.0035808}, "completion_tokens_details": {"reasoning_tokens": 2056, "image_tokens": 0, "audio_tokens": 0}}, "response_id": "gen-1781609107-93VgN5zR0zUMdsL10XxH", "finish_reason": "stop", "score": {"passed": true, "score": 1.0, "details": {"returncode": 0, "stdout": "", "stderr": ""}}, "error": null, "rescored_from": "/Users/dracohu/REPO/dracobench/runs/v0.3-challenge100-minimax-minimax-m3-ability16384.jsonl", "scoring_policy": "text_rules_length_diagnostic_only+v03_alias_relaxations_round2"} {"case_id": "challenge-debugging-009", "suite": "debugging", "case_version": "0.2.0", "tags": ["python", "parsing", "debugging"], "scorer": "regex", "expected": {"pattern": "FIX:\\s*`?\\s*return\\s+\\[part\\.strip\\(\\)\\s+for\\s+part\\s+in\\s+s\\.split\\('\\,'\\)\\s+if\\s+part\\.strip\\(\\)\\]\\s*`?", "flags": "is"}, "model": "minimax/minimax-m3", "provider": null, "prompt": "下面代码期望把输入按逗号拆分并去掉空白，但会把空字段也保留下来。请指出 bug，并给出最小修复。最后单独一行写 FIX: <修复后的 return 行>。\n\n```python\ndef parse(s):\n return [part.strip() for part in s.split(',')]\n```", "parameters": {"temperature": 0, "max_tokens": 16384, "seed": null}, "prompt_hash": "fc78c43e70a6a2fe59904be00b93d579300af6ba72fe5a9629bfe5bd82fad3d6", "started_at": "2026-06-16T11:27:24.716658+00:00", "latency_ms": 21904, "output": "## Bug 分析\n\n`split(',')` 会保留所有由逗号分隔出的字段，包括空字符串。例如：\n\n- `parse(\"a, b, , c\")` → `['a', 'b', '', 'c']`（中间的空格字段被保留）\n- `parse(\",a,b,\")` → `['', 'a', 'b', '']`（首尾的空字段被保留）\n\n题目要求\"去掉空白\"，但当前实现只对每个字段做 `strip()`，并没有把 `strip()` 后为空的字段过滤掉。\n\n## 最小修复\n\n在列表推导式里加一个 `if part.strip()` 过滤条件：\n\n```python\ndef parse(s):\n return [part.strip() for part in s.split(',') if part.strip()]\n```\n\nFIX: ` return [part.strip() for part in s.split(',') if part.strip()]`", "usage": {"prompt_tokens": 254, "completion_tokens": 387, "total_tokens": 641, "cost": 0.00050172, "is_byok": false, "prompt_tokens_details": {"cached_tokens": 162, "cache_write_tokens": 0, "audio_tokens": 0, "video_tokens": 0}, "cost_details": {"upstream_inference_cost": 0.00050172, "upstream_inference_prompt_cost": 3.732e-05, "upstream_inference_completions_cost": 0.0004644}, "completion_tokens_details": {"reasoning_tokens": 211, "image_tokens": 0, "audio_tokens": 0}}, "response_id": "gen-1781609247-N87fe2LKnaNJZuGiGwTp", "finish_reason": "stop", "score": {"passed": true, "score": 1.0, "details": {"pattern": "FIX:\\s*`?\\s*return\\s+\\[part\\.strip\\(\\)\\s+for\\s+part\\s+in\\s+s\\.split\\('\\,'\\)\\s+if\\s+part\\.strip\\(\\)\\]\\s*`?"}}, "error": null, "rescored_from": "/Users/dracohu/REPO/dracobench/runs/v0.3-challenge100-minimax-minimax-m3-ability16384.jsonl", "scoring_policy": "text_rules_length_diagnostic_only+v03_alias_relaxations_round2"} {"case_id": "challenge-debugging-010", "suite": "debugging", "case_version": "0.2.0", "tags": ["python", "datetime", "debugging"], "scorer": "contains_any", "expected": {"answers": ["datetime.fromisoformat", "dateutil.parser", "解析 ts 后排序", "parse ts", "fromisoformat", "int(r['ts'])", "float(r['ts'])", "key=lambda r: int(r['ts'])", "key=lambda r: float(r['ts'])"]}, "model": "minimax/minimax-m3", "provider": null, "prompt": "下面代码期望用 UTC 时间戳排序，但字符串排序会错。请指出 bug，并给出最小修复。最后单独一行写 FIX: <修复后的 sort 行>。\n\n```python\nrows.sort(key=lambda r: r['ts'])\n```", "parameters": {"temperature": 0, "max_tokens": 16384, "seed": null}, "prompt_hash": "c0d491a68f215902d8b314423a6ab9aea7ac0782e1a34e6d6997670c2ddb8458", "started_at": "2026-06-16T11:27:46.634386+00:00", "latency_ms": 24443, "output": "**Bug 分析**\n\n`r['ts']` 当前是字符串（例如 `\"1705314600\"` 或 ISO 字符串），按字典序比较并非按时间先后：\n\n- `\"1705314601\" < \"1705314602\"` 看似对的，但若 `ts` 是 ISO 字符串且时区/格式不统一（如 `\"2024-1-5 ...\"` 与 `\"2024-01-15 ...\"`），字典序就会错位；即使是纯数字串，Python 2 时代还会被当 long 比较，存在歧义与跨语言风险。\n- 最稳妥做法：把 `ts` 解析为**数值型时间戳**（或 `datetime`）后再排序。\n\n**最小修复**\n\n将 key 改为把字符串转成数值时间戳，强制按数值大小排序：\n\n```python\nrows.sort(key=lambda r: float(r['ts']))\n```\n\n如果 `ts` 一定是整型秒级时间戳，更严格写法：\n\n```python\nrows.sort(key=lambda r: int(r['ts']))\n```\n\n**FIX:** `rows.sort(key=lambda r: float(r['ts']))`**Bug 分析**\n\n`r['ts']` 当前是字符串（例如 `\"1705314600\"` 或 ISO 字符串），按字典序比较并非按时间先后：\n\n- `\"1705314601\" < \"1705314602\"` 看似对的，但若 `ts` 是 ISO 字符串且时区/格式不统一（如 `\"2024-1-5 ...\"` 与 `\"2024-01-15 ...\"`），字典序就会错位；即使是纯数字串，Python 2 时代还会被当 long 比较，存在歧义与跨语言风险。\n- 最稳妥做法：把 `ts` 解析为**数值型时间戳**（或 `datetime`）后再排序。\n\n**最小修复**\n\n将 key 改为把字符串转成数值时间戳，强制按数值大小排序：\n\n```python\nrows.sort(key=lambda r: float(r['ts']))\n```\n\n如果 `ts` 一定是整型秒级时间戳，更严格写法：\n\n```python\nrows.sort(key=lambda r: int(r['ts']))\n```\n\n**FIX:** `rows.sort(key=lambda r: float(r['ts']))`", "usage": {"prompt_tokens": 241, "completion_tokens": 1026, "total_tokens": 1267, "cost": 0.001786596, "is_byok": false, "prompt_tokens_details": {"cached_tokens": 114, "cache_write_tokens": 0, "audio_tokens": 0, "video_tokens": 0}, "cost_details": {"upstream_inference_cost": 0.001786596, "upstream_inference_prompt_cost": 6.2916e-05, "upstream_inference_completions_cost": 0.00172368}, "completion_tokens_details": {"reasoning_tokens": 875, "image_tokens": 0, "audio_tokens": 0}}, "response_id": "gen-1781609268-EMdYSZJAzBXYAKB2xOSA", "finish_reason": "stop", "score": {"passed": true, "score": 1.0, "details": {"matched": ["int(r['ts'])", "float(r['ts'])", "key=lambda r: int(r['ts'])", "key=lambda r: float(r['ts'])"]}}, "error": null, "rescored_from": "/Users/dracohu/REPO/dracobench/runs/v0.3-challenge100-minimax-minimax-m3-ability16384.jsonl", "scoring_policy": "text_rules_length_diagnostic_only+v03_alias_relaxations_round2"} {"case_id": "challenge-debugging-011", "suite": "debugging", "case_version": "0.2.0", "tags": ["python", "truthiness", "debugging"], "scorer": "regex", "expected": {"pattern": "FIX:\\s*`?\\s*(?:limit\\s*=\\s*10\\s+if\\s+limit\\s+is\\s+None\\s+else\\s+limit|limit\\s*=\\s*limit\\s+if\\s+limit\\s+is\\s+not\\s+None\\s+else\\s+10|if\\s+limit\\s+is\\s+None\\s*:\\s*limit\\s*=\\s*10)\\s*`?", "flags": "is"}, "model": "minimax/minimax-m3", "provider": null, "prompt": "下面代码在输入包含 0 时会误判为默认值 10。请指出 bug，并给出最小修复。最后单独一行写 FIX: <修复后的 limit 赋值行>。\n\n```python\ndef query(limit=None):\n limit = limit or 10\n return list(range(limit))\n```", "parameters": {"temperature": 0, "max_tokens": 16384, "seed": null}, "prompt_hash": "81dfcde4613fe16d52b7386d005b50beaafcb9916e6cb6c0d326eab3c808a843", "started_at": "2026-06-16T11:28:11.092616+00:00", "latency_ms": 16267, "output": "**Bug 分析**\n\n`limit or 10` 利用了 Python 的真值判断，而 `0` 是 falsy 值，所以当传入 `limit=0` 时会被替换成 `10`，丢失了\"上限为 0\"的语义。\n\n**最小修复**：用 `is None` 显式判断\"未传入\"，而不是依赖真值。\n\n```python\ndef query(limit=None):\n limit = 10 if limit is None else limit\n return list(range(limit))\n```\n\nFIX: ` limit = 10 if limit is None else limit`", "usage": {"prompt_tokens": 256, "completion_tokens": 244, "total_tokens": 500, "cost": 0.00032376, "is_byok": false, "prompt_tokens_details": {"cached_tokens": 191, "cache_write_tokens": 0, "audio_tokens": 0, "video_tokens": 0}, "cost_details": {"upstream_inference_cost": 0.00032376, "upstream_inference_prompt_cost": 3.096e-05, "upstream_inference_completions_cost": 0.0002928}, "completion_tokens_details": {"reasoning_tokens": 133, "image_tokens": 0, "audio_tokens": 0}}, "response_id": "gen-1781609293-9Fr3pWqDcE7JbSDKVie2", "finish_reason": "stop", "score": {"passed": true, "score": 1.0, "details": {"pattern": "FIX:\\s*`?\\s*(?:limit\\s*=\\s*10\\s+if\\s+limit\\s+is\\s+None\\s+else\\s+limit|limit\\s*=\\s*limit\\s+if\\s+limit\\s+is\\s+not\\s+None\\s+else\\s+10|if\\s+limit\\s+is\\s+None\\s*:\\s*limit\\s*=\\s*10)\\s*`?"}}, "error": null, "rescored_from": "/Users/dracohu/REPO/dracobench/runs/v0.3-challenge100-minimax-minimax-m3-ability16384.jsonl", "scoring_policy": "text_rules_length_diagnostic_only+v03_alias_relaxations_round2"} {"case_id": "challenge-debugging-012", "suite": "debugging", "case_version": "0.2.0", "tags": ["python", "deepcopy", "debugging"], "scorer": "regex", "expected": {"pattern": "FIX:\\s*`?\\s*copied\\s*=\\s*(?:copy\\.deepcopy\\(config\\)|__import__\\(['\\\"]copy['\\\"]\\)\\.deepcopy\\(config\\))\\s*`?", "flags": "is"}, "model": "minimax/minimax-m3", "provider": null, "prompt": "下面代码期望复制嵌套配置后修改副本，但原配置也被修改。请指出 bug，并给出最小修复。最后单独一行写 FIX: <修复后的 copied 赋值行>。\n\n```python\ndef update(config):\n copied = dict(config)\n copied['db']['host'] = 'localhost'\n return copied\n```", "parameters": {"temperature": 0, "max_tokens": 16384, "seed": null}, "prompt_hash": "b9ce1efc39e6db4861310a8b269b4f7a5f68b90016ca7ebbef71eb8b6b011913", "started_at": "2026-06-16T11:28:27.374207+00:00", "latency_ms": 25907, "output": "**Bug 分析**\n\n`dict(config)` 只做了**浅拷贝**：`copied` 是一个新字典，但 `copied['db']` 仍然指向 `config['db']` 同一个嵌套字典对象。因此 `copied['db']['host'] = 'localhost'` 会同时修改原配置。\n\n**最小修复**\n\n使用 `copy.deepcopy` 进行深拷贝（需 `import copy`）。\n\nFIX: copied = copy.deepcopy(config)", "usage": {"prompt_tokens": 259, "completion_tokens": 284, "total_tokens": 543, "cost": 0.00037962, "is_byok": false, "prompt_tokens_details": {"cached_tokens": 162, "cache_write_tokens": 0, "audio_tokens": 0, "video_tokens": 0}, "cost_details": {"upstream_inference_cost": 0.00037962, "upstream_inference_prompt_cost": 3.882e-05, "upstream_inference_completions_cost": 0.0003408}, "completion_tokens_details": {"reasoning_tokens": 203, "image_tokens": 0, "audio_tokens": 0}}, "response_id": "gen-1781609309-OxE1EiVt9NSVCEnRjmbz", "finish_reason": "stop", "score": {"passed": true, "score": 1.0, "details": {"pattern": "FIX:\\s*`?\\s*copied\\s*=\\s*(?:copy\\.deepcopy\\(config\\)|__import__\\(['\\\"]copy['\\\"]\\)\\.deepcopy\\(config\\))\\s*`?"}}, "error": null, "rescored_from": "/Users/dracohu/REPO/dracobench/runs/v0.3-challenge100-minimax-minimax-m3-ability16384.jsonl", "scoring_policy": "text_rules_length_diagnostic_only+v03_alias_relaxations_round2"} {"case_id": "challenge-debugging-013", "suite": "debugging", "case_version": "0.2.0", "tags": ["python", "generator", "debugging"], "scorer": "regex", "expected": {"pattern": "\\*{0,2}FIX\\*{0,2}:\\*{0,2}\\s*`?\\s*return\\s+next\\(\\(u\\s+for\\s+u\\s+in\\s+users\\s+if\\s+u\\['name'\\]\\s*==\\s*name\\),\\s*None\\)\\s*`?", "flags": "is"}, "model": "minimax/minimax-m3", "provider": null, "prompt": "下面代码期望找到第一个匹配项，但找不到时会抛 StopIteration。请指出 bug，并给出最小修复。最后单独一行写 FIX: <修复后的 return 行>。\n\n```python\ndef find_user(users, name):\n return next(u for u in users if u['name'] == name)\n```", "parameters": {"temperature": 0, "max_tokens": 16384, "seed": null}, "prompt_hash": "3f10be3c52149f96743fe37c0fb7327abfb5c8d4b9310088d76b04282b552f5b", "started_at": "2026-06-16T11:28:53.294691+00:00", "latency_ms": 5700, "output": "## Bug 分析\n\n`next()` 在没有提供默认值时，若迭代器耗尽会抛出 `StopIteration` 异常。这里生成器表达式找不到匹配项时即会触发，导致函数在未找到用户时崩溃。\n\n## 最小修复\n\n为 `next()` 传入默认值 `None`：\n\n```python\ndef find_user(users, name):\n return next((u for u in users if u['name'] == name), None)\n```\n\nFIX: `return next((u for u in users if u['name'] == name), None)`", "usage": {"prompt_tokens": 254, "completion_tokens": 271, "total_tokens": 525, "cost": 0.00037404, "is_byok": false, "prompt_tokens_details": {"cached_tokens": 114, "cache_write_tokens": 0, "audio_tokens": 0, "video_tokens": 0}, "cost_details": {"upstream_inference_cost": 0.00037404, "upstream_inference_prompt_cost": 4.884e-05, "upstream_inference_completions_cost": 0.0003252}, "completion_tokens_details": {"reasoning_tokens": 166, "image_tokens": 0, "audio_tokens": 0}}, "response_id": "gen-1781609335-JsWctqc4zuf1GOmGuBtk", "finish_reason": "stop", "score": {"passed": true, "score": 1.0, "details": {"pattern": "\\*{0,2}FIX\\*{0,2}:\\*{0,2}\\s*`?\\s*return\\s+next\\(\\(u\\s+for\\s+u\\s+in\\s+users\\s+if\\s+u\\['name'\\]\\s*==\\s*name\\),\\s*None\\)\\s*`?"}}, "error": null, "rescored_from": "/Users/dracohu/REPO/dracobench/runs/v0.3-challenge100-minimax-minimax-m3-ability16384.jsonl", "scoring_policy": "text_rules_length_diagnostic_only+v03_alias_relaxations_round2"} {"case_id": "challenge-debugging-014", "suite": "debugging", "case_version": "0.2.0", "tags": ["python", "mutable-default", "debugging"], "scorer": "regex", "expected": {"pattern": "FIX:\\s*(?:```(?:python)?\\s*)?`?\\s*def\\s+log_request\\(req,\\s*logs=None\\)\\s*:", "flags": "is"}, "model": "minimax/minimax-m3", "provider": null, "prompt": "下面代码期望按请求顺序记录日志，但默认参数导致多次调用共用同一个列表。请指出 bug，并给出最小修复。最后单独一行写 FIX: <修复后的函数定义行>。\n\n```python\ndef log_request(req, logs=[]):\n logs.append(req['id'])\n return logs\n```", "parameters": {"temperature": 0, "max_tokens": 16384, "seed": null}, "prompt_hash": "2131fc867d62d7093265e77811a401805ff12d38e7bbdc5e8797b3d01b4c8c59", "started_at": "2026-06-16T11:28:59.009513+00:00", "latency_ms": 10840, "output": "## Bug 分析\n\nPython 的默认参数在函数**定义时**只求值一次，而不是每次调用时重新创建。`logs=[]` 这个列表对象在函数定义时就已经生成并驻留，所有不传 `logs` 的调用都会共享同一个列表，导致日志被串到一起。\n\n```python\nlog_request({'id': 1}) # [1]\nlog_request({'id': 2}) # [1, 2] ← 期望 [2]\nlog_request({'id': 3}) # [1, 2, 3] ← 期望 [3]\n```\n\n## 最小修复\n\n把默认值改成 `None`，在函数体内惰性创建新列表：\n\n```python\ndef log_request(req, logs=None):\n if logs is None:\n logs = []\n logs.append(req['id'])\n return logs\n```\n\n这样每次不传 `logs` 的调用都会得到一个全新的列表，行为符合\"按请求顺序独立记录\"的预期。\n\nFIX: def log_request(req, logs=None):", "usage": {"prompt_tokens": 253, "completion_tokens": 399, "total_tokens": 652, "cost": 0.0011094, "is_byok": false, "prompt_tokens_details": {"cached_tokens": 0, "cache_write_tokens": 0, "audio_tokens": 0, "video_tokens": 0}, "cost_details": {"upstream_inference_cost": 0.0011094, "upstream_inference_prompt_cost": 0.0001518, "upstream_inference_completions_cost": 0.0009576}, "completion_tokens_details": {"reasoning_tokens": 221, "image_tokens": 0, "audio_tokens": 0}}, "response_id": "gen-1781609341-ZoSuKiZxFw5ETKFFxgJB", "finish_reason": "stop", "score": {"passed": true, "score": 1.0, "details": {"pattern": "FIX:\\s*(?:```(?:python)?\\s*)?`?\\s*def\\s+log_request\\(req,\\s*logs=None\\)\\s*:"}}, "error": null, "rescored_from": "/Users/dracohu/REPO/dracobench/runs/v0.3-challenge100-minimax-minimax-m3-ability16384.jsonl", "scoring_policy": "text_rules_length_diagnostic_only+v03_alias_relaxations_round2"} {"case_id": "challenge-reasoning-006", "suite": "reasoning", "case_version": "0.2.0", "tags": ["symbolic", "sequence"], "scorer": "exact", "expected": {"answer": "3", "normalize": true}, "model": "minimax/minimax-m3", "provider": null, "prompt": "只回答最终数字：一个队列从左到右是 A,B,C,D,E。依次执行：把第 2 个移到末尾；删除第 3 个；在最前面插入 X；把最后两个反转。最终队列中 C 的位置是第几位？", "parameters": {"temperature": 0, "max_tokens": 16384, "seed": null}, "prompt_hash": "0defd3c3114d287777b39b65961dfa6fd7469da02e6c49a8c170b4f1be17f971", "started_at": "2026-06-16T11:29:09.865181+00:00", "latency_ms": 4140, "output": "3", "usage": {"prompt_tokens": 247, "completion_tokens": 173, "total_tokens": 420, "cost": 0.00025434, "is_byok": false, "prompt_tokens_details": {"cached_tokens": 114, "cache_write_tokens": 0, "audio_tokens": 0, "video_tokens": 0}, "cost_details": {"upstream_inference_cost": 0.00025434, "upstream_inference_prompt_cost": 4.674e-05, "upstream_inference_completions_cost": 0.0002076}, "completion_tokens_details": {"reasoning_tokens": 133, "image_tokens": 0, "audio_tokens": 0}}, "response_id": "gen-1781609352-xo7hnk7hdMAlC5f58zgH", "finish_reason": "stop", "score": {"passed": true, "score": 1.0, "details": {"expected": "3", "actual": "3"}}, "error": null, "rescored_from": "/Users/dracohu/REPO/dracobench/runs/v0.3-challenge100-minimax-minimax-m3-ability16384.jsonl", "scoring_policy": "text_rules_length_diagnostic_only+v03_alias_relaxations_round2"} {"case_id": "challenge-reasoning-007", "suite": "reasoning", "case_version": "0.2.0", "tags": ["percentage", "metrics"], "scorer": "exact", "expected": {"answer": "120", "normalize": true}, "model": "minimax/minimax-m3", "provider": null, "prompt": "只回答最终数字：某缓存命中率从 60% 提升到 75%。如果总请求数为 800，未命中请求减少了多少个？", "parameters": {"temperature": 0, "max_tokens": 16384, "seed": null}, "prompt_hash": "b5b568242a1b6c56d53db2d69addca2d25e472dcc9fb5aa8669dfa273609c0d4", "started_at": "2026-06-16T11:29:14.020036+00:00", "latency_ms": 12461, "output": "120120", "usage": {"prompt_tokens": 225, "completion_tokens": 18, "total_tokens": 243, "cost": 8.6436e-05, "is_byok": false, "prompt_tokens_details": {"cached_tokens": 114, "cache_write_tokens": 0, "audio_tokens": 0, "video_tokens": 0}, "cost_details": {"upstream_inference_cost": 8.6436e-05, "upstream_inference_prompt_cost": 5.6196e-05, "upstream_inference_completions_cost": 3.024e-05}, "completion_tokens_details": {"reasoning_tokens": 16, "image_tokens": 0, "audio_tokens": 0}}, "response_id": "gen-1781609356-zqt5zuySIAwBPpjsjr0g", "finish_reason": "stop", "score": {"passed": false, "score": 0.0, "details": {"expected": "120", "actual": "120120"}}, "error": null, "rescored_from": "/Users/dracohu/REPO/dracobench/runs/v0.3-challenge100-minimax-minimax-m3-ability16384.jsonl", "scoring_policy": "text_rules_length_diagnostic_only+v03_alias_relaxations_round2"} {"case_id": "challenge-rag-005", "suite": "rag_long_context", "case_version": "0.2.0", "tags": ["rag", "mode-comparison"], "scorer": "text_rules", "expected": {"required": ["ability mode", "efficiency mode", "真实能力", "低预算"], "forbidden": ["只看一个模式"], "max_chars": 180}, "model": "minimax/minimax-m3", "provider": null, "prompt": "只根据资料回答。\n\n资料：A 模型在 ability mode 中通过 46/50，成本 0.12 美元；在 efficiency mode 中通过 34/50，成本 0.04 美元。报告建议把 ability mode 用于能力判断，把 efficiency mode 用于成本和稳定性判断。\n\n问题：如果文章讨论“模型真实能力”，应该优先引用哪个模式？如果讨论“低预算可用性”，应该优先引用哪个模式？", "parameters": {"temperature": 0, "max_tokens": 16384, "seed": null}, "prompt_hash": "0265f7540e44005627e16c6995d2a9cb458be65bb560cd1d5646c0435265c4e3", "started_at": "2026-06-16T11:29:26.494876+00:00", "latency_ms": 7972, "output": "根据资料：\n\n1. **讨论\"模型真实能力\"**：应优先引用 **ability mode**。该模式通过率更高（46/50），且报告明确建议将其用于能力判断。\n\n2. **讨论\"低预算可用性\"**：应优先引用 **efficiency mode**。该模式成本更低（0.04 美元），且报告建议将其用于成本和稳定性判断。", "usage": {"prompt_tokens": 277, "completion_tokens": 296, "total_tokens": 573, "cost": 0.0008766, "is_byok": false, "prompt_tokens_details": {"cached_tokens": 0, "cache_write_tokens": 0, "audio_tokens": 0, "video_tokens": 0}, "cost_details": {"upstream_inference_cost": 0.0008766, "upstream_inference_prompt_cost": 0.0001662, "upstream_inference_completions_cost": 0.0007104}, "completion_tokens_details": {"reasoning_tokens": 257, "image_tokens": 0, "audio_tokens": 0}}, "response_id": "gen-1781609368-VGQkkkWrEgkDwZaO1K8d", "finish_reason": "stop", "score": {"passed": true, "score": 1.0, "details": {"missing": [], "missing_any": [], "present_forbidden": [], "char_count": 161, "length_ok": true, "length_scored": false}}, "error": null, "rescored_from": "/Users/dracohu/REPO/dracobench/runs/v0.3-challenge100-minimax-minimax-m3-ability16384.jsonl", "scoring_policy": "text_rules_length_diagnostic_only+v03_alias_relaxations_round2"} {"case_id": "challenge-rag-006", "suite": "rag_long_context", "case_version": "0.2.0", "tags": ["rag", "failure-analysis"], "scorer": "text_rules", "expected": {"required": ["key"], "required_any": [["字符串", "类型不匹配"], ["整数", "int"], ["类型假设", "假设 key 为整数", "假设 key 是整数", "key 为整数类型"]], "forbidden": [], "max_chars": 120}, "model": "minimax/minimax-m3", "provider": null, "prompt": "只根据资料回答。\n\n资料：一次失败分析显示，模型输出了 key = int(parts[1])，但测试数据里的 key 是字符串 a、b、c。报告认为这是“擅自补类型假设”，不是 token 截断。\n\n问题：这次失败的根因是什么？", "parameters": {"temperature": 0, "max_tokens": 16384, "seed": null}, "prompt_hash": "a49a29fca4961af6e5e2b25c2e82cc0a7be110d48893fc55f7af30f6e1804362", "started_at": "2026-06-16T11:29:34.481531+00:00", "latency_ms": 9194, "output": "根因是**擅自补类型假设**：模型在没有依据的情况下，自行假设 key 应为整数类型并执行了 `int(parts[1])` 转换，而实际测试数据中的 key 是字符串（a、b、c）。报告明确指出这不是 token 截断问题，而是模型越权做出了类型推断。", "usage": {"prompt_tokens": 254, "completion_tokens": 261, "total_tokens": 515, "cost": 0.00035052, "is_byok": false, "prompt_tokens_details": {"cached_tokens": 162, "cache_write_tokens": 0, "audio_tokens": 0, "video_tokens": 0}, "cost_details": {"upstream_inference_cost": 0.00035052, "upstream_inference_prompt_cost": 3.732e-05, "upstream_inference_completions_cost": 0.0003132}, "completion_tokens_details": {"reasoning_tokens": 232, "image_tokens": 0, "audio_tokens": 0}}, "response_id": "gen-1781609376-LahqjcxsK0y0p3ydgX69", "finish_reason": "stop", "score": {"passed": true, "score": 1.0, "details": {"missing": [], "missing_any": [], "present_forbidden": [], "char_count": 127, "length_ok": false, "length_scored": false}}, "error": null, "rescored_from": "/Users/dracohu/REPO/dracobench/runs/v0.3-challenge100-minimax-minimax-m3-ability16384.jsonl", "scoring_policy": "text_rules_length_diagnostic_only+v03_alias_relaxations_round2"} {"case_id": "challenge-rag-007", "suite": "rag_long_context", "case_version": "0.2.0", "tags": ["rag", "abstain"], "scorer": "text_rules", "expected": {"required": ["图表", "资料"], "required_any": [["提到", "提及"]], "forbidden": ["已经支持", "折线图", "柱状图"], "max_chars": 100}, "model": "minimax/minimax-m3", "provider": null, "prompt": "只根据资料回答，不要自行补充。\n\n资料：DracoBench 的 HTML 报告目前展示 Summary、By Suite、Failure Examples 和 Case Details。Failure Examples 中包括 Prompt、Mistake Analysis、Scorer Details 和 Output。资料没有提到图表功能。\n\n问题：报告是否已经支持图表？回答时说明依据。", "parameters": {"temperature": 0, "max_tokens": 16384, "seed": null}, "prompt_hash": "c47ee5e02396d5ba9d3852992bf82351aeea58cd22d81035d26a6bac283f07ba", "started_at": "2026-06-16T11:29:43.689821+00:00", "latency_ms": 3717, "output": "**不支持。**\n\n**依据：** 资料明确说明 HTML 报告目前展示的内容为 Summary、By Suite、Failure Examples 和 Case Details，其中 Failure Examples 包含 Prompt、Mistake Analysis、Scorer Details 和 Output。资料最后特别指出\"资料没有提到图表功能\"，因此根据现有资料，报告尚未支持图表。", "usage": {"prompt_tokens": 259, "completion_tokens": 205, "total_tokens": 464, "cost": 0.00029634, "is_byok": false, "prompt_tokens_details": {"cached_tokens": 114, "cache_write_tokens": 0, "audio_tokens": 0, "video_tokens": 0}, "cost_details": {"upstream_inference_cost": 0.00029634, "upstream_inference_prompt_cost": 5.034e-05, "upstream_inference_completions_cost": 0.000246}, "completion_tokens_details": {"reasoning_tokens": 183, "image_tokens": 0, "audio_tokens": 0}}, "response_id": "gen-1781609385-NtGebt7JP1OMGODGvfqt", "finish_reason": "stop", "score": {"passed": true, "score": 1.0, "details": {"missing": [], "missing_any": [], "present_forbidden": [], "char_count": 200, "length_ok": false, "length_scored": false}}, "error": null, "rescored_from": "/Users/dracohu/REPO/dracobench/runs/v0.3-challenge100-minimax-minimax-m3-ability16384.jsonl", "scoring_policy": "text_rules_length_diagnostic_only+v03_alias_relaxations_round2"} {"case_id": "challenge-if-004", "suite": "instruction_following", "case_version": "0.2.0", "tags": ["json", "nested", "strict"], "scorer": "json_schema_lite", "expected": {"schema": {"type": "object", "required": ["mode", "limits", "warnings"], "additionalProperties": false, "properties": {"mode": {"type": "string", "const": "ability"}, "limits": {"type": "object", "required": ["max_tokens", "temperature"], "additionalProperties": false, "properties": {"max_tokens": {"type": "integer", "const": 4096}, "temperature": {"type": "integer", "const": 0}}}, "warnings": {"type": "array", "minItems": 2, "maxItems": 2, "items": {"type": "string"}}}}}, "model": "minimax/minimax-m3", "provider": null, "prompt": "只输出 JSON 对象。字段 mode 必须是 \"ability\"；字段 limits 必须是对象，包含 max_tokens:4096 和 temperature:0；字段 warnings 必须是正好两个字符串组成的数组。不要 Markdown。", "parameters": {"temperature": 0, "max_tokens": 16384, "seed": null}, "prompt_hash": "14a17039d4b99be719ac311a5d824ceb40affbf22079ddbcf8bf33af6f04fb0a", "started_at": "2026-06-16T11:29:47.430906+00:00", "latency_ms": 2163, "output": "{\"mode\":\"ability\",\"limits\":{\"max_tokens\":4096,\"temperature\":0},\"warnings\":[\"strict output\",\"no markdown\"]}", "usage": {"prompt_tokens": 235, "completion_tokens": 75, "total_tokens": 310, "cost": 0.00013314, "is_byok": false, "prompt_tokens_details": {"cached_tokens": 114, "cache_write_tokens": 0, "audio_tokens": 0, "video_tokens": 0}, "cost_details": {"upstream_inference_cost": 0.00013314, "upstream_inference_prompt_cost": 4.314e-05, "upstream_inference_completions_cost": 9e-05}, "completion_tokens_details": {"reasoning_tokens": 55, "image_tokens": 0, "audio_tokens": 0}}, "response_id": "gen-1781609389-weRyF5y8p6dxy84VZpHr", "finish_reason": "stop", "score": {"passed": true, "score": 1.0, "details": {"errors": [], "value": {"mode": "ability", "limits": {"max_tokens": 4096, "temperature": 0}, "warnings": ["strict output", "no markdown"]}}}, "error": null, "rescored_from": "/Users/dracohu/REPO/dracobench/runs/v0.3-challenge100-minimax-minimax-m3-ability16384.jsonl", "scoring_policy": "text_rules_length_diagnostic_only+v03_alias_relaxations_round2"} {"case_id": "challenge-if-005", "suite": "instruction_following", "case_version": "0.2.0", "tags": ["markdown", "table"], "scorer": "regex", "expected": {"pattern": "^\\s*\\|\\s*模式\\s*\\|\\s*用途\\s*\\|\\s*风险\\s*\\|\\s*\\n\\s*\\|\\s*-+\\s*\\|\\s*-+\\s*\\|\\s*-+\\s*\\|[\\s\\S]*ability mode[\\s\\S]*efficiency mode[\\s\\S]*$", "flags": "is"}, "model": "minimax/minimax-m3", "provider": null, "prompt": "只输出 Markdown 表格，必须有 3 列：模式、用途、风险。必须包含 ability mode 和 efficiency mode 两行。不能添加表格外说明。", "parameters": {"temperature": 0, "max_tokens": 16384, "seed": null}, "prompt_hash": "0a28a65a8a714a5c613c2e01f5bf2b72b7b18cdd17059ad9d38cea38a6e1ff4f", "started_at": "2026-06-16T11:29:49.607615+00:00", "latency_ms": 8214, "output": "| 模式 | 用途 | 风险 |\n|------|------|------|\n| ability mode | 启用模型全部能力以处理复杂任务 | 资源消耗高，可能产生不可预测输出 |\n| efficiency mode | 优化资源使用以提升响应速度 | 可能牺牲部分能力，输出精度下降 |", "usage": {"prompt_tokens": 226, "completion_tokens": 124, "total_tokens": 350, "cost": 0.00017772, "is_byok": false, "prompt_tokens_details": {"cached_tokens": 162, "cache_write_tokens": 0, "audio_tokens": 0, "video_tokens": 0}, "cost_details": {"upstream_inference_cost": 0.00017772, "upstream_inference_prompt_cost": 2.892e-05, "upstream_inference_completions_cost": 0.0001488}, "completion_tokens_details": {"reasoning_tokens": 63, "image_tokens": 0, "audio_tokens": 0}}, "response_id": "gen-1781609391-262YlmhZyM9GSY8CIVDI", "finish_reason": "stop", "score": {"passed": true, "score": 1.0, "details": {"pattern": "^\\s*\\|\\s*模式\\s*\\|\\s*用途\\s*\\|\\s*风险\\s*\\|\\s*\\n\\s*\\|\\s*-+\\s*\\|\\s*-+\\s*\\|\\s*-+\\s*\\|[\\s\\S]*ability mode[\\s\\S]*efficiency mode[\\s\\S]*$"}}, "error": null, "rescored_from": "/Users/dracohu/REPO/dracobench/runs/v0.3-challenge100-minimax-minimax-m3-ability16384.jsonl", "scoring_policy": "text_rules_length_diagnostic_only+v03_alias_relaxations_round2"} {"case_id": "challenge-zh-writing-003", "suite": "chinese_writing", "case_version": "0.2.0", "tags": ["wechat", "explanation"], "scorer": "text_rules", "expected": {"required": ["原题", "错误解释", "可复查"], "forbidden": ["玄学"], "max_chars": 140}, "model": "minimax/minimax-m3", "provider": null, "prompt": "写一段不超过 140 个汉字的公众号小结，主题是“为什么要同时展示原题、模型输出和错误解释”。必须包含“原题”“错误解释”“可复查”，不能出现“玄学”。", "parameters": {"temperature": 0.2, "max_tokens": 16384, "seed": null}, "prompt_hash": "f0853803e81259a7bee0074883cc95566fe68e7e6f9ce84b4c3bda73b8c0e638", "started_at": "2026-06-16T11:29:57.834200+00:00", "latency_ms": 34642, "output": "为什么必须把**原题**、模型输出、**错误解释**一并展示？三者并列，读者才能看清问题背景、AI 回答与失误所在。每条错误解释都是一次**可复查**的案例：帮助用户验证推理链条，也提醒团队持续打磨模型。透明公开，才能积累真正的信任。\n\n（正文约 93 字，符合要求）为什么必须把**原题**、模型输出、**错误解释**一并展示？三者并列，读者才能看清问题背景、AI 回答与失误所在。每条错误解释都是一次**可复查**的案例：帮助用户验证推理链条，也提醒团队持续打磨模型。透明公开，才能积累真正的信任。\n\n（正文约 93 字，符合要求）", "usage": {"prompt_tokens": 238, "completion_tokens": 1779, "total_tokens": 2017, "cost": 0.003050376, "is_byok": false, "prompt_tokens_details": {"cached_tokens": 114, "cache_write_tokens": 0, "audio_tokens": 0, "video_tokens": 0}, "cost_details": {"upstream_inference_cost": 0.003050376, "upstream_inference_prompt_cost": 6.1656e-05, "upstream_inference_completions_cost": 0.00298872}, "completion_tokens_details": {"reasoning_tokens": 1416, "image_tokens": 0, "audio_tokens": 0}}, "response_id": "gen-1781609400-iAAD8sFDL6txRQqjS1P1", "finish_reason": "stop", "score": {"passed": true, "score": 1.0, "details": {"missing": [], "missing_any": [], "present_forbidden": [], "char_count": 268, "length_ok": false, "length_scored": false}}, "error": null, "rescored_from": "/Users/dracohu/REPO/dracobench/runs/v0.3-challenge100-minimax-minimax-m3-ability16384.jsonl", "scoring_policy": "text_rules_length_diagnostic_only+v03_alias_relaxations_round2"} {"case_id": "challenge-reasoning-008", "suite": "reasoning", "case_version": "0.3.0", "tags": ["state-tracking", "branching"], "scorer": "exact", "expected": {"answer": "15", "normalize": true}, "model": "minimax/minimax-m3", "provider": null, "prompt": "只回答最终数字：变量初始为 x=2, y=5, z=1。若 x list[str]，解析一行简化 CSV：逗号分隔；字段可以用双引号包裹；引号内逗号保留；引号内两个连续双引号表示一个双引号；空字段保留；空格是普通字符，不要自动 trim。", "parameters": {"temperature": 0, "max_tokens": 16384, "seed": null}, "prompt_hash": "81b6be132a0538b92491e39887550c116b7f09684b97f8dd1ad7df5426e97adb", "started_at": "2026-06-16T11:33:30.198335+00:00", "latency_ms": 125609, "output": "```python\ndef solve(line: str) -> list[str]:\n result = []\n field = []\n in_quotes = False\n i = 0\n n = len(line)\n while i < n:\n c = line[i]\n if in_quotes:\n if c == '\"':\n if i + 1 < n and line[i + 1] == '\"':\n field.append('\"')\n i += 2\n else:\n in_quotes = False\n i += 1\n else:\n field.append(c)\n i += 1\n else:\n if c == '\"' and len(field) == 0:\n in_quotes = True\n i += 1\n elif c == ',':\n result.append(''.join(field))\n field = []\n i += 1\n else:\n field.append(c)\n i += 1\n result.append(''.join(field))\n return result\n```", "usage": {"prompt_tokens": 257, "completion_tokens": 2860, "total_tokens": 3117, "cost": 0.0070182, "is_byok": false, "prompt_tokens_details": {"cached_tokens": 0, "cache_write_tokens": 0, "audio_tokens": 0, "video_tokens": 0}, "cost_details": {"upstream_inference_cost": 0.0070182, "upstream_inference_prompt_cost": 0.0001542, "upstream_inference_completions_cost": 0.006864}, "completion_tokens_details": {"reasoning_tokens": 2478, "image_tokens": 0, "audio_tokens": 0}}, "response_id": "gen-1781609612-ez50E4B3i2DXqo2qbo5K", "finish_reason": "stop", "score": {"passed": true, "score": 1.0, "details": {"returncode": 0, "stdout": "", "stderr": ""}}, "error": null, "rescored_from": "/Users/dracohu/REPO/dracobench/runs/v0.3-challenge100-minimax-minimax-m3-ability16384.jsonl", "scoring_policy": "text_rules_length_diagnostic_only+v03_alias_relaxations_round2"} {"case_id": "challenge-coding-016", "suite": "coding", "case_version": "0.3.0", "tags": ["python", "intervals", "sorting"], "scorer": "code_python_tests", "expected": {"tests": ["assert solution.solve([(5,7),(1,2),(3,4),(10,10)]) == [(1,7),(10,10)]", "assert solution.solve([(1,1),(3,3)]) == [(1,1),(3,3)]", "assert solution.solve([]) == []"], "timeout_seconds": 3}, "model": "minimax/minimax-m3", "provider": null, "prompt": "只输出 Python 代码，不要解释。实现 solve(intervals: list[tuple[int, int]]) -> list[tuple[int, int]]。intervals 是整数闭区间，先按起点排序；重叠或端点相邻的区间要合并，例如 [1,2] 和 [3,4] 合并为 [1,4]。", "parameters": {"temperature": 0, "max_tokens": 16384, "seed": null}, "prompt_hash": "6dc22c1e6ae004a46c47dc11d5cc9ed5d57f09ec77549ff0ada57b459929d814", "started_at": "2026-06-16T11:35:35.857672+00:00", "latency_ms": 6238, "output": "```python\ndef solve(intervals: list[tuple[int, int]]) -> list[tuple[int, int]]:\n if not intervals:\n return []\n intervals = sorted(intervals, key=lambda x: x[0])\n merged = [intervals[0]]\n for start, end in intervals[1:]:\n last_start, last_end = merged[-1]\n if start <= last_end + 1:\n merged[-1] = (last_start, max(last_end, end))\n else:\n merged.append((start, end))\n return merged\n```", "usage": {"prompt_tokens": 262, "completion_tokens": 527, "total_tokens": 789, "cost": 0.00068364, "is_byok": false, "prompt_tokens_details": {"cached_tokens": 114, "cache_write_tokens": 0, "audio_tokens": 0, "video_tokens": 0}, "cost_details": {"upstream_inference_cost": 0.00068364, "upstream_inference_prompt_cost": 5.124e-05, "upstream_inference_completions_cost": 0.0006324}, "completion_tokens_details": {"reasoning_tokens": 376, "image_tokens": 0, "audio_tokens": 0}}, "response_id": "gen-1781609738-424OuGtjlZs1tneqhSEH", "finish_reason": "stop", "score": {"passed": true, "score": 1.0, "details": {"returncode": 0, "stdout": "", "stderr": ""}}, "error": null, "rescored_from": "/Users/dracohu/REPO/dracobench/runs/v0.3-challenge100-minimax-minimax-m3-ability16384.jsonl", "scoring_policy": "text_rules_length_diagnostic_only+v03_alias_relaxations_round2"} {"case_id": "challenge-coding-017", "suite": "coding", "case_version": "0.3.0", "tags": ["python", "rate-limit", "state"], "scorer": "code_python_tests", "expected": {"tests": ["events=[('a',1),('a',2),('a',3),('a',4),('a',8)]\nassert solution.solve(events, 5, 2) == [True, True, False, False, True]", "assert solution.solve([('x',10),('x',11),('x',16)], 5, 1) == [True, False, True]"], "timeout_seconds": 3}, "model": "minimax/minimax-m3", "provider": null, "prompt": "只输出 Python 代码，不要解释。实现 solve(events: list[tuple[str, int]], window: int, limit: int) -> list[bool]，模拟按用户限流。events 按时间非降序排列。某用户在当前事件时间 ts 的窗口 (ts-window, ts] 内，已放行事件数少于 limit 才放行；被拒绝的事件不计入窗口。", "parameters": {"temperature": 0, "max_tokens": 16384, "seed": null}, "prompt_hash": "1539217453d6af4bac3c7abfbd8df43ed131cd647e58a0ad2a5a670794a91f80", "started_at": "2026-06-16T11:35:42.139585+00:00", "latency_ms": 13781, "output": "```python\nfrom collections import defaultdict, deque\n\ndef solve(events: list[tuple[str, int]], window: int, limit: int) -> list[bool]:\n user_events = defaultdict(deque)\n result = []\n for user, ts in events:\n while user_events[user] and user_events[user][0] <= ts - window:\n user_events[user].popleft()\n if len(user_events[user]) < limit:\n user_events[user].append(ts)\n result.append(True)\n else:\n result.append(False)\n return result\n```", "usage": {"prompt_tokens": 271, "completion_tokens": 1292, "total_tokens": 1563, "cost": 0.00160434, "is_byok": false, "prompt_tokens_details": {"cached_tokens": 114, "cache_write_tokens": 0, "audio_tokens": 0, "video_tokens": 0}, "cost_details": {"upstream_inference_cost": 0.00160434, "upstream_inference_prompt_cost": 5.394e-05, "upstream_inference_completions_cost": 0.0015504}, "completion_tokens_details": {"reasoning_tokens": 1131, "image_tokens": 0, "audio_tokens": 0}}, "response_id": "gen-1781609744-c81E6nytjm0YSGTvfmkA", "finish_reason": "stop", "score": {"passed": true, "score": 1.0, "details": {"returncode": 0, "stdout": "", "stderr": ""}}, "error": null, "rescored_from": "/Users/dracohu/REPO/dracobench/runs/v0.3-challenge100-minimax-minimax-m3-ability16384.jsonl", "scoring_policy": "text_rules_length_diagnostic_only+v03_alias_relaxations_round2"} {"case_id": "challenge-coding-018", "suite": "coding", "case_version": "0.3.0", "tags": ["python", "path", "stack"], "scorer": "code_python_tests", "expected": {"tests": ["assert solution.solve('/a//b/./c/../') == '/a/b'", "assert solution.solve('/../../x') == '/x'", "assert solution.solve('/') == '/'"], "timeout_seconds": 3}, "model": "minimax/minimax-m3", "provider": null, "prompt": "只输出 Python 代码，不要解释。实现 solve(path: str) -> str，规范化绝对 Unix 路径：处理重复斜杠、.、..；.. 在根目录时仍留在根目录；返回结果必须以 / 开头且除根目录外不能以 / 结尾。不要使用 os.path。", "parameters": {"temperature": 0, "max_tokens": 16384, "seed": null}, "prompt_hash": "92e706f1062d7652c2f14c514c44602c6708e65d87771889d3aff7450e33e85d", "started_at": "2026-06-16T11:35:55.970867+00:00", "latency_ms": 18183, "output": "def solve(path: str) -> str:\n parts = path.split('/')\n stack = []\n for part in parts:\n if part == '' or part == '.':\n continue\n elif part == '..':\n if stack:\n stack.pop()\n else:\n stack.append(part)\n return '/' + '/'.join(stack) if stack else '/'", "usage": {"prompt_tokens": 257, "completion_tokens": 1069, "total_tokens": 1326, "cost": 0.00133254, "is_byok": false, "prompt_tokens_details": {"cached_tokens": 114, "cache_write_tokens": 0, "audio_tokens": 0, "video_tokens": 0}, "cost_details": {"upstream_inference_cost": 0.00133254, "upstream_inference_prompt_cost": 4.974e-05, "upstream_inference_completions_cost": 0.0012828}, "completion_tokens_details": {"reasoning_tokens": 1057, "image_tokens": 0, "audio_tokens": 0}}, "response_id": "gen-1781609758-VcsNMJjEjlsfynhkm3IR", "finish_reason": "stop", "score": {"passed": true, "score": 1.0, "details": {"returncode": 0, "stdout": "", "stderr": ""}}, "error": null, "rescored_from": "/Users/dracohu/REPO/dracobench/runs/v0.3-challenge100-minimax-minimax-m3-ability16384.jsonl", "scoring_policy": "text_rules_length_diagnostic_only+v03_alias_relaxations_round2"} {"case_id": "challenge-coding-019", "suite": "coding", "case_version": "0.3.0", "tags": ["python", "grouping", "stable-order"], "scorer": "code_python_tests", "expected": {"tests": ["assert solution.solve(['eat','tea','tan','ate','nat','bat']) == [['eat','tea','ate'], ['tan','nat'], ['bat']]", "assert solution.solve(['b','B','bb']) == [['b'], ['B'], ['bb']]"], "timeout_seconds": 3}, "model": "minimax/minimax-m3", "provider": null, "prompt": "只输出 Python 代码，不要解释。实现 solve(words: list[str]) -> list[list[str]]，按变位词分组。大小写敏感；组内保持输入顺序；组的顺序按该组第一个单词在输入中出现的位置。", "parameters": {"temperature": 0, "max_tokens": 16384, "seed": null}, "prompt_hash": "0a957bf71a0be6fa198a854347b2fe5d2c5d5f9b9a8bd5041dac9cdb46ae06ce", "started_at": "2026-06-16T11:36:14.189766+00:00", "latency_ms": 25589, "output": "```python\nfrom collections import defaultdict\n\ndef solve(words: list[str]) -> list[list[str]]:\n groups: dict[tuple, list[str]] = {}\n for word in words:\n key = tuple(sorted(word))\n if key not in groups:\n groups[key] = []\n groups[key].append(word)\n return list(groups.values())\n``````python\nfrom collections import defaultdict\n\ndef solve(words: list[str]) -> list[list[str]]:\n groups: dict[tuple, list[str]] = {}\n for word in words:\n key = tuple(sorted(word))\n if key not in groups:\n groups[key] = []\n groups[key].append(word)\n return list(groups.values())\n```", "usage": {"prompt_tokens": 243, "completion_tokens": 663, "total_tokens": 906, "cost": 0.001177596, "is_byok": false, "prompt_tokens_details": {"cached_tokens": 114, "cache_write_tokens": 0, "audio_tokens": 0, "video_tokens": 0}, "cost_details": {"upstream_inference_cost": 0.001177596, "upstream_inference_prompt_cost": 6.3756e-05, "upstream_inference_completions_cost": 0.00111384}, "completion_tokens_details": {"reasoning_tokens": 553, "image_tokens": 0, "audio_tokens": 0}}, "response_id": "gen-1781609776-2N1EQoa4nTtCMbuSmUHP", "finish_reason": "stop", "score": {"passed": true, "score": 1.0, "details": {"returncode": 0, "stdout": "", "stderr": ""}}, "error": null, "rescored_from": "/Users/dracohu/REPO/dracobench/runs/v0.3-challenge100-minimax-minimax-m3-ability16384.jsonl", "scoring_policy": "text_rules_length_diagnostic_only+v03_alias_relaxations_round2"} {"case_id": "challenge-coding-020", "suite": "coding", "case_version": "0.3.0", "tags": ["python", "parser", "front-matter"], "scorer": "code_python_tests", "expected": {"tests": ["text='---\\ntitle: Test\\ntags: [ai, bench, v0]\\ndraft: false\\n---\\nbody'\nassert solution.solve(text) == {'title':'Test','tags':['ai','bench','v0'],'draft':'false'}", "assert solution.solve('hello\\n---\\na: b') == {}", "assert solution.solve('---\\na: 1') == {}"], "timeout_seconds": 3}, "model": "minimax/minimax-m3", "provider": null, "prompt": "只输出 Python 代码，不要解释。实现 solve(text: str) -> dict，解析 Markdown front matter。只有第一行正好是 --- 时才解析，直到下一行 --- 结束；每行 key: value，key 和 value 两侧 trim；形如 [a, b] 的 value 返回字符串列表；其他 value 返回字符串。没有完整 front matter 返回 {}。", "parameters": {"temperature": 0, "max_tokens": 16384, "seed": null}, "prompt_hash": "4111f2228a7b621a743019a4cad1b5638c8a120765153c2d35f8db2aa136f79c", "started_at": "2026-06-16T11:36:39.824213+00:00", "latency_ms": 26505, "output": "```python\ndef solve(text: str) -> dict:\n lines = text.split('\\n')\n if not lines or lines[0] != '---':\n return {}\n \n result = {}\n for line in lines[1:]:\n if line == '---':\n break\n if ':' not in line:\n continue\n key, _, value = line.partition(':')\n key = key.strip()\n value = value.strip()\n if value.startswith('[') and value.endswith(']'):\n inner = value[1:-1]\n if inner.strip() == '':\n result[key] = []\n else:\n result[key] = [item.strip() for item in inner.split(',')]\n else:\n result[key] = value\n else:\n return {}\n \n return result\n```", "usage": {"prompt_tokens": 267, "completion_tokens": 1808, "total_tokens": 2075, "cost": 0.00222234, "is_byok": false, "prompt_tokens_details": {"cached_tokens": 114, "cache_write_tokens": 0, "audio_tokens": 0, "video_tokens": 0}, "cost_details": {"upstream_inference_cost": 0.00222234, "upstream_inference_prompt_cost": 5.274e-05, "upstream_inference_completions_cost": 0.0021696}, "completion_tokens_details": {"reasoning_tokens": 1713, "image_tokens": 0, "audio_tokens": 0}}, "response_id": "gen-1781609802-oOvwoRrFgG0BjdQ1GVLX", "finish_reason": "stop", "score": {"passed": true, "score": 1.0, "details": {"returncode": 0, "stdout": "", "stderr": ""}}, "error": null, "rescored_from": "/Users/dracohu/REPO/dracobench/runs/v0.3-challenge100-minimax-minimax-m3-ability16384.jsonl", "scoring_policy": "text_rules_length_diagnostic_only+v03_alias_relaxations_round2"} {"case_id": "challenge-coding-021", "suite": "coding", "case_version": "0.3.0", "tags": ["python", "frequency", "stable-order"], "scorer": "code_python_tests", "expected": {"tests": ["assert solution.solve(['b','a','b','c','a','b'], 2) == ['b','a']", "assert solution.solve(['x','y','z','y','z','x'], 2) == ['x','y']", "assert solution.solve(['a'], 5) == ['a']"], "timeout_seconds": 3}, "model": "minimax/minimax-m3", "provider": null, "prompt": "只输出 Python 代码，不要解释。实现 solve(items: list[str], k: int) -> list[str]，返回出现次数最高的前 k 个元素；次数相同按第一次出现的先后排序；k 大于不同元素数量时返回全部。", "parameters": {"temperature": 0, "max_tokens": 16384, "seed": null}, "prompt_hash": "e73dccf0e991c296063c917fbe5a6b5f99f50fb3baed6d0820cfc35df858c663", "started_at": "2026-06-16T11:37:06.375788+00:00", "latency_ms": 51901, "output": "```python\ndef solve(items: list[str], k: int) -> list[str]:\n from collections import Counter\n \n if not items:\n return []\n \n freq = Counter(items)\n first_idx = {}\n for i, item in enumerate(items):\n if item not in first_idx:\n first_idx[item] = i\n \n sorted_items = sorted(first_idx.keys(), key=lambda x: (-freq[x], first_idx[x]))\n return sorted_items[:k]\n```", "usage": {"prompt_tokens": 243, "completion_tokens": 721, "total_tokens": 964, "cost": 0.00089922, "is_byok": false, "prompt_tokens_details": {"cached_tokens": 162, "cache_write_tokens": 0, "audio_tokens": 0, "video_tokens": 0}, "cost_details": {"upstream_inference_cost": 0.00089922, "upstream_inference_prompt_cost": 3.402e-05, "upstream_inference_completions_cost": 0.0008652}, "completion_tokens_details": {"reasoning_tokens": 605, "image_tokens": 0, "audio_tokens": 0}}, "response_id": "gen-1781609828-uE5qajajGmGfeQVx5Go1", "finish_reason": "stop", "score": {"passed": true, "score": 1.0, "details": {"returncode": 0, "stdout": "", "stderr": ""}}, "error": null, "rescored_from": "/Users/dracohu/REPO/dracobench/runs/v0.3-challenge100-minimax-minimax-m3-ability16384.jsonl", "scoring_policy": "text_rules_length_diagnostic_only+v03_alias_relaxations_round2"} {"case_id": "challenge-coding-022", "suite": "coding", "case_version": "0.3.0", "tags": ["python", "nested-dict", "copy"], "scorer": "code_python_tests", "expected": {"tests": ["base={'db':{'host':'old','port':5432},'feature':{'x':1}}\noverrides={'db.host':'new','feature.x':None,'api.timeout':30}\nres=solution.solve(base, overrides)\nassert res == {'db':{'host':'new','port':5432},'feature':{},'api':{'timeout':30}}\nassert base == {'db':{'host':'old','port':5432},'feature':{'x':1}}", "assert solution.solve({'a':1}, {'a.b':2}) == {'a': {'b': 2}}"], "timeout_seconds": 3}, "model": "minimax/minimax-m3", "provider": null, "prompt": "只输出 Python 代码，不要解释。实现 solve(config: dict, overrides: dict[str, object]) -> dict。overrides 的 key 是点号路径；值为 None 表示删除该路径的叶子 key；其他值写入路径，必要时创建中间 dict；不能修改输入 config。", "parameters": {"temperature": 0, "max_tokens": 16384, "seed": null}, "prompt_hash": "e21e5bbf42159ba49144d39d17824dcf011df026efab7a9bfb6179aed2bb558a", "started_at": "2026-06-16T11:37:58.323390+00:00", "latency_ms": 35115, "output": "```python\ndef solve(config: dict, overrides: dict[str, object]) -> dict:\n import copy\n result = copy.deepcopy(config)\n \n for key_path, value in overrides.items():\n parts = key_path.split('.')\n \n if value is None:\n current = result\n for part in parts[:-1]:\n if not isinstance(current, dict) or part not in current:\n current = None\n break\n current = current[part]\n if isinstance(current, dict) and parts[-1] in current:\n del current[parts[-1]]\n else:\n current = result\n for part in parts[:-1]:\n if part not in current or not isinstance(current[part], dict):\n current[part] = {}\n current = current[part]\n current[parts[-1]] = value\n \n return result\n```", "usage": {"prompt_tokens": 251, "completion_tokens": 2768, "total_tokens": 3019, "cost": 0.00336954, "is_byok": false, "prompt_tokens_details": {"cached_tokens": 114, "cache_write_tokens": 0, "audio_tokens": 0, "video_tokens": 0}, "cost_details": {"upstream_inference_cost": 0.00336954, "upstream_inference_prompt_cost": 4.794e-05, "upstream_inference_completions_cost": 0.0033216}, "completion_tokens_details": {"reasoning_tokens": 2705, "image_tokens": 0, "audio_tokens": 0}}, "response_id": "gen-1781609880-cJYeIgrHQVf6lH2ssMNs", "finish_reason": "stop", "score": {"passed": true, "score": 1.0, "details": {"returncode": 0, "stdout": "", "stderr": ""}}, "error": null, "rescored_from": "/Users/dracohu/REPO/dracobench/runs/v0.3-challenge100-minimax-minimax-m3-ability16384.jsonl", "scoring_policy": "text_rules_length_diagnostic_only+v03_alias_relaxations_round2"} {"case_id": "challenge-coding-023", "suite": "coding", "case_version": "0.3.0", "tags": ["python", "url", "parser"], "scorer": "code_python_tests", "expected": {"tests": ["assert solution.solve('a=1&b=two+words&a=3') == {'a':['1','3'], 'b':['two words']} ", "assert solution.solve('empty=&flag&x=a%2Bb') == {'empty':[''], 'flag':[''], 'x':['a+b']} ", "assert solution.solve('') == {}"], "timeout_seconds": 3}, "model": "minimax/minimax-m3", "provider": null, "prompt": "只输出 Python 代码，不要解释。实现 solve(query: str) -> dict[str, list[str]]，解析 URL query string。& 分隔参数；没有 = 的参数值为空字符串；+ 表示空格；%XX 需要按 UTF-8 百分号解码；同名 key 保留所有值的列表。可以使用标准库。", "parameters": {"temperature": 0, "max_tokens": 16384, "seed": null}, "prompt_hash": "cfe8be23444d346039a0740ab1568f54ed98bd44e5d63909779673da2207f393", "started_at": "2026-06-16T11:38:33.488279+00:00", "latency_ms": 18095, "output": "```python\nfrom urllib.parse import unquote\n\ndef solve(query: str) -> dict[str, list[str]]:\n result: dict[str, list[str]] = {}\n for pair in query.split('&'):\n if not pair:\n continue\n key, _, value = pair.partition('=')\n key = unquote(key.replace('+', ' '))\n value = unquote(value.replace('+', ' '))\n result.setdefault(key, []).append(value)\n return result\n```", "usage": {"prompt_tokens": 261, "completion_tokens": 1226, "total_tokens": 1487, "cost": 0.00152214, "is_byok": false, "prompt_tokens_details": {"cached_tokens": 114, "cache_write_tokens": 0, "audio_tokens": 0, "video_tokens": 0}, "cost_details": {"upstream_inference_cost": 0.00152214, "upstream_inference_prompt_cost": 5.094e-05, "upstream_inference_completions_cost": 0.0014712}, "completion_tokens_details": {"reasoning_tokens": 1102, "image_tokens": 0, "audio_tokens": 0}}, "response_id": "gen-1781609915-F2HfSLavUilCQVM6IsjC", "finish_reason": "stop", "score": {"passed": true, "score": 1.0, "details": {"returncode": 0, "stdout": "", "stderr": ""}}, "error": null, "rescored_from": "/Users/dracohu/REPO/dracobench/runs/v0.3-challenge100-minimax-minimax-m3-ability16384.jsonl", "scoring_policy": "text_rules_length_diagnostic_only+v03_alias_relaxations_round2"} {"case_id": "challenge-coding-024", "suite": "coding", "case_version": "0.3.0", "tags": ["python", "parser", "stack"], "scorer": "code_python_tests", "expected": {"tests": ["assert solution.solve('

x

') is True", "assert solution.solve('') is False", "assert solution.solve('text') is True"], "timeout_seconds": 3}, "model": "minimax/minimax-m3", "provider": null, "prompt": "只输出 Python 代码，不要解释。实现 solve(html: str) -> bool，校验简化 HTML 标签是否配对。标签形如 、、
，标签名只含小写字母；自闭合标签不入栈；普通文本忽略。", "parameters": {"temperature": 0, "max_tokens": 16384, "seed": null}, "prompt_hash": "088a9867c7f154cf1ee172830e13e8e49c53a8e572707d776f0cdea59f3555cb", "started_at": "2026-06-16T11:38:51.664037+00:00", "latency_ms": 41327, "output": "```python\ndef solve(html: str) -> bool:\n stack = []\n i = 0\n n = len(html)\n while i < n:\n if html[i] == '<':\n j = html.find('>', i)\n if j == -1:\n return False\n content = html[i+1:j]\n if content.startswith('/'):\n name = content[1:]\n if not stack or stack[-1] != name:\n return False\n stack.pop()\n elif content.endswith('/'):\n pass\n else:\n stack.append(content)\n i = j + 1\n else:\n i += 1\n return len(stack) == 0\n```", "usage": {"prompt_tokens": 252, "completion_tokens": 3911, "total_tokens": 4163, "cost": 0.00474144, "is_byok": false, "prompt_tokens_details": {"cached_tokens": 114, "cache_write_tokens": 0, "audio_tokens": 0, "video_tokens": 0}, "cost_details": {"upstream_inference_cost": 0.00474144, "upstream_inference_prompt_cost": 4.824e-05, "upstream_inference_completions_cost": 0.0046932}, "completion_tokens_details": {"reasoning_tokens": 3586, "image_tokens": 0, "audio_tokens": 0}}, "response_id": "gen-1781609933-oEWBTeYLsYH6V3u764mN", "finish_reason": "stop", "score": {"passed": true, "score": 1.0, "details": {"returncode": 0, "stdout": "", "stderr": ""}}, "error": null, "rescored_from": "/Users/dracohu/REPO/dracobench/runs/v0.3-challenge100-minimax-minimax-m3-ability16384.jsonl", "scoring_policy": "text_rules_length_diagnostic_only+v03_alias_relaxations_round2"} {"case_id": "challenge-coding-025", "suite": "coding", "case_version": "0.3.0", "tags": ["python", "bfs", "grid"], "scorer": "code_python_tests", "expected": {"tests": ["assert solution.solve(['S..','##.','..E']) == 4", "assert solution.solve(['S#','##','E.']) == -1", "assert solution.solve(['SE']) == 1"], "timeout_seconds": 3}, "model": "minimax/minimax-m3", "provider": null, "prompt": "只输出 Python 代码，不要解释。实现 solve(grid: list[str]) -> int。grid 中 S 是起点，E 是终点，# 是墙，. 可走。每步上下左右移动一格，返回从 S 到 E 的最短步数；不可达返回 -1。", "parameters": {"temperature": 0, "max_tokens": 16384, "seed": null}, "prompt_hash": "8c6dc47fe32c2eef1e05bed2e6bd50520bb606e6965fe6c18e354a50a1e65085", "started_at": "2026-06-16T11:39:33.038018+00:00", "latency_ms": 14892, "output": "```python\nfrom collections import deque\n\ndef solve(grid: list[str]) -> int:\n if not grid:\n return -1\n rows, cols = len(grid), len(grid[0])\n start = end = None\n for i in range(rows):\n row = grid[i]\n for j, ch in enumerate(row):\n if ch == 'S':\n start = (i, j)\n elif ch == 'E':\n end = (i, j)\n if start is None or end is None:\n return -1\n if start == end:\n return 0\n visited = [[False] * cols for _ in range(rows)]\n q = deque([(start[0], start[1], 0)])\n visited[start[0]][start[1]] = True\n directions = [(-1, 0), (1, 0), (0, -1), (0, 1)]\n while q:\n x, y, d = q.popleft()\n for dx, dy in directions:\n nx, ny = x + dx, y + dy\n if 0 <= nx < rows and 0 <= ny < cols and not visited[nx][ny] and grid[nx][ny] != '#':\n if (nx, ny) == end:\n return d + 1\n visited[nx][ny] = True\n q.append((nx, ny, d + 1))\n return -1\n```", "usage": {"prompt_tokens": 251, "completion_tokens": 1273, "total_tokens": 1524, "cost": 0.00157554, "is_byok": false, "prompt_tokens_details": {"cached_tokens": 114, "cache_write_tokens": 0, "audio_tokens": 0, "video_tokens": 0}, "cost_details": {"upstream_inference_cost": 0.00157554, "upstream_inference_prompt_cost": 4.794e-05, "upstream_inference_completions_cost": 0.0015276}, "completion_tokens_details": {"reasoning_tokens": 987, "image_tokens": 0, "audio_tokens": 0}}, "response_id": "gen-1781609975-cAwM3rASbF65Wz1LPC3m", "finish_reason": "stop", "score": {"passed": true, "score": 1.0, "details": {"returncode": 0, "stdout": "", "stderr": ""}}, "error": null, "rescored_from": "/Users/dracohu/REPO/dracobench/runs/v0.3-challenge100-minimax-minimax-m3-ability16384.jsonl", "scoring_policy": "text_rules_length_diagnostic_only+v03_alias_relaxations_round2"} {"case_id": "challenge-coding-026", "suite": "coding", "case_version": "0.3.0", "tags": ["python", "sliding-window", "median"], "scorer": "code_python_tests", "expected": {"tests": ["assert solution.solve([1,3,-1,-3,5,3,6,7], 3) == [1, -1, -1, 3, 5, 6]", "assert solution.solve([1,2,3,4], 2) == [1.5, 2.5, 3.5]"], "timeout_seconds": 3}, "model": "minimax/minimax-m3", "provider": null, "prompt": "只输出 Python 代码，不要解释。实现 solve(nums: list[int], k: int) -> list[float]，返回每个长度为 k 的滑动窗口中位数。k 为奇数时返回中间值；k 为偶数时返回两个中间值平均数。", "parameters": {"temperature": 0, "max_tokens": 16384, "seed": null}, "prompt_hash": "53547e8e9f25c840b8a6248c7be329b1779ddc5a47484208f5c434403bd8228c", "started_at": "2026-06-16T11:39:47.963954+00:00", "latency_ms": 209589, "output": "", "usage": {"prompt_tokens": 247, "completion_tokens": 16384, "total_tokens": 16631, "cost": 0.027590556, "is_byok": false, "prompt_tokens_details": {"cached_tokens": 114, "cache_write_tokens": 0, "audio_tokens": 0, "video_tokens": 0}, "cost_details": {"upstream_inference_cost": 0.027590556, "upstream_inference_prompt_cost": 6.5436e-05, "upstream_inference_completions_cost": 0.02752512}, "completion_tokens_details": {"reasoning_tokens": 12333, "image_tokens": 0, "audio_tokens": 0}}, "response_id": "gen-1781609990-EvqO70v6NQOpYYOhSQhK", "finish_reason": "length", "score": {"passed": false, "score": 0.0, "details": {"returncode": 1, "stdout": "", "stderr": "Traceback (most recent call last):\n File \"/var/folders/x_/4cht1kfj1_j31gdv5w01sgmw0000gn/T/dracobench-code-39lnz5hx/test_solution.py\", line 3, in \n assert solution.solve([1,3,-1,-3,5,3,6,7], 3) == [1, -1, -1, 3, 5, 6]\n ^^^^^^^^^^^^^^\nAttributeError: module 'solution' has no attribute 'solve'\n"}}, "error": null, "rescored_from": "/Users/dracohu/REPO/dracobench/runs/v0.3-challenge100-minimax-minimax-m3-ability16384.jsonl", "scoring_policy": "text_rules_length_diagnostic_only+v03_alias_relaxations_round2"} {"case_id": "challenge-coding-027", "suite": "coding", "case_version": "0.3.0", "tags": ["python", "sorting", "semver"], "scorer": "code_python_tests", "expected": {"tests": ["assert solution.solve(['1.0.0','1.0.0-alpha','1.0.1','1.0.0-beta','0.9.9']) == ['0.9.9','1.0.0-alpha','1.0.0-beta','1.0.0','1.0.1']", "assert solution.solve(['1.0.10','1.0.2']) == ['1.0.2','1.0.10']"], "timeout_seconds": 3}, "model": "minimax/minimax-m3", "provider": null, "prompt": "只输出 Python 代码，不要解释。实现 solve(versions: list[str]) -> list[str]，按简化语义版本排序。版本为 major.minor.patch 或 major.minor.patch-label；major/minor/patch 按数字比较；同一数字版本下，带 label 的预发布版本低于正式版本；label 按字典序比较。", "parameters": {"temperature": 0, "max_tokens": 16384, "seed": null}, "prompt_hash": "f9b7324425840beb574b30433a6e004d34a834ef548c9cfa579b9b9b6950377d", "started_at": "2026-06-16T11:43:17.577558+00:00", "latency_ms": 26900, "output": "```python\ndef solve(versions: list[str]) -> list[str]:\n def key(v):\n if '-' in v:\n num_part, label = v.split('-', 1)\n has_label = 0\n else:\n num_part = v\n label = ''\n has_label = 1\n major, minor, patch = map(int, num_part.split('.'))\n return (major, minor, patch, has_label, label)\n \n return sorted(versions, key=key)\n```", "usage": {"prompt_tokens": 261, "completion_tokens": 2249, "total_tokens": 2510, "cost": 0.00274974, "is_byok": false, "prompt_tokens_details": {"cached_tokens": 114, "cache_write_tokens": 0, "audio_tokens": 0, "video_tokens": 0}, "cost_details": {"upstream_inference_cost": 0.00274974, "upstream_inference_prompt_cost": 5.094e-05, "upstream_inference_completions_cost": 0.0026988}, "completion_tokens_details": {"reasoning_tokens": 1537, "image_tokens": 0, "audio_tokens": 0}}, "response_id": "gen-1781610200-nWLZAusiQjeTirR68ndp", "finish_reason": "stop", "score": {"passed": true, "score": 1.0, "details": {"returncode": 0, "stdout": "", "stderr": ""}}, "error": null, "rescored_from": "/Users/dracohu/REPO/dracobench/runs/v0.3-challenge100-minimax-minimax-m3-ability16384.jsonl", "scoring_policy": "text_rules_length_diagnostic_only+v03_alias_relaxations_round2"} {"case_id": "challenge-coding-028", "suite": "coding", "case_version": "0.3.0", "tags": ["python", "graph", "reverse-deps"], "scorer": "code_python_tests", "expected": {"tests": ["deps={'app':['lib','ui'],'ui':['core'],'lib':['core'],'core':[],'docs':[]}\nassert solution.solve(deps, ['core']) == ['app','core','lib','ui']", "assert solution.solve({'a':['x']}, ['x']) == ['a','x']", "assert solution.solve({'a':['b'], 'b':[]}, ['a']) == ['a']"], "timeout_seconds": 3}, "model": "minimax/minimax-m3", "provider": null, "prompt": "只输出 Python 代码，不要解释。实现 solve(deps: dict[str, list[str]], changed: list[str]) -> list[str]。deps 表示 package -> 直接依赖。返回所有 changed 包，以及直接或间接依赖 changed 包的包，按字典序排序；只出现在依赖列表中的包也要能作为 changed 包返回。", "parameters": {"temperature": 0, "max_tokens": 16384, "seed": null}, "prompt_hash": "2a0e51751556bc0e35f2a2a2aa4ed86810eacfc274bb9c5f48e8990611a9a473", "started_at": "2026-06-16T11:43:44.503567+00:00", "latency_ms": 14808, "output": "```python\ndef solve(deps: dict[str, list[str]], changed: list[str]) -> list[str]:\n reverse_deps = {}\n for pkg, dep_list in deps.items():\n for d in dep_list:\n reverse_deps.setdefault(d, []).append(pkg)\n \n result = set()\n stack = list(changed)\n while stack:\n pkg = stack.pop()\n if pkg in result:\n continue\n result.add(pkg)\n for dependent in reverse_deps.get(pkg, []):\n if dependent not in result:\n stack.append(dependent)\n \n return sorted(result)\n```", "usage": {"prompt_tokens": 255, "completion_tokens": 760, "total_tokens": 1015, "cost": 0.001977, "is_byok": false, "prompt_tokens_details": {"cached_tokens": 0, "cache_write_tokens": 0, "audio_tokens": 0, "video_tokens": 0}, "cost_details": {"upstream_inference_cost": 0.001977, "upstream_inference_prompt_cost": 0.000153, "upstream_inference_completions_cost": 0.001824}, "completion_tokens_details": {"reasoning_tokens": 713, "image_tokens": 0, "audio_tokens": 0}}, "response_id": "gen-1781610226-JDuquTlJmJGSt7gZyuIZ", "finish_reason": "stop", "score": {"passed": true, "score": 1.0, "details": {"returncode": 0, "stdout": "", "stderr": ""}}, "error": null, "rescored_from": "/Users/dracohu/REPO/dracobench/runs/v0.3-challenge100-minimax-minimax-m3-ability16384.jsonl", "scoring_policy": "text_rules_length_diagnostic_only+v03_alias_relaxations_round2"} {"case_id": "challenge-rag-008", "suite": "rag_long_context", "case_version": "0.3.0", "tags": ["rag", "conflict", "workflow"], "scorer": "text_rules", "expected": {"required": ["50", "审题"], "required_any": [["不跑", "不要立刻", "先不跑", "不应立刻", "不应该立刻", "不应立即", "不应该立即", "不应立刻跑", "不应该立刻跑", "不应立即跑", "不应该立即跑"]], "forbidden": [], "max_chars": 160}, "model": "minimax/minimax-m3", "provider": null, "prompt": "只根据资料回答。\n\n资料 A：早期草案说 v0.3 只新增 20 道题。\n资料 B：2026-06-16 的确认方案是 v0.3-challenge100，在现有 50 题基础上新增 50 道题。\n资料 C：新增题应先生成审题 HTML，确认后再跑模型，不要立即消耗 API。\n\n问题：下一步应该新增多少题？是否应该立刻跑模型？", "parameters": {"temperature": 0, "max_tokens": 16384, "seed": null}, "prompt_hash": "c99da78812f6f4ab7745f793b9e3dc087033b0cce0591db49bf8642829324a81", "started_at": "2026-06-16T11:43:59.331828+00:00", "latency_ms": 9453, "output": "**下一步应新增 50 道题**（以 2026-06-16 确认方案 v0.3-challenge100 为准，资料 A 的早期草案 20 题已被资料 B 覆盖）。\n\n**不应立刻跑模型**。应先按资料 C 的流程，生成审题 HTML，经确认后再调用模型跑题，避免直接消耗 API。", "usage": {"prompt_tokens": 282, "completion_tokens": 251, "total_tokens": 533, "cost": 0.00035844, "is_byok": false, "prompt_tokens_details": {"cached_tokens": 114, "cache_write_tokens": 0, "audio_tokens": 0, "video_tokens": 0}, "cost_details": {"upstream_inference_cost": 0.00035844, "upstream_inference_prompt_cost": 5.724e-05, "upstream_inference_completions_cost": 0.0003012}, "completion_tokens_details": {"reasoning_tokens": 207, "image_tokens": 0, "audio_tokens": 0}}, "response_id": "gen-1781610241-Hdo1fTFshC1GEFa4WOXX", "finish_reason": "stop", "score": {"passed": true, "score": 1.0, "details": {"missing": [], "missing_any": [], "present_forbidden": [], "char_count": 141, "length_ok": true, "length_scored": false}}, "error": null, "rescored_from": "/Users/dracohu/REPO/dracobench/runs/v0.3-challenge100-minimax-minimax-m3-ability16384.jsonl", "scoring_policy": "text_rules_length_diagnostic_only+v03_alias_relaxations_round2"} {"case_id": "challenge-rag-009", "suite": "rag_long_context", "case_version": "0.3.0", "tags": ["rag", "abstain"], "scorer": "text_rules", "expected": {"required": ["不能", "用户满意度"], "required_any": [["没有包含", "没有提供", "资料没有", "未包含"]], "forbidden": ["可以判断"], "max_chars": 120}, "model": "minimax/minimax-m3", "provider": null, "prompt": "只根据资料回答，不要自行补充。\n\n资料：报告展示 pass_rate、avg_latency_ms、total_cost、reasoning_tokens、失败样例和逐题明细。资料没有包含用户满意度调查，也没有包含模型训练数据来源。\n\n问题：能否根据这份报告判断用户满意度？", "parameters": {"temperature": 0, "max_tokens": 16384, "seed": null}, "prompt_hash": "c072287ee5dbe1d477ab21e37485fb72f07cd11a57b57f199f071287443f404e", "started_at": "2026-06-16T11:44:08.789625+00:00", "latency_ms": 4291, "output": "不能。该报告未包含用户满意度调查数据，无法据此判断用户满意度。", "usage": {"prompt_tokens": 252, "completion_tokens": 114, "total_tokens": 366, "cost": 0.00018504, "is_byok": false, "prompt_tokens_details": {"cached_tokens": 114, "cache_write_tokens": 0, "audio_tokens": 0, "video_tokens": 0}, "cost_details": {"upstream_inference_cost": 0.00018504, "upstream_inference_prompt_cost": 4.824e-05, "upstream_inference_completions_cost": 0.0001368}, "completion_tokens_details": {"reasoning_tokens": 133, "image_tokens": 0, "audio_tokens": 0}}, "response_id": "gen-1781610251-SPWOsCADkGQhpvyBgoCB", "finish_reason": "stop", "score": {"passed": true, "score": 1.0, "details": {"missing": [], "missing_any": [], "present_forbidden": [], "char_count": 31, "length_ok": true, "length_scored": false}}, "error": null, "rescored_from": "/Users/dracohu/REPO/dracobench/runs/v0.3-challenge100-minimax-minimax-m3-ability16384.jsonl", "scoring_policy": "text_rules_length_diagnostic_only+v03_alias_relaxations_round2"} {"case_id": "challenge-rag-010", "suite": "rag_long_context", "case_version": "0.3.0", "tags": ["rag", "tradeoff"], "scorer": "text_rules", "expected": {"required": ["B", "延迟"], "required_any": [["没有超时", "无超时"]], "forbidden": ["选择 A", "选择 C"], "max_chars": 160}, "model": "minimax/minimax-m3", "provider": null, "prompt": "只根据资料回答。\n\n资料：模型 A 通过 48/50，平均延迟 20 秒，成本 0.20 美元，没有超时失败。模型 B 通过 44/50，平均延迟 4 秒，成本 0.05 美元，没有超时失败。模型 C 通过 46/50，平均延迟 9 秒，成本 0.04 美元，但有 3 次 API 超时。\n\n问题：如果要选低延迟且稳定的生产摘要模型，资料更支持选择哪个？", "parameters": {"temperature": 0, "max_tokens": 16384, "seed": null}, "prompt_hash": "a17bb8c56d524481eedc45557a1394b5b65b034af6847928cca16e2c8c2428c9", "started_at": "2026-06-16T11:44:13.085347+00:00", "latency_ms": 16898, "output": "# 分析：低延迟且稳定的生产摘要模型选择\n\n## 逐步对比\n\n| 指标 | 模型 A | 模型 B | 模型 C |\n|------|--------|--------|--------|\n| 通过率 | **48/50** | 44/50 | 46/50 |\n| 平均延迟 | 20 秒 | **4 秒** | 9 秒 |\n| 成本 | $0.20 | $0.05 | $0.04 |\n| 超时失败 | 无 | 无 | **有 3 次** |\n\n## 结论：**资料更支持选择模型 B**\n\n理由：\n\n1. **延迟方面**：B 的 4 秒远优于 A 的 20 秒（快 5 倍），也优于 C 的 9 秒。\n2. **稳定性方面**：C 因有 3 次 API 超时，直接不符合\"稳定\"要求被排除。\n3. **A vs B 的取舍**：A 通过率仅比 B 高 4 个百分点（48 vs 44），但延迟代价巨大（20s vs 4s）；且 B 通过率 88% 仍处于可用水平，同时无超时失败，**综合更契合\"低延迟且稳定\"的双重诉求**。", "usage": {"prompt_tokens": 293, "completion_tokens": 1123, "total_tokens": 1416, "cost": 0.00140814, "is_byok": false, "prompt_tokens_details": {"cached_tokens": 114, "cache_write_tokens": 0, "audio_tokens": 0, "video_tokens": 0}, "cost_details": {"upstream_inference_cost": 0.00140814, "upstream_inference_prompt_cost": 6.054e-05, "upstream_inference_completions_cost": 0.0013476}, "completion_tokens_details": {"reasoning_tokens": 903, "image_tokens": 0, "audio_tokens": 0}}, "response_id": "gen-1781610255-DLNxO7bO0NJgEjQzXKmc", "finish_reason": "stop", "score": {"passed": true, "score": 1.0, "details": {"missing": [], "missing_any": [], "present_forbidden": [], "char_count": 466, "length_ok": false, "length_scored": false}}, "error": null, "rescored_from": "/Users/dracohu/REPO/dracobench/runs/v0.3-challenge100-minimax-minimax-m3-ability16384.jsonl", "scoring_policy": "text_rules_length_diagnostic_only+v03_alias_relaxations_round2"} {"case_id": "challenge-rag-011", "suite": "rag_long_context", "case_version": "0.3.0", "tags": ["rag", "scoring-policy"], "scorer": "text_rules", "expected": {"required": ["诊断"], "required_any": [["不扣分", "不会", "不会因长度", "不会因此被扣分", "不会直接导致扣分", "不会导致扣分", "不会导致失败或扣分"], ["length_scored", "长度"]], "forbidden": ["直接失败", "必须重跑"], "max_chars": 140}, "model": "minimax/minimax-m3", "provider": null, "prompt": "只根据资料回答。\n\n资料：DracoBench 当前 scoring policy 中，text_rules 的 max_chars/min_chars 只作为诊断信息记录，length_scored 为 false。缺少 required、required_any 或出现 forbidden 才会导致失败。\n\n问题：如果答案超过 max_chars，但 required 都满足且没有 forbidden，会因为长度直接扣分吗？", "parameters": {"temperature": 0, "max_tokens": 16384, "seed": null}, "prompt_hash": "1ff7f897fc3d3121f25358f9a8df2a2df2970bc5762ff1218dd63c3f42d7d598", "started_at": "2026-06-16T11:44:29.990758+00:00", "latency_ms": 14635, "output": "不会。\n\n根据资料，max_chars/min_chars 仅作为诊断信息记录，length_scored 为 false，因此超出 max_chars 不会直接导致扣分或失败。只有以下情况才会失败：\n\n- 缺少 required\n- 缺少 required_any\n- 出现 forbidden\n\n只要这三项都满足，即使长度超过 max_chars，也不会因此被扣分。", "usage": {"prompt_tokens": 262, "completion_tokens": 204, "total_tokens": 466, "cost": 0.00029604, "is_byok": false, "prompt_tokens_details": {"cached_tokens": 114, "cache_write_tokens": 0, "audio_tokens": 0, "video_tokens": 0}, "cost_details": {"upstream_inference_cost": 0.00029604, "upstream_inference_prompt_cost": 5.124e-05, "upstream_inference_completions_cost": 0.0002448}, "completion_tokens_details": {"reasoning_tokens": 174, "image_tokens": 0, "audio_tokens": 0}}, "response_id": "gen-1781610272-xwfh72VG8TvnIAm969cu", "finish_reason": "stop", "score": {"passed": true, "score": 1.0, "details": {"missing": [], "missing_any": [], "present_forbidden": [], "char_count": 185, "length_ok": false, "length_scored": false}}, "error": null, "rescored_from": "/Users/dracohu/REPO/dracobench/runs/v0.3-challenge100-minimax-minimax-m3-ability16384.jsonl", "scoring_policy": "text_rules_length_diagnostic_only+v03_alias_relaxations_round2"} {"case_id": "challenge-rag-012", "suite": "rag_long_context", "case_version": "0.3.0", "tags": ["rag", "rescoring"], "scorer": "text_rules", "expected": {"required": ["46/50"], "forbidden": ["45/50 更合适", "原始结果为准"], "max_chars": 140}, "model": "minimax/minimax-m3", "provider": null, "prompt": "只根据资料回答。\n\n资料 A：某模型原始结果为 45/50。\n资料 B：修正一个同义表达评分规则后，rescored 结果为 46/50。\n资料 C：总览 index 使用 rescored JSONL 作为展示结果。\n\n问题：写文章引用总览结果时，应写 45/50 还是 46/50？", "parameters": {"temperature": 0, "max_tokens": 16384, "seed": null}, "prompt_hash": "274c0483d9fd5cae178e795e16add941178c14f5ae34a82f631655c000ff929a", "started_at": "2026-06-16T11:44:44.639683+00:00", "latency_ms": 3965, "output": "应写 **46/50**。\n\n**理由：** 资料 C 明确规定总览 index 使用的是 rescored 后的 JSONL 作为展示结果，而 rescored 结果为 46/50（资料 B）。因此，引用总览结果时需与 index 实际展示的数据保持一致，即 46/50；45/50 只是修正前的原始结果（资料 A），不是当前总览的展示数据。", "usage": {"prompt_tokens": 270, "completion_tokens": 229, "total_tokens": 499, "cost": 0.00032844, "is_byok": false, "prompt_tokens_details": {"cached_tokens": 114, "cache_write_tokens": 0, "audio_tokens": 0, "video_tokens": 0}, "cost_details": {"upstream_inference_cost": 0.00032844, "upstream_inference_prompt_cost": 5.364e-05, "upstream_inference_completions_cost": 0.0002748}, "completion_tokens_details": {"reasoning_tokens": 150, "image_tokens": 0, "audio_tokens": 0}}, "response_id": "gen-1781610286-80C5SQ33P2WAlMspXPKo", "finish_reason": "stop", "score": {"passed": true, "score": 1.0, "details": {"missing": [], "missing_any": [], "present_forbidden": [], "char_count": 172, "length_ok": false, "length_scored": false}}, "error": null, "rescored_from": "/Users/dracohu/REPO/dracobench/runs/v0.3-challenge100-minimax-minimax-m3-ability16384.jsonl", "scoring_policy": "text_rules_length_diagnostic_only+v03_alias_relaxations_round2"} {"case_id": "challenge-rag-013", "suite": "rag_long_context", "case_version": "0.3.0", "tags": ["rag", "reproducibility"], "scorer": "text_rules", "expected": {"required": ["model", "prompt_hash", "parameters", "scorer"], "forbidden": ["chain of thought", "训练数据"], "max_chars": 200}, "model": "minimax/minimax-m3", "provider": null, "prompt": "只根据资料回答。\n\n资料：为了复现一次 benchmark，记录中包含 model、case_id、prompt_hash、parameters、scorer details、output、latency_ms 和 usage。记录不包含 chain of thought，也不包含训练数据。\n\n问题：复现评测主要应该依赖哪些记录字段？", "parameters": {"temperature": 0, "max_tokens": 16384, "seed": null}, "prompt_hash": "7455c8e21abbb53f7cb11c9db29ed66ad1dfcc7e60138f6dfccb7cbcacc958c3", "started_at": "2026-06-16T11:44:48.615792+00:00", "latency_ms": 31651, "output": "根据资料，复现评测主要应依赖以下字段：\n\n**核心输入字段（复现必需）：**\n- **model**：确定使用的模型\n- **case_id**：定位具体测试用例\n- **prompt_hash**：标识所用的 prompt（但需注意：仅有 hash 无法还原 prompt 原文，严格复现仍需 prompt 本身）\n- **parameters**：保证推理参数（如 temperature、top_p 等）一致\n- **scorer details**：保证评分逻辑一致\n\n**非复现字段（仅作结果记录）：**\n- **output**：是复现要得到的结果，不是输入\n- **latency_ms**、**usage**：性能/资源指标，不影响复现结果\n\n**关键提示**：资料中只记录了 `prompt_hash` 而非 prompt 原文，这构成复现的缺口——hash 只能校验一致性，不能用于重建输入。若要真正可复现，应额外保存 prompt 原文（或可由 hash 反查的 prompt 存储）。", "usage": {"prompt_tokens": 254, "completion_tokens": 804, "total_tokens": 1058, "cost": 0.00100212, "is_byok": false, "prompt_tokens_details": {"cached_tokens": 162, "cache_write_tokens": 0, "audio_tokens": 0, "video_tokens": 0}, "cost_details": {"upstream_inference_cost": 0.00100212, "upstream_inference_prompt_cost": 3.732e-05, "upstream_inference_completions_cost": 0.0009648}, "completion_tokens_details": {"reasoning_tokens": 752, "image_tokens": 0, "audio_tokens": 0}}, "response_id": "gen-1781610290-1HFaJhc2Cdk6hwsg8J2D", "finish_reason": "stop", "score": {"passed": true, "score": 1.0, "details": {"missing": [], "missing_any": [], "present_forbidden": [], "char_count": 454, "length_ok": false, "length_scored": false}}, "error": null, "rescored_from": "/Users/dracohu/REPO/dracobench/runs/v0.3-challenge100-minimax-minimax-m3-ability16384.jsonl", "scoring_policy": "text_rules_length_diagnostic_only+v03_alias_relaxations_round2"} {"case_id": "challenge-rag-014", "suite": "rag_long_context", "case_version": "0.3.0", "tags": ["rag", "mode"], "scorer": "text_rules", "expected": {"required": ["16384"], "forbidden": ["4096"], "max_chars": 100}, "model": "minimax/minimax-m3", "provider": null, "prompt": "只根据资料回答。\n\n资料 A：早期 ability mode 使用 4096 max_tokens。\n资料 B：后来为了公平，ability mode 统一放宽到 16384 max_tokens。\n资料 C：当前总览页只展示 v0.2-challenge50 且 ability16384 的结果。\n\n问题：当前总览页对应的 token limit 是多少？", "parameters": {"temperature": 0, "max_tokens": 16384, "seed": null}, "prompt_hash": "1d5b45892f4d0509e7805976e9bb60b3893e68657b40fcfa934db89a27eccc7b", "started_at": "2026-06-16T11:45:20.278860+00:00", "latency_ms": 14060, "output": "当前总览页对应的 token limit 是 **16384**。\n\n根据资料 B，为了公平起见，ability mode 已统一放宽到 16384 max_tokens；资料 C 也确认当前总览页只展示 ability16384 的结果。", "usage": {"prompt_tokens": 266, "completion_tokens": 141, "total_tokens": 407, "cost": 0.00022164, "is_byok": false, "prompt_tokens_details": {"cached_tokens": 114, "cache_write_tokens": 0, "audio_tokens": 0, "video_tokens": 0}, "cost_details": {"upstream_inference_cost": 0.00022164, "upstream_inference_prompt_cost": 5.244e-05, "upstream_inference_completions_cost": 0.0001692}, "completion_tokens_details": {"reasoning_tokens": 109, "image_tokens": 0, "audio_tokens": 0}}, "response_id": "gen-1781610322-KknyrniKgYgVKEkQNtby", "finish_reason": "stop", "score": {"passed": true, "score": 1.0, "details": {"missing": [], "missing_any": [], "present_forbidden": [], "char_count": 120, "length_ok": false, "length_scored": false}}, "error": null, "rescored_from": "/Users/dracohu/REPO/dracobench/runs/v0.3-challenge100-minimax-minimax-m3-ability16384.jsonl", "scoring_policy": "text_rules_length_diagnostic_only+v03_alias_relaxations_round2"} {"case_id": "challenge-rag-015", "suite": "rag_long_context", "case_version": "0.3.0", "tags": ["rag", "failure-analysis", "scoring-policy"], "scorer": "text_rules", "expected": {"required": ["人工复查", "coding-004"], "required_any": [["rag-006", "规则", "评分规则", "同义表达"], ["不属于", "不是", "不需要"]], "max_chars": 180}, "model": "minimax/minimax-m3", "provider": null, "prompt": "只根据资料回答。\n\n资料：coding-004 的失败是代码运行时 ValueError，根因是把字符串 key 强转 int。rag-006 的失败曾经是规则没有把 int 类型视作“整数”的同义表达，后来通过人工复查修正规则。\n\n问题：哪类失败更需要人工复查评分规则？回答时也说明 coding-004 是否属于这种情况。", "parameters": {"temperature": 0, "max_tokens": 16384, "seed": null}, "prompt_hash": "d4725ef21042620eab8231d5671ac8f778c2b71f85afb72d592c3da2278cc9c4", "started_at": "2026-06-16T11:45:34.354923+00:00", "latency_ms": 8453, "output": "根据资料，**rag-006 这类失败**更需要人工复查评分规则。\n\n**理由：**\nrag-006 的失败根因是评分规则本身存在覆盖不足——规则没有将编程语言中的 `int` 类型识别为自然语言中\"整数\"的同义表达，这属于**规则定义不完善**的问题，必须依靠人工复查评分规则、补充同义映射才能修正。\n\n**coding-004 不属于这种情况。**\ncoding-004 的失败是代码运行时的 `ValueError`，根因是代码逻辑错误（把字符串 key 强转 int），属于**代码实现缺陷**，而非评分规则的问题。因此它不需要通过人工复查规则来修复，而应通过修正代码逻辑解决。", "usage": {"prompt_tokens": 271, "completion_tokens": 362, "total_tokens": 633, "cost": 0.00048834, "is_byok": false, "prompt_tokens_details": {"cached_tokens": 114, "cache_write_tokens": 0, "audio_tokens": 0, "video_tokens": 0}, "cost_details": {"upstream_inference_cost": 0.00048834, "upstream_inference_prompt_cost": 5.394e-05, "upstream_inference_completions_cost": 0.0004344}, "completion_tokens_details": {"reasoning_tokens": 266, "image_tokens": 0, "audio_tokens": 0}}, "response_id": "gen-1781610336-cn8wQEhzf2O7L1jKXZuu", "finish_reason": "stop", "score": {"passed": true, "score": 1.0, "details": {"missing": [], "missing_any": [], "present_forbidden": [], "char_count": 293, "length_ok": false, "length_scored": false}}, "error": null, "rescored_from": "/Users/dracohu/REPO/dracobench/runs/v0.3-challenge100-minimax-minimax-m3-ability16384.jsonl", "scoring_policy": "text_rules_length_diagnostic_only+v03_alias_relaxations_round2"} {"case_id": "challenge-rag-016", "suite": "rag_long_context", "case_version": "0.3.0", "tags": ["rag", "abstain", "cost"], "scorer": "text_rules", "expected": {"required": ["不能", "利润率"], "required_any": [["不包含", "没有"]], "forbidden": ["可以判断"], "max_chars": 120}, "model": "minimax/minimax-m3", "provider": null, "prompt": "只根据资料回答。\n\n资料：usage 字段包含 prompt_tokens、completion_tokens、reasoning_tokens 和 cost。报告没有供应商真实利润率，也没有供应商内部成本。\n\n问题：能否从 usage 字段判断供应商利润率？", "parameters": {"temperature": 0, "max_tokens": 16384, "seed": null}, "prompt_hash": "622c02bde50c8b232afe7bb574da76e2ed1e35d49a55a96f63b935dbbbb16175", "started_at": "2026-06-16T11:45:42.824251+00:00", "latency_ms": 8843, "output": "**不能。**\n\n根据资料，usage 字段仅包含 prompt_tokens、completion_tokens、reasoning_tokens 和 cost（用户支付的费用）。这些信息只能反映**对外计费情况**，而供应商利润率需要知道其**内部成本**（如算力、带宽、研发等成本），资料明确指出\"没有供应商真实利润率，也没有供应商内部成本\"。\n\n因此，仅凭 usage 字段无法判断供应商利润率。", "usage": {"prompt_tokens": 238, "completion_tokens": 206, "total_tokens": 444, "cost": 0.00027972, "is_byok": false, "prompt_tokens_details": {"cached_tokens": 162, "cache_write_tokens": 0, "audio_tokens": 0, "video_tokens": 0}, "cost_details": {"upstream_inference_cost": 0.00027972, "upstream_inference_prompt_cost": 3.252e-05, "upstream_inference_completions_cost": 0.0002472}, "completion_tokens_details": {"reasoning_tokens": 161, "image_tokens": 0, "audio_tokens": 0}}, "response_id": "gen-1781610345-xbedbVQie1ZTIIN1k2y1", "finish_reason": "stop", "score": {"passed": true, "score": 1.0, "details": {"missing": [], "missing_any": [], "present_forbidden": [], "char_count": 202, "length_ok": false, "length_scored": false}}, "error": null, "rescored_from": "/Users/dracohu/REPO/dracobench/runs/v0.3-challenge100-minimax-minimax-m3-ability16384.jsonl", "scoring_policy": "text_rules_length_diagnostic_only+v03_alias_relaxations_round2"} {"case_id": "challenge-rag-017", "suite": "rag_long_context", "case_version": "0.3.0", "tags": ["rag", "index-scope"], "scorer": "text_rules", "expected": {"required": ["v0.2-challenge50", "ability16384"], "required_any": [["不应该", "不展示", "未展示", "不显示", "均不显示", "不应展示"], ["旧版本", "之前的版本"]], "max_chars": 160}, "model": "minimax/minimax-m3", "provider": null, "prompt": "只根据资料回答。\n\n资料：当前 index 页只显示最后这版 50 道题的 v0.2-challenge50 结果，并且 token limit 都统一为 ability16384。之前的版本和 ability4096 结果不显示在 index 页。\n\n问题：index 页是否应该展示旧版本结果？当前展示范围是什么？", "parameters": {"temperature": 0, "max_tokens": 16384, "seed": null}, "prompt_hash": "84234aeba9875f705a8daae80d0f0a287d1cef74de57877dfe6fbd41e6d1e835", "started_at": "2026-06-16T11:45:51.681988+00:00", "latency_ms": 8655, "output": "**根据资料：**\n\n1. **当前展示范围**：index 页**只**展示 v0.-challenge50 这版 50 道题的结果，token limit 统一为 ability16384。\n\n2. **不展示的内容**：旧版本结果以及 ability4096 的结果**均不**显示在 index 页。\n\n3. **关于\"是否应该展示旧版本\"**：资料中**未给出明确判断或建议**，仅描述了当前的展示规则（只显示最新版 ability16384 的结果）。因此，仅依据该资料无法得出\"应该\"或\"不应该\"展示旧版本的结论，只能确认**当前实际未展示**旧版本及 ability4096 结果。", "usage": {"prompt_tokens": 260, "completion_tokens": 401, "total_tokens": 661, "cost": 0.00053184, "is_byok": false, "prompt_tokens_details": {"cached_tokens": 114, "cache_write_tokens": 0, "audio_tokens": 0, "video_tokens": 0}, "cost_details": {"upstream_inference_cost": 0.00053184, "upstream_inference_prompt_cost": 5.064e-05, "upstream_inference_completions_cost": 0.0004812}, "completion_tokens_details": {"reasoning_tokens": 330, "image_tokens": 0, "audio_tokens": 0}}, "response_id": "gen-1781610353-yXhpThbMVbzvhHqoVpDm", "finish_reason": "stop", "score": {"passed": false, "score": 0.0, "details": {"missing": ["v0.2-challenge50"], "missing_any": [], "present_forbidden": [], "char_count": 299, "length_ok": false, "length_scored": false}}, "error": null, "rescored_from": "/Users/dracohu/REPO/dracobench/runs/v0.3-challenge100-minimax-minimax-m3-ability16384.jsonl", "scoring_policy": "text_rules_length_diagnostic_only+v03_alias_relaxations_round2"} {"case_id": "challenge-rag-018", "suite": "rag_long_context", "case_version": "0.3.0", "tags": ["rag", "failure-analysis", "truncation"], "scorer": "text_rules", "expected": {"required": ["输出"], "required_any": [["截断", "length", "token"]], "forbidden": [], "max_chars": 140}, "model": "minimax/minimax-m3", "provider": null, "prompt": "只根据资料回答。\n\n资料：某 coding 题中，模型响应的 finish_reason 为 length，output 为空，测试报错为 module has no attribute solve。报告认为这不是算法逻辑错误，而是输出被截断后没有形成可执行函数。\n\n问题：这次失败应如何归因？", "parameters": {"temperature": 0, "max_tokens": 16384, "seed": null}, "prompt_hash": "f819b3ec516c1c77e6e0c1bbcece217d77fc5a4aaee6e7787be0b4cd5d97c9ff", "started_at": "2026-06-16T11:46:00.350959+00:00", "latency_ms": 11972, "output": "## 失败归因\n\n**应归因为：输出截断（Output Truncation），而非模型能力或算法错误。**\n\n### 判断依据\n\n| 线索 | 说明 |\n|------|------|\n| `finish_reason: length` | 模型在生成过程中触发了 token 上限，被强制截断 |\n| `output 为空` | 截断发生得过早，连函数定义都没写完 |\n| `module has no attribute solve` | 这是**结果性报错**，不是原因——`solve` 函数根本未生成 |\n\n### 归因要点\n\n- **不是算法错误**：模型还没来得及写代码就停了，无法判断其解题逻辑是否正确。\n- **不是测试框架问题**：测试代码本身正常，是被测对象缺失。\n- **根因是工程/基础设施层面**：最大输出长度不足，模型未能完成完整函数的书写。\n\n### 建议处置\n\n1. 提高该题的最大输出 token 限制后重跑。\n2. 若重跑后仍能完成，说明原失败纯粹由截断导致，应从失败统计中剔除或单独标记为 `infra_truncation`。\n3. 仅在增大 token 后仍出现逻辑错误时，才归因为模型能力问题。", "usage": {"prompt_tokens": 254, "completion_tokens": 534, "total_tokens": 788, "cost": 0.00068964, "is_byok": false, "prompt_tokens_details": {"cached_tokens": 114, "cache_write_tokens": 0, "audio_tokens": 0, "video_tokens": 0}, "cost_details": {"upstream_inference_cost": 0.00068964, "upstream_inference_prompt_cost": 4.884e-05, "upstream_inference_completions_cost": 0.0006408}, "completion_tokens_details": {"reasoning_tokens": 366, "image_tokens": 0, "audio_tokens": 0}}, "response_id": "gen-1781610362-4MQFwaf51gEa25cpdmnC", "finish_reason": "stop", "score": {"passed": true, "score": 1.0, "details": {"missing": [], "missing_any": [], "present_forbidden": [], "char_count": 521, "length_ok": false, "length_scored": false}}, "error": null, "rescored_from": "/Users/dracohu/REPO/dracobench/runs/v0.3-challenge100-minimax-minimax-m3-ability16384.jsonl", "scoring_policy": "text_rules_length_diagnostic_only+v03_alias_relaxations_round2"} {"case_id": "challenge-debugging-015", "suite": "debugging", "case_version": "0.3.0", "tags": ["python", "closure", "debugging"], "scorer": "regex", "expected": {"pattern": "FIX:\\s*`?\\s*funcs\\.append\\(lambda\\s+i=i\\s*:\\s*i\\)\\s*`?", "flags": "is"}, "model": "minimax/minimax-m3", "provider": null, "prompt": "下面代码期望 funcs[0]()、funcs[1]()、funcs[2]() 分别返回 0、1、2，但实际都会返回 2。请指出根因，并给出最小修复。最后单独一行写 FIX: <修复后的 append 行>。\n\n```python\nfuncs = []\nfor i in range(3):\n funcs.append(lambda: i)\n```", "parameters": {"temperature": 0, "max_tokens": 16384, "seed": null}, "prompt_hash": "e90d1ddfafd6f041b7be435f823b34b7ad74ddf85bb4dbb228db8e1183cd8843", "started_at": "2026-06-16T11:46:12.335923+00:00", "latency_ms": 4162, "output": "**根因**：闭包按**晚绑定**（late binding）规则捕获变量 `i`，而不是绑定循环的当前值。当这些 lambda 实际执行时（调用 `funcs[k]()` 时），循环早已结束，`i` 的最终值是 `2`，所以所有函数都返回 `2`。\n\n**最小修复**：在 lambda 的参数列表中加一个默认参数，把 `i` 的当前值\"冻结\"住。\n\n```python\nfuncs = []\nfor i in range(3):\n funcs.append(lambda i=i: i)\n```\n\nFIX: `funcs.append(lambda i=i: i)`", "usage": {"prompt_tokens": 272, "completion_tokens": 145, "total_tokens": 417, "cost": 0.00022824, "is_byok": false, "prompt_tokens_details": {"cached_tokens": 114, "cache_write_tokens": 0, "audio_tokens": 0, "video_tokens": 0}, "cost_details": {"upstream_inference_cost": 0.00022824, "upstream_inference_prompt_cost": 5.424e-05, "upstream_inference_completions_cost": 0.000174}, "completion_tokens_details": {"reasoning_tokens": 22, "image_tokens": 0, "audio_tokens": 0}}, "response_id": "gen-1781610374-WNkNVq6h214sS04kLFZ0", "finish_reason": "stop", "score": {"passed": true, "score": 1.0, "details": {"pattern": "FIX:\\s*`?\\s*funcs\\.append\\(lambda\\s+i=i\\s*:\\s*i\\)\\s*`?"}}, "error": null, "rescored_from": "/Users/dracohu/REPO/dracobench/runs/v0.3-challenge100-minimax-minimax-m3-ability16384.jsonl", "scoring_policy": "text_rules_length_diagnostic_only+v03_alias_relaxations_round2"} {"case_id": "challenge-debugging-016", "suite": "debugging", "case_version": "0.3.0", "tags": ["python", "context-manager", "debugging"], "scorer": "regex", "expected": {"pattern": "FIX:\\s*`?\\s*return(?:\\s+False|\\s+None)?\\s*`?", "flags": "is"}, "model": "minimax/minimax-m3", "provider": null, "prompt": "下面 context manager 期望异常继续向外抛出，但实际异常被吞掉。请指出 bug，并给出最小修复。最后单独一行写 FIX: <修复后的 return 行>。\n\n```python\nclass Span:\n def __enter__(self):\n return self\n def __exit__(self, exc_type, exc, tb):\n return True\n```", "parameters": {"temperature": 0, "max_tokens": 16384, "seed": null}, "prompt_hash": "e7cbf0d9b2df76954e4b3aacf7d21f0a22c40f64ccc2432b115896cdb5a3f123", "started_at": "2026-06-16T11:46:16.512789+00:00", "latency_ms": 13305, "output": "## Bug 分析\n\n`__exit__` 返回 `True`（或非 None、非 falsy 的真值）时，Python 上下文管理器协议会**抑制（吞掉）** `with` 块内抛出的异常，不再向外传播。\n\n```python\ndef __exit__(self, exc_type, exc, tb):\n return True # ← 这会吞掉异常\n```\n\n要让异常按原样向上抛出，`__exit__` 必须返回 `None` / `False`（或直接 `return`，隐式为 `None`），表示\"我不处理这个异常，请继续传播\"。\n\n## 最小修复\n\n```python\nclass Span:\n def __enter__(self):\n return self\n def __exit__(self, exc_type, exc, tb):\n return\n```\n\n这样：\n- 若 `with` 块内无异常：正常离开。\n- 若 `with` 块内抛出异常：异常不会被吞掉，会原样向上抛出，行为符合\"期望异常继续向外抛出\"。\n\n补充说明：很多人会想\"那为什么 `__exit__` 会有返回值？\"——这正是 `contextlib.suppress` 之类的工具所依赖的机制：返回真值 = 显式声明\"该异常我已处理，请吞掉\"。一旦没有这个意图，就必须返回 falsy。\n\nFIX: return", "usage": {"prompt_tokens": 265, "completion_tokens": 772, "total_tokens": 1037, "cost": 0.00097854, "is_byok": false, "prompt_tokens_details": {"cached_tokens": 114, "cache_write_tokens": 0, "audio_tokens": 0, "video_tokens": 0}, "cost_details": {"upstream_inference_cost": 0.00097854, "upstream_inference_prompt_cost": 5.214e-05, "upstream_inference_completions_cost": 0.0009264}, "completion_tokens_details": {"reasoning_tokens": 566, "image_tokens": 0, "audio_tokens": 0}}, "response_id": "gen-1781610378-eHIHRT3v7r3BWwbk6HBt", "finish_reason": "stop", "score": {"passed": true, "score": 1.0, "details": {"pattern": "FIX:\\s*`?\\s*return(?:\\s+False|\\s+None)?\\s*`?"}}, "error": null, "rescored_from": "/Users/dracohu/REPO/dracobench/runs/v0.3-challenge100-minimax-minimax-m3-ability16384.jsonl", "scoring_policy": "text_rules_length_diagnostic_only+v03_alias_relaxations_round2"} {"case_id": "challenge-debugging-017", "suite": "debugging", "case_version": "0.3.0", "tags": ["python", "class-variable", "debugging"], "scorer": "regex", "expected": {"pattern": "FIX:\\s*`?\\s*self\\.items\\s*=\\s*\\[\\]\\s*`?", "flags": "is"}, "model": "minimax/minimax-m3", "provider": null, "prompt": "下面代码期望每个 Bag 实例都有独立列表，但不同实例会共享 items。请指出 bug，并给出最小修复。最后单独一行写 FIX: <新增到 __init__ 中的赋值行>。\n\n```python\nclass Bag:\n items = []\n def add(self, x):\n self.items.append(x)\n```", "parameters": {"temperature": 0, "max_tokens": 16384, "seed": null}, "prompt_hash": "c2b320a4b54c57505deb9a6f14ebb63217e6c351a61dbfeed24131e848fe7553", "started_at": "2026-06-16T11:46:29.832597+00:00", "latency_ms": 4104, "output": "**Bug 分析**：`items = []` 是定义在类体中的可变对象，属于**类属性**，被所有实例共享。因此 `Bag().add(1)` 和 `Bag().add(2)` 后，`Bag().items` 会是 `[1, 2]`，而不是各自的独立列表。\n\n**最小修复**：在 `__init__` 中为每个实例创建独立的 `items` 列表。\n\n```python\nclass Bag:\n def __init__(self):\n self.items = []\n def add(self, x):\n self.items.append(x)\n```\n\nFIX: self.items = []", "usage": {"prompt_tokens": 257, "completion_tokens": 163, "total_tokens": 420, "cost": 0.00024534, "is_byok": false, "prompt_tokens_details": {"cached_tokens": 114, "cache_write_tokens": 0, "audio_tokens": 0, "video_tokens": 0}, "cost_details": {"upstream_inference_cost": 0.00024534, "upstream_inference_prompt_cost": 4.974e-05, "upstream_inference_completions_cost": 0.0001956}, "completion_tokens_details": {"reasoning_tokens": 40, "image_tokens": 0, "audio_tokens": 0}}, "response_id": "gen-1781610392-pgQ2xkY1JpeqjmdsOBzd", "finish_reason": "stop", "score": {"passed": true, "score": 1.0, "details": {"pattern": "FIX:\\s*`?\\s*self\\.items\\s*=\\s*\\[\\]\\s*`?"}}, "error": null, "rescored_from": "/Users/dracohu/REPO/dracobench/runs/v0.3-challenge100-minimax-minimax-m3-ability16384.jsonl", "scoring_policy": "text_rules_length_diagnostic_only+v03_alias_relaxations_round2"} {"case_id": "challenge-debugging-018", "suite": "debugging", "case_version": "0.3.0", "tags": ["python", "default-argument", "debugging"], "scorer": "regex", "expected": {"pattern": "FIX:\\s*`?\\s*def\\s+mark\\(ts=None\\)\\s*:", "flags": "is"}, "model": "minimax/minimax-m3", "provider": null, "prompt": "下面代码期望每次不传 ts 时使用当前时间，但默认时间只会在函数定义时计算一次。请指出 bug，并给出最小修复。最后单独一行写 FIX: <修复后的函数定义行>。\n\n```python\nfrom datetime import datetime\n\ndef mark(ts=datetime.utcnow()):\n return ts.isoformat()\n```", "parameters": {"temperature": 0, "max_tokens": 16384, "seed": null}, "prompt_hash": "96674f1c8091e56110c21b7b057178e05bd5225812543e3101e86fc6c5d2dba7", "started_at": "2026-06-16T11:46:33.948302+00:00", "latency_ms": 39060, "output": "**Bug 分析**\n\nPython 的默认参数只在函数定义时求值一次，之后所有调用都会复用同一个对象。因此 `datetime.utcnow()` 只在模块加载时执行一次，后续所有不传 `ts` 的调用都会得到同一个时间戳，而不是\"当前时间\"。\n\n**最小修复**\n\n```python\nfrom datetime import datetime\n\ndef mark(ts=None):\n if ts is None:\n ts = datetime.utcnow()\n return ts.isoformat()\n```\n\n把可变/动态的默认值换成哨兵 `None`，在函数体内按需计算。\n\nFIX: def mark(ts=None):", "usage": {"prompt_tokens": 260, "completion_tokens": 1001, "total_tokens": 1261, "cost": 0.00123384, "is_byok": false, "prompt_tokens_details": {"cached_tokens": 189, "cache_write_tokens": 0, "audio_tokens": 0, "video_tokens": 0}, "cost_details": {"upstream_inference_cost": 0.00123384, 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normalize(s):\n return s.strip().lower\n```", "parameters": {"temperature": 0, "max_tokens": 16384, "seed": null}, "prompt_hash": "e9d483dafd7852b396cee6b27c6c25c888983460dba83c871f7f9a239dd601ef", "started_at": "2026-06-16T11:47:13.017698+00:00", "latency_ms": 6449, "output": "bug：`.lower` 是方法引用（bound method 对象），没有加括号调用，所以返回的是方法本身而不是调用结果。\n\n最小修复：给 `.lower` 加上括号使其真正执行。\n\n```python\ndef normalize(s):\n return s.strip().lower()\n```\n\nFIX: ` return s.strip().lower()`", "usage": {"prompt_tokens": 242, "completion_tokens": 179, "total_tokens": 421, "cost": 0.00024852, "is_byok": false, "prompt_tokens_details": {"cached_tokens": 162, "cache_write_tokens": 0, "audio_tokens": 0, "video_tokens": 0}, "cost_details": {"upstream_inference_cost": 0.00024852, "upstream_inference_prompt_cost": 3.372e-05, "upstream_inference_completions_cost": 0.0002148}, "completion_tokens_details": {"reasoning_tokens": 134, "image_tokens": 0, "audio_tokens": 0}}, "response_id": 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