{"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": "qwen/qwen3.7-plus", "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": "2026-06-16T16:40:07.797836+00:00", "latency_ms": 19734, "output": "```python\ndef solve(lines: list[str]) -> dict:\n result = {}\n current_section = None\n for line in lines:\n line = line.strip()\n if not line or line.startswith('#'):\n continue\n if line.startswith('[') and line.endswith(']'):\n current_section = line[1:-1].strip()\n if current_section not in result:\n result[current_section] = {}\n elif current_section is not None and '=' in line:\n key, value = line.split('=', 1)\n result[current_section][key.strip()] = value.strip()\n return result\n```", "usage": {"prompt_tokens": 114, "completion_tokens": 1033, "total_tokens": 1147, "cost": 0.00135872, "is_byok": false, "prompt_tokens_details": {"cached_tokens": 0, "cache_write_tokens": 0, "audio_tokens": 0, "video_tokens": 0}, "cost_details": {"upstream_inference_cost": 0.00135872, "upstream_inference_prompt_cost": 3.648e-05, "upstream_inference_completions_cost": 0.00132224}, "completion_tokens_details": {"reasoning_tokens": 888, "image_tokens": 0, "audio_tokens": 0}}, "response_id": "gen-1781628010-YOflLWrdMtrFhpUU4awT", "finish_reason": "stop", "score": {"passed": true, "score": 1.0, "details": {"returncode": 0, "stdout": "", "stderr": ""}}, "error": null} {"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": "qwen/qwen3.7-plus", "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": "2026-06-16T16:40:27.567631+00:00", "latency_ms": 48342, "output": "```python\ndef solve(events: list[str]) -> int:\n balance = 0\n seen = set()\n amounts = {}\n revoked = set()\n for event in events:\n parts = event.split()\n if parts[0] == 'rev':\n tid = parts[1]\n if tid in seen and tid not in revoked:\n revoked.add(tid)\n balance -= amounts[tid]\n else:\n tid = parts[0]\n if tid not in seen:\n seen.add(tid)\n val = int(parts[2]) if parts[1] == 'credit' else -int(parts[2])\n amounts[tid] = val\n balance += val\n return balance\n```", "usage": {"prompt_tokens": 116, "completion_tokens": 2623, "total_tokens": 2739, "cost": 0.00339456, "is_byok": false, "prompt_tokens_details": {"cached_tokens": 0, "cache_write_tokens": 0, "audio_tokens": 0, "video_tokens": 0}, "cost_details": {"upstream_inference_cost": 0.00339456, "upstream_inference_prompt_cost": 3.712e-05, "upstream_inference_completions_cost": 0.00335744}, "completion_tokens_details": {"reasoning_tokens": 2455, "image_tokens": 0, "audio_tokens": 0}}, "response_id": "gen-1781628029-Co2uRqSCKLo2c91mvb2C", "finish_reason": "stop", "score": {"passed": true, "score": 1.0, "details": {"returncode": 0, "stdout": "", "stderr": ""}}, "error": null} {"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": "qwen/qwen3.7-plus", "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": "2026-06-16T16:41:15.950598+00:00", "latency_ms": 45477, "output": "```python\ndef solve(deps: dict[str, list[str]]) -> list[list[str]]:\n nodes = set(deps.keys())\n for prereqs in deps.values():\n nodes.update(prereqs)\n \n in_degree = {node: 0 for node in nodes}\n adj = {node: [] for node in nodes}\n \n for task, prereqs in deps.items():\n for p in prereqs:\n adj[p].append(task)\n in_degree[task] += 1\n \n result = []\n current_layer = sorted([node for node, degree in in_degree.items() if degree == 0])\n \n while current_layer:\n result.append(current_layer)\n next_layer = []\n for node in current_layer:\n for neighbor in adj[node]:\n in_degree[neighbor] -= 1\n if in_degree[neighbor] == 0:\n next_layer.append(neighbor)\n current_layer = sorted(next_layer)\n \n if sum(len(layer) for layer in result) != len(nodes):\n return []\n return result\n```", "usage": {"prompt_tokens": 106, "completion_tokens": 2446, "total_tokens": 2552, "cost": 0.0031648, "is_byok": false, "prompt_tokens_details": {"cached_tokens": 0, "cache_write_tokens": 0, "audio_tokens": 0, "video_tokens": 0}, "cost_details": {"upstream_inference_cost": 0.0031648, "upstream_inference_prompt_cost": 3.392e-05, "upstream_inference_completions_cost": 0.00313088}, "completion_tokens_details": {"reasoning_tokens": 2198, "image_tokens": 0, "audio_tokens": 0}}, "response_id": "gen-1781628078-7dMTZziAVox1QlnNcbvG", "finish_reason": "stop", "score": {"passed": true, "score": 1.0, "details": {"returncode": 0, "stdout": "", "stderr": ""}}, "error": null} {"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": "qwen/qwen3.7-plus", "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": "2026-06-16T16:42:01.473732+00:00", "latency_ms": 36034, "output": "```python\nfrom collections import OrderedDict\n\ndef solve(capacity: int, ops: list[str]) -> list[int]:\n cache = OrderedDict()\n res = []\n for op in ops:\n parts = op.split()\n if parts[0] == \"get\":\n key = int(parts[1])\n if capacity == 0:\n res.append(-1)\n elif key in cache:\n cache.move_to_end(key)\n res.append(cache[key])\n else:\n res.append(-1)\n elif parts[0] == \"put\":\n key = int(parts[1])\n value = int(parts[2])\n if capacity == 0:\n continue\n if key in cache:\n cache.move_to_end(key)\n else:\n if len(cache) == capacity:\n cache.popitem(last=False)\n cache[key] = value\n return res\n```", "usage": {"prompt_tokens": 123, "completion_tokens": 1948, "total_tokens": 2071, "cost": 0.0025328, "is_byok": false, "prompt_tokens_details": {"cached_tokens": 0, "cache_write_tokens": 0, "audio_tokens": 0, "video_tokens": 0}, "cost_details": {"upstream_inference_cost": 0.0025328, "upstream_inference_prompt_cost": 3.936e-05, "upstream_inference_completions_cost": 0.00249344}, "completion_tokens_details": {"reasoning_tokens": 1737, "image_tokens": 0, "audio_tokens": 0}}, "response_id": "gen-1781628123-1aXIaZQ0iWwdlSDCnGqH", "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-4gug7ckv/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-4gug7ckv/solution.py\", line 18, in solve\n key = int(parts[1])\n ^^^^^^^^^^^^^\nValueError: invalid literal for int() with base 10: 'a'\n"}}, "error": null} {"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": "qwen/qwen3.7-plus", "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": "2026-06-16T16:42:37.549023+00:00", "latency_ms": 65423, "output": "import copy\n\ndef solve(base: dict, patch: dict) -> dict:\n res = copy.deepcopy(base)\n for k, v in patch.items():\n if v is None:\n res.pop(k, None)\n elif isinstance(v, dict) and isinstance(res.get(k), dict):\n res[k] = solve(res[k], v)\n else:\n res[k] = copy.deepcopy(v)\n return res", "usage": {"prompt_tokens": 96, "completion_tokens": 3579, "total_tokens": 3675, "cost": 0.00461184, "is_byok": false, "prompt_tokens_details": {"cached_tokens": 0, "cache_write_tokens": 0, "audio_tokens": 0, "video_tokens": 0}, "cost_details": {"upstream_inference_cost": 0.00461184, "upstream_inference_prompt_cost": 3.072e-05, "upstream_inference_completions_cost": 0.00458112}, "completion_tokens_details": {"reasoning_tokens": 3479, "image_tokens": 0, "audio_tokens": 0}}, "response_id": "gen-1781628159-4J70VY2aZaQQKDoRTexq", "finish_reason": "stop", "score": {"passed": true, "score": 1.0, "details": {"returncode": 0, "stdout": "", "stderr": ""}}, "error": null} {"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": "qwen/qwen3.7-plus", "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": "2026-06-16T16:43:43.055345+00:00", "latency_ms": 18945, "output": "import re\n\ndef solve(text: str) -> int:\n multipliers = {'d': 86400000, 'h': 3600000, 'm': 60000, 's': 1000, 'ms': 1}\n return sum(int(val) * multipliers[unit] for val, unit in re.findall(r'(\\d+)\\s*(d|h|m|s|ms)', text))", "usage": {"prompt_tokens": 103, "completion_tokens": 991, "total_tokens": 1094, "cost": 0.00130144, "is_byok": false, "prompt_tokens_details": {"cached_tokens": 0, "cache_write_tokens": 0, "audio_tokens": 0, "video_tokens": 0}, "cost_details": {"upstream_inference_cost": 0.00130144, "upstream_inference_prompt_cost": 3.296e-05, "upstream_inference_completions_cost": 0.00126848}, "completion_tokens_details": {"reasoning_tokens": 883, "image_tokens": 0, "audio_tokens": 0}}, "response_id": "gen-1781628225-74wTDtzQt0IZAew5rUCy", "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-lg5d4gyo/test_solution.py\", line 3, in \n assert solution.solve('1h 30m 500ms') == 5400500\nAssertionError\n"}}, "error": null} {"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": "qwen/qwen3.7-plus", "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": "2026-06-16T16:44:02.021839+00:00", "latency_ms": 49815, "output": "```python\ndef solve(s: str) -> bool:\n stack = []\n in_single = False\n in_double = False\n i = 0\n n = len(s)\n match = {')': '(', ']': '[', '}': '{'}\n while i < n:\n c = s[i]\n if c == '\\\\':\n i += 2\n continue\n if c == \"'\" and not in_double:\n in_single = not in_single\n elif c == '\"' and not in_single:\n in_double = not in_double\n elif not in_single and not in_double:\n if c in '([{':\n stack.append(c)\n elif c in match:\n if not stack or stack[-1] != match[c]:\n return False\n stack.pop()\n i += 1\n return not stack\n```", "usage": {"prompt_tokens": 94, "completion_tokens": 2702, "total_tokens": 2796, "cost": 0.00348864, "is_byok": false, "prompt_tokens_details": {"cached_tokens": 0, "cache_write_tokens": 0, "audio_tokens": 0, "video_tokens": 0}, "cost_details": {"upstream_inference_cost": 0.00348864, "upstream_inference_prompt_cost": 3.008e-05, "upstream_inference_completions_cost": 0.00345856}, "completion_tokens_details": {"reasoning_tokens": 2502, "image_tokens": 0, "audio_tokens": 0}}, "response_id": "gen-1781628244-7O7uc5ecLoo5yXUEYFj5", "finish_reason": "stop", "score": {"passed": true, "score": 1.0, "details": {"returncode": 0, "stdout": "", "stderr": ""}}, "error": null} {"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": "qwen/qwen3.7-plus", "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": "2026-06-16T16:44:51.883313+00:00", "latency_ms": 27471, "output": "```python\ndef solve(rows: list[dict]) -> list[dict]:\n best = {}\n for i, row in enumerate(rows):\n u, ts = row['user'], row['ts']\n if u not in best or (ts, i) > (best[u][0], best[u][1]):\n best[u] = (ts, i, row)\n return [v[2] for v in sorted(best.values(), key=lambda x: x[2]['user'])]\n```", "usage": {"prompt_tokens": 105, "completion_tokens": 1463, "total_tokens": 1568, "cost": 0.00190624, "is_byok": false, "prompt_tokens_details": {"cached_tokens": 0, "cache_write_tokens": 0, "audio_tokens": 0, "video_tokens": 0}, "cost_details": {"upstream_inference_cost": 0.00190624, "upstream_inference_prompt_cost": 3.36e-05, "upstream_inference_completions_cost": 0.00187264}, "completion_tokens_details": {"reasoning_tokens": 1347, "image_tokens": 0, "audio_tokens": 0}}, "response_id": "gen-1781628294-LML3XqmGSm9Z6KdScnNs", "finish_reason": "stop", "score": {"passed": true, "score": 1.0, "details": {"returncode": 0, "stdout": "", "stderr": ""}}, "error": null} {"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": "qwen/qwen3.7-plus", "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": "2026-06-16T16:45:19.394636+00:00", "latency_ms": 29844, "output": "**根因**：Python 的默认参数在函数定义时仅求值一次。`iter([1, 2, 3])` 创建的迭代器在首次调用 `sum()` 后被耗尽，后续调用会复用这个已耗尽的迭代器，导致再次读取时为空。\n\n**最小修复**：将默认参数改为 `None`，并在函数内部进行初始化，确保每次调用时都能创建新的迭代器。\n\nFIX: def summarize(items=None):", "usage": {"prompt_tokens": 105, "completion_tokens": 1601, "total_tokens": 1706, "cost": 0.00208288, "is_byok": false, "prompt_tokens_details": {"cached_tokens": 0, "cache_write_tokens": 0, "audio_tokens": 0, "video_tokens": 0}, "cost_details": {"upstream_inference_cost": 0.00208288, "upstream_inference_prompt_cost": 3.36e-05, "upstream_inference_completions_cost": 0.00204928}, "completion_tokens_details": {"reasoning_tokens": 1496, "image_tokens": 0, "audio_tokens": 0}}, "response_id": "gen-1781628321-iRsalEgAJNKTS28XbW03", "finish_reason": "stop", "score": {"passed": true, "score": 1.0, "details": {"pattern": "FIX:\\s*`?\\s*def\\s+summarize\\(items=None\\)\\s*:"}}, "error": null} {"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": "qwen/qwen3.7-plus", "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": "2026-06-16T16:45:49.242954+00:00", "latency_ms": 14488, "output": "根因：`lambda: shared` 每次返回的是同一个字典对象 `shared` 的引用，导致所有新用户的键都指向同一个字典，从而共享了 `'seen'` 列表。\n\n最小修复：让 lambda 函数每次返回一个全新的字典实例，而不是引用外部变量。\n\nFIX: counts = defaultdict(lambda: {'seen': []})", "usage": {"prompt_tokens": 131, "completion_tokens": 741, "total_tokens": 872, "cost": 0.0009904, "is_byok": false, "prompt_tokens_details": {"cached_tokens": 0, "cache_write_tokens": 0, "audio_tokens": 0, "video_tokens": 0}, "cost_details": {"upstream_inference_cost": 0.0009904, "upstream_inference_prompt_cost": 4.192e-05, "upstream_inference_completions_cost": 0.00094848}, "completion_tokens_details": {"reasoning_tokens": 661, "image_tokens": 0, "audio_tokens": 0}}, "response_id": "gen-1781628351-lpqbeQtoHdm1RGOW1yo1", "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} {"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": "qwen/qwen3.7-plus", "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": "2026-06-16T16:46:03.733040+00:00", "latency_ms": 19418, "output": "Bug：`range` 的 `stop` 参数使用了 `len(items) - page_size`。当列表长度不是 `page_size` 的整数倍时，这会导致最后剩余的元素（不足一页的部分）无法被遍历到。\n\n最小修复：将 `stop` 参数改为 `len(items)`。Python 的切片操作会自动处理超出列表长度的索引，因此直接遍历到 `len(items)` 即可安全且完整地获取包括最后一页在内的所有分页。\n\nFIX: for start in range(0, len(items), page_size):", "usage": {"prompt_tokens": 129, "completion_tokens": 1008, "total_tokens": 1137, "cost": 0.00133152, "is_byok": false, "prompt_tokens_details": {"cached_tokens": 0, "cache_write_tokens": 0, "audio_tokens": 0, "video_tokens": 0}, "cost_details": {"upstream_inference_cost": 0.00133152, "upstream_inference_prompt_cost": 4.128e-05, "upstream_inference_completions_cost": 0.00129024}, "completion_tokens_details": {"reasoning_tokens": 888, "image_tokens": 0, "audio_tokens": 0}}, "response_id": "gen-1781628366-5N46eBAc1QCZxMGAwp1n", "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} {"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": "qwen/qwen3.7-plus", "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": "2026-06-16T16:46:23.152263+00:00", "latency_ms": 8031, "output": "Bug: `sorted` 函数默认按字符串的字典序进行排序，没有指定按数值大小比较的规则。\n\n最小修复: 为 `sorted` 函数添加 `key=int` 参数，使其在比较时将字符串转换为整数。\n\nFIX: return sorted(ids, key=int)", "usage": {"prompt_tokens": 94, "completion_tokens": 388, "total_tokens": 482, "cost": 0.00052672, "is_byok": false, "prompt_tokens_details": {"cached_tokens": 0, "cache_write_tokens": 0, "audio_tokens": 0, "video_tokens": 0}, "cost_details": {"upstream_inference_cost": 0.00052672, "upstream_inference_prompt_cost": 3.008e-05, "upstream_inference_completions_cost": 0.00049664}, "completion_tokens_details": {"reasoning_tokens": 322, "image_tokens": 0, "audio_tokens": 0}}, "response_id": "gen-1781628385-wsadq6Os4IMb0L7ytKH8", "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} {"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": "qwen/qwen3.7-plus", "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": "2026-06-16T16:46:31.184935+00:00", "latency_ms": 27319, "output": "Bug：缓存键 `key` 仅使用了函数名 `fn.__name__`，没有包含参数 `args` 和 `kwargs`，导致不同参数的调用会命中同一个缓存键，从而返回错误的结果。\n\n修复：将参数也加入到缓存键中。为了保证字典键的可哈希性，需要将 `kwargs` 转换为排序后的元组（或 `frozenset`）。\n\nFIX: key = (fn.__name__, args, tuple(sorted(kwargs.items())))", "usage": {"prompt_tokens": 138, "completion_tokens": 1459, "total_tokens": 1597, "cost": 0.00191168, "is_byok": false, "prompt_tokens_details": {"cached_tokens": 0, "cache_write_tokens": 0, "audio_tokens": 0, "video_tokens": 0}, "cost_details": {"upstream_inference_cost": 0.00191168, "upstream_inference_prompt_cost": 4.416e-05, "upstream_inference_completions_cost": 0.00186752}, "completion_tokens_details": {"reasoning_tokens": 1355, "image_tokens": 0, "audio_tokens": 0}}, "response_id": "gen-1781628393-dvFe1f9iK33cHUyCsjZR", "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} {"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": "qwen/qwen3.7-plus", "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": "2026-06-16T16:46:58.507208+00:00", "latency_ms": 11840, "output": "Bug：`copied = items` 只是将变量引用指向同一个列表对象，并未创建新列表（未进行浅拷贝），导致修改 `copied` 时原列表 `items` 也被修改。\n\n修复：使用 `items.copy()`、`list(items)` 或 `items[:]` 进行浅拷贝。\n\nFIX: copied = items.copy()", "usage": {"prompt_tokens": 107, "completion_tokens": 605, "total_tokens": 712, "cost": 0.00080864, "is_byok": false, "prompt_tokens_details": {"cached_tokens": 0, "cache_write_tokens": 0, "audio_tokens": 0, "video_tokens": 0}, "cost_details": {"upstream_inference_cost": 0.00080864, "upstream_inference_prompt_cost": 3.424e-05, "upstream_inference_completions_cost": 0.0007744}, "completion_tokens_details": {"reasoning_tokens": 523, "image_tokens": 0, "audio_tokens": 0}}, "response_id": "gen-1781628420-GSUPgKlBMgREjhA5L7Un", "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} {"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": "qwen/qwen3.7-plus", "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": "2026-06-16T16:47:10.348975+00:00", "latency_ms": 16030, "output": "Bug：`json.loads()` 只能接受字符串、字节或字节数组。如果传入的 `payload` 已经是 `dict`，会引发 `TypeError`。\n\n最小修复：在解析前检查 `payload` 的类型，若已是字典则直接使用，否则进行 JSON 解析。\n\nFIX: data = payload if isinstance(payload, dict) else json.loads(payload)", "usage": {"prompt_tokens": 108, "completion_tokens": 836, "total_tokens": 944, "cost": 0.00110464, "is_byok": false, "prompt_tokens_details": {"cached_tokens": 0, "cache_write_tokens": 0, "audio_tokens": 0, "video_tokens": 0}, "cost_details": {"upstream_inference_cost": 0.00110464, "upstream_inference_prompt_cost": 3.456e-05, "upstream_inference_completions_cost": 0.00107008}, "completion_tokens_details": {"reasoning_tokens": 754, "image_tokens": 0, "audio_tokens": 0}}, "response_id": "gen-1781628432-yYuzNCxdASWwgt9zN7Pq", "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} {"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": "qwen/qwen3.7-plus", "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": "2026-06-16T16:47:26.381051+00:00", "latency_ms": 19247, "output": "Bug：在 Python 中，`finally` 块中的 `return` 语句会覆盖 `try` 块中的返回值，导致函数始终返回 `None`。\n\n最小修复：删除 `finally` 块中的 `return None`。\n\nFIX: 删除 finally 中的 return None", "usage": {"prompt_tokens": 114, "completion_tokens": 1012, "total_tokens": 1126, "cost": 0.00133184, "is_byok": false, "prompt_tokens_details": {"cached_tokens": 0, "cache_write_tokens": 0, "audio_tokens": 0, "video_tokens": 0}, "cost_details": {"upstream_inference_cost": 0.00133184, "upstream_inference_prompt_cost": 3.648e-05, "upstream_inference_completions_cost": 0.00129536}, "completion_tokens_details": {"reasoning_tokens": 945, "image_tokens": 0, "audio_tokens": 0}}, "response_id": "gen-1781628448-iqLdJI0QeTBzuUPwOwuQ", "finish_reason": "stop", "score": {"passed": true, "score": 1.0, "details": {"matched": ["删除 finally 中的 return None", "删除 `finally` 块中的 `return None`"]}}, "error": null} {"case_id": "challenge-reasoning-001", "suite": "reasoning", "case_version": "0.2.0", "tags": ["arithmetic", "multi-step"], "scorer": "exact", "expected": {"answer": "125", "normalize": true}, "model": "qwen/qwen3.7-plus", "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": "2026-06-16T16:47:45.630440+00:00", "latency_ms": 6257, "output": "125", "usage": {"prompt_tokens": 111, "completion_tokens": 292, "total_tokens": 403, "cost": 0.00040928, "is_byok": false, "prompt_tokens_details": {"cached_tokens": 0, "cache_write_tokens": 0, "audio_tokens": 0, "video_tokens": 0}, "cost_details": {"upstream_inference_cost": 0.00040928, "upstream_inference_prompt_cost": 3.552e-05, "upstream_inference_completions_cost": 0.00037376}, "completion_tokens_details": {"reasoning_tokens": 284, "image_tokens": 0, "audio_tokens": 0}}, "response_id": "gen-1781628467-AhKlInbQaTYF0X7HmNLX", "finish_reason": "stop", "score": {"passed": true, "score": 1.0, "details": {"expected": "125", "actual": "125"}}, "error": null} {"case_id": "challenge-reasoning-002", "suite": "reasoning", "case_version": "0.2.0", "tags": ["constraint-solving"], "scorer": "exact", "expected": {"answer": "D", "normalize": true}, "model": "qwen/qwen3.7-plus", "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": "2026-06-16T16:47:51.889974+00:00", "latency_ms": 30246, "output": "D", "usage": {"prompt_tokens": 103, "completion_tokens": 1631, "total_tokens": 1734, "cost": 0.00212064, "is_byok": false, "prompt_tokens_details": {"cached_tokens": 0, "cache_write_tokens": 0, "audio_tokens": 0, "video_tokens": 0}, "cost_details": {"upstream_inference_cost": 0.00212064, "upstream_inference_prompt_cost": 3.296e-05, "upstream_inference_completions_cost": 0.00208768}, "completion_tokens_details": {"reasoning_tokens": 1626, "image_tokens": 0, "audio_tokens": 0}}, "response_id": "gen-1781628474-9hBnJUxbzgOyPH6QnsNM", "finish_reason": "stop", "score": {"passed": true, "score": 1.0, "details": {"expected": "D", "actual": "D"}}, "error": null} {"case_id": "challenge-reasoning-003", "suite": "reasoning", "case_version": "0.2.0", "tags": ["binary", "symbolic"], "scorer": "exact", "expected": {"answer": "0", "normalize": true}, "model": "qwen/qwen3.7-plus", "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": "2026-06-16T16:48:22.137642+00:00", "latency_ms": 4938, "output": "0", "usage": {"prompt_tokens": 95, "completion_tokens": 215, "total_tokens": 310, "cost": 0.0003056, "is_byok": false, "prompt_tokens_details": {"cached_tokens": 0, "cache_write_tokens": 0, "audio_tokens": 0, "video_tokens": 0}, "cost_details": {"upstream_inference_cost": 0.0003056, "upstream_inference_prompt_cost": 3.04e-05, "upstream_inference_completions_cost": 0.0002752}, "completion_tokens_details": {"reasoning_tokens": 210, "image_tokens": 0, "audio_tokens": 0}}, "response_id": "gen-1781628504-VDJTogIVSH1GjBynUJ1I", "finish_reason": "stop", "score": {"passed": true, "score": 1.0, "details": {"expected": "0", "actual": "0"}}, "error": null} {"case_id": "challenge-reasoning-004", "suite": "reasoning", "case_version": "0.2.0", "tags": ["set", "operations"], "scorer": "exact", "expected": {"answer": "18", "normalize": true}, "model": "qwen/qwen3.7-plus", "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": "2026-06-16T16:48:27.077125+00:00", "latency_ms": 6282, "output": "18", "usage": {"prompt_tokens": 98, "completion_tokens": 293, "total_tokens": 391, "cost": 0.0004064, "is_byok": false, "prompt_tokens_details": {"cached_tokens": 0, "cache_write_tokens": 0, "audio_tokens": 0, "video_tokens": 0}, "cost_details": {"upstream_inference_cost": 0.0004064, "upstream_inference_prompt_cost": 3.136e-05, "upstream_inference_completions_cost": 0.00037504}, "completion_tokens_details": {"reasoning_tokens": 286, "image_tokens": 0, "audio_tokens": 0}}, "response_id": "gen-1781628509-aXaMLxXjE2cTfoEm6Vta", "finish_reason": "stop", "score": {"passed": true, "score": 1.0, "details": {"expected": "18", "actual": "18"}}, "error": null} {"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": "qwen/qwen3.7-plus", "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": "2026-06-16T16:48:33.360947+00:00", "latency_ms": 8313, "output": "可满足", "usage": {"prompt_tokens": 109, "completion_tokens": 391, "total_tokens": 500, "cost": 0.00053536, "is_byok": false, "prompt_tokens_details": {"cached_tokens": 0, "cache_write_tokens": 0, "audio_tokens": 0, "video_tokens": 0}, "cost_details": {"upstream_inference_cost": 0.00053536, "upstream_inference_prompt_cost": 3.488e-05, "upstream_inference_completions_cost": 0.00050048}, "completion_tokens_details": {"reasoning_tokens": 386, "image_tokens": 0, "audio_tokens": 0}}, "response_id": "gen-1781628515-Qzd60nSeLrViktBmo6J6", "finish_reason": "stop", "score": {"passed": true, "score": 1.0, "details": {"expected": "可满足", "actual": "可满足"}}, "error": null} {"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": "qwen/qwen3.7-plus", "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": "2026-06-16T16:48:41.675422+00:00", "latency_ms": 6171, "output": "根据提供的资料，未提及 v0.2 没有把 LLM-as-judge 作为默认判分的原因。", "usage": {"prompt_tokens": 166, "completion_tokens": 298, "total_tokens": 464, "cost": 0.00043456, "is_byok": false, "prompt_tokens_details": {"cached_tokens": 0, "cache_write_tokens": 0, "audio_tokens": 0, "video_tokens": 0}, "cost_details": {"upstream_inference_cost": 0.00043456, "upstream_inference_prompt_cost": 5.312e-05, "upstream_inference_completions_cost": 0.00038144}, "completion_tokens_details": {"reasoning_tokens": 268, "image_tokens": 0, "audio_tokens": 0}}, "response_id": "gen-1781628523-FKySwArHEGK5TxYLFKUd", "finish_reason": "stop", "score": {"passed": false, "score": 0.0, "details": {"missing": ["人工抽检"], "missing_any": [], "present_forbidden": [], "char_count": 44, "length_ok": true, "length_scored": false}}, "error": null} {"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": "qwen/qwen3.7-plus", "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": "2026-06-16T16:48:47.848726+00:00", "latency_ms": 12147, "output": "资料更支持选择**模型 Beta**（因为其成本更低且延迟更低，符合低成本高吞吐的需求）。\n\n多模态表现**不能判断**（资料明确说明没有给出两者在多模态任务上的表现）。", "usage": {"prompt_tokens": 152, "completion_tokens": 598, "total_tokens": 750, "cost": 0.00081408, "is_byok": false, "prompt_tokens_details": {"cached_tokens": 0, "cache_write_tokens": 0, "audio_tokens": 0, "video_tokens": 0}, "cost_details": {"upstream_inference_cost": 0.00081408, "upstream_inference_prompt_cost": 4.864e-05, "upstream_inference_completions_cost": 0.00076544}, "completion_tokens_details": {"reasoning_tokens": 548, "image_tokens": 0, "audio_tokens": 0}}, "response_id": "gen-1781628530-ZPjBn4cPY6LoirdEwi05", "finish_reason": "stop", "score": {"passed": true, "score": 1.0, "details": {"missing": [], "missing_any": [], "present_forbidden": [], "char_count": 85, "length_ok": true, "length_scored": false}}, "error": null} {"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": "qwen/qwen3.7-plus", "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": "2026-06-16T16:48:59.997016+00:00", "latency_ms": 14013, "output": "K 模型在 hard set 上的原始结果为 47/50。若排除 2 道因题面歧义（复测已通过）的题目，其实际有效通过题数为 49/50（通过率 98%）；另有 1 题未通过是因 reasoning tokens 用尽导致空输出。", "usage": {"prompt_tokens": 137, "completion_tokens": 723, "total_tokens": 860, "cost": 0.00096928, "is_byok": false, "prompt_tokens_details": {"cached_tokens": 0, "cache_write_tokens": 0, "audio_tokens": 0, "video_tokens": 0}, "cost_details": {"upstream_inference_cost": 0.00096928, "upstream_inference_prompt_cost": 4.384e-05, "upstream_inference_completions_cost": 0.00092544}, "completion_tokens_details": {"reasoning_tokens": 646, "image_tokens": 0, "audio_tokens": 0}}, "response_id": "gen-1781628542-r4iF4A6JldYrPnOsvcAh", "finish_reason": "stop", "score": {"passed": true, "score": 1.0, "details": {"missing": [], "missing_any": [], "present_forbidden": [], "char_count": 115, "length_ok": true, "length_scored": false}}, "error": null} {"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": "qwen/qwen3.7-plus", "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": "2026-06-16T16:49:14.011514+00:00", "latency_ms": 2924, "output": "不能。资料明确指出报告不包含供应商真实利润率。", "usage": {"prompt_tokens": 102, "completion_tokens": 119, "total_tokens": 221, "cost": 0.00018496, "is_byok": false, "prompt_tokens_details": {"cached_tokens": 0, "cache_write_tokens": 0, "audio_tokens": 0, "video_tokens": 0}, "cost_details": {"upstream_inference_cost": 0.00018496, "upstream_inference_prompt_cost": 3.264e-05, "upstream_inference_completions_cost": 0.00015232}, "completion_tokens_details": {"reasoning_tokens": 104, "image_tokens": 0, "audio_tokens": 0}}, "response_id": "gen-1781628556-0M2srcnC5JCS4ZGhDxj5", "finish_reason": "stop", "score": {"passed": true, "score": 1.0, "details": {"missing": [], "missing_any": [], "present_forbidden": [], "char_count": 23, "length_ok": true, "length_scored": false}}, "error": null} {"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": "qwen/qwen3.7-plus", "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": "2026-06-16T16:49:16.937683+00:00", "latency_ms": 15841, "output": "{\"status\":\"review\",\"scores\":{\"coding\":90,\"debugging\":85,\"rag\":88},\"note\":\"各项指标均达标\"}", "usage": {"prompt_tokens": 91, "completion_tokens": 801, "total_tokens": 892, "cost": 0.0010544, "is_byok": false, "prompt_tokens_details": {"cached_tokens": 0, "cache_write_tokens": 0, "audio_tokens": 0, "video_tokens": 0}, "cost_details": {"upstream_inference_cost": 0.0010544, "upstream_inference_prompt_cost": 2.912e-05, "upstream_inference_completions_cost": 0.00102528}, "completion_tokens_details": {"reasoning_tokens": 766, "image_tokens": 0, "audio_tokens": 0}}, "response_id": "gen-1781628559-wVmrTdRiONPNpCJVWvl3", "finish_reason": "stop", "score": {"passed": true, "score": 1.0, "details": {"errors": [], "value": {"status": "review", "scores": {"coding": 90, "debugging": 85, "rag": 88}, "note": "各项指标均达标"}}}, "error": null} {"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*$", "flags": "is"}, "model": "qwen/qwen3.7-plus", "provider": null, "prompt": "只输出 4 行，不要解释。第 1 行是 BEGIN，第 4 行是 END。第 2 行必须包含“模型评测”，第 3 行必须包含“HTML报告”。不能出现任何项目符号。", "parameters": {"temperature": 0, "max_tokens": 16384, "seed": null}, "prompt_hash": "773b2e7398be979e738e08c3c8486ca69bae0f71d9a2abe3430cef775f5a59fb", "started_at": "2026-06-16T16:49:32.781130+00:00", "latency_ms": 10937, "output": "BEGIN\n模型评测\nHTML报告\nEND", "usage": {"prompt_tokens": 94, "completion_tokens": 553, "total_tokens": 647, "cost": 0.00073792, "is_byok": false, "prompt_tokens_details": {"cached_tokens": 0, "cache_write_tokens": 0, "audio_tokens": 0, "video_tokens": 0}, "cost_details": {"upstream_inference_cost": 0.00073792, "upstream_inference_prompt_cost": 3.008e-05, "upstream_inference_completions_cost": 0.00070784}, "completion_tokens_details": {"reasoning_tokens": 539, "image_tokens": 0, "audio_tokens": 0}}, "response_id": "gen-1781628575-UuT3RNYHYGfGw27jm6U4", "finish_reason": "stop", "score": {"passed": true, "score": 1.0, "details": {"pattern": "^\\s*BEGIN\\s*\\n[^\\n]*模型评测[^\\n]*\\n[^\\n]*HTML报告[^\\n]*\\nEND\\s*$"}}, "error": null} {"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": "qwen/qwen3.7-plus", "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": "2026-06-16T16:49:43.720886+00:00", "latency_ms": 13163, "output": "这道难题的区分度很高，能有效检验考生的真实水平。", "usage": {"prompt_tokens": 78, "completion_tokens": 675, "total_tokens": 753, "cost": 0.00088896, "is_byok": false, "prompt_tokens_details": {"cached_tokens": 0, "cache_write_tokens": 0, "audio_tokens": 0, "video_tokens": 0}, "cost_details": {"upstream_inference_cost": 0.00088896, "upstream_inference_prompt_cost": 2.496e-05, "upstream_inference_completions_cost": 0.000864}, "completion_tokens_details": {"reasoning_tokens": 657, "image_tokens": 0, "audio_tokens": 0}}, "response_id": "gen-1781628586-QzvhEbM22mhyKwUwWR1w", "finish_reason": "stop", "score": {"passed": true, "score": 1.0, "details": {"missing": [], "missing_any": [], "present_forbidden": [], "char_count": 24, "length_ok": true, "length_scored": false}}, "error": null} {"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": "qwen/qwen3.7-plus", "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": "2026-06-16T16:49:56.885753+00:00", "latency_ms": 10848, "output": "模型在简单题上拿满分，绝不等于具备强大的真实能力。盲目迷信高分容易陷入误区，我们更应关注模型在复杂场景下的失败样例。只有直面短板、深入剖析，才能客观评估其真实水平，避免被表面的完美所误导。", "usage": {"prompt_tokens": 85, "completion_tokens": 557, "total_tokens": 642, "cost": 0.00074016, "is_byok": false, "prompt_tokens_details": {"cached_tokens": 0, "cache_write_tokens": 0, "audio_tokens": 0, "video_tokens": 0}, "cost_details": {"upstream_inference_cost": 0.00074016, "upstream_inference_prompt_cost": 2.72e-05, "upstream_inference_completions_cost": 0.00071296}, "completion_tokens_details": {"reasoning_tokens": 497, "image_tokens": 0, "audio_tokens": 0}}, "response_id": "gen-1781628599-FKObjL66Ovze1TM2GPTH", "finish_reason": "stop", "score": {"passed": true, "score": 1.0, "details": {"missing": [], "missing_any": [], "present_forbidden": [], "char_count": 94, "length_ok": true, "length_scored": false}}, "error": null} {"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": "qwen/qwen3.7-plus", "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": "2026-06-16T16:50:07.735927+00:00", "latency_ms": 9761, "output": "该模型表面性能优异，但在面临复杂调试时，其稳定性明显不足。", "usage": {"prompt_tokens": 92, "completion_tokens": 487, "total_tokens": 579, "cost": 0.0006528, "is_byok": false, "prompt_tokens_details": {"cached_tokens": 0, "cache_write_tokens": 0, "audio_tokens": 0, "video_tokens": 0}, "cost_details": {"upstream_inference_cost": 0.0006528, "upstream_inference_prompt_cost": 2.944e-05, "upstream_inference_completions_cost": 0.00062336}, "completion_tokens_details": {"reasoning_tokens": 465, "image_tokens": 0, "audio_tokens": 0}}, "response_id": "gen-1781628610-r9V3YaQ3zuWgC78bFBTp", "finish_reason": "stop", "score": {"passed": true, "score": 1.0, "details": {"missing": [], "missing_any": [], "present_forbidden": [], "char_count": 29, "length_ok": true, "length_scored": false}}, "error": null} {"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": "qwen/qwen3.7-plus", "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": "2026-06-16T16:50:17.498577+00:00", "latency_ms": 19924, "output": "```python\ndef solve(events: list[dict]) -> dict[str, int]:\n inv = {}\n for e in events:\n inv[e[\"user\"]] = inv.get(e[\"user\"], 0) + (int(e[\"amount\"]) if e[\"type\"] == \"add\" else -int(e[\"amount\"]))\n return {k: v for k, v in sorted(inv.items()) if v != 0}\n```", "usage": {"prompt_tokens": 122, "completion_tokens": 1049, "total_tokens": 1171, "cost": 0.00138176, "is_byok": false, "prompt_tokens_details": {"cached_tokens": 0, "cache_write_tokens": 0, "audio_tokens": 0, "video_tokens": 0}, "cost_details": {"upstream_inference_cost": 0.00138176, "upstream_inference_prompt_cost": 3.904e-05, "upstream_inference_completions_cost": 0.00134272}, "completion_tokens_details": {"reasoning_tokens": 951, "image_tokens": 0, "audio_tokens": 0}}, "response_id": "gen-1781628619-ivfQWlWIkczlXcIJSSGB", "finish_reason": "stop", "score": {"passed": true, "score": 1.0, "details": {"returncode": 0, "stdout": "", "stderr": ""}}, "error": null} {"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": "qwen/qwen3.7-plus", "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": "2026-06-16T16:50:37.467699+00:00", "latency_ms": 16398, "output": "```python\ndef solve(expr: str) -> int:\n res = 0\n num = 0\n sign = 1\n stack = []\n for char in expr:\n if char.isdigit():\n num = num * 10 + int(char)\n elif char == '+':\n res += sign * num\n num = 0\n sign = 1\n elif char == '-':\n res += sign * num\n num = 0\n sign = -1\n elif char == '(':\n stack.append(res)\n stack.append(sign)\n res = 0\n sign = 1\n elif char == ')':\n res += sign * num\n num = 0\n res *= stack.pop()\n res += stack.pop()\n res += sign * num\n return res\n```", "usage": {"prompt_tokens": 82, "completion_tokens": 857, "total_tokens": 939, "cost": 0.0011232, "is_byok": false, "prompt_tokens_details": {"cached_tokens": 0, "cache_write_tokens": 0, "audio_tokens": 0, "video_tokens": 0}, "cost_details": {"upstream_inference_cost": 0.0011232, "upstream_inference_prompt_cost": 2.624e-05, "upstream_inference_completions_cost": 0.00109696}, "completion_tokens_details": {"reasoning_tokens": 661, "image_tokens": 0, "audio_tokens": 0}}, "response_id": "gen-1781628639-LrRw924FGXaSF1cf8EoC", "finish_reason": "stop", "score": {"passed": true, "score": 1.0, "details": {"returncode": 0, "stdout": "", "stderr": ""}}, "error": null} {"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": "qwen/qwen3.7-plus", "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": "2026-06-16T16:50:53.903926+00:00", "latency_ms": 20359, "output": "```python\ndef solve(rows: list[str]) -> list[tuple[str, int]]:\n max_scores = {}\n for row in rows:\n parts = row.split()\n if not parts:\n continue\n name, score = parts[0], int(parts[1])\n if name not in max_scores or score > max_scores[name]:\n max_scores[name] = score\n return sorted(max_scores.items(), key=lambda x: (-x[1], x[0]))\n```", "usage": {"prompt_tokens": 101, "completion_tokens": 1077, "total_tokens": 1178, "cost": 0.00141088, "is_byok": false, "prompt_tokens_details": {"cached_tokens": 0, "cache_write_tokens": 0, "audio_tokens": 0, "video_tokens": 0}, "cost_details": {"upstream_inference_cost": 0.00141088, "upstream_inference_prompt_cost": 3.232e-05, "upstream_inference_completions_cost": 0.00137856}, "completion_tokens_details": {"reasoning_tokens": 963, "image_tokens": 0, "audio_tokens": 0}}, "response_id": "gen-1781628656-fK8xI1HhjVkVERFCwSk2", "finish_reason": "stop", "score": {"passed": true, "score": 1.0, "details": {"returncode": 0, "stdout": "", "stderr": ""}}, "error": null} {"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": "qwen/qwen3.7-plus", "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": "2026-06-16T16:51:14.302530+00:00", "latency_ms": 31135, "output": "```python\ndef solve(pattern: str, text: str) -> bool:\n i, j = 0, 0\n star_idx = -1\n match = 0\n \n while i < len(text):\n if j < len(pattern) and (pattern[j] == '?' or pattern[j] == text[i]):\n i += 1\n j += 1\n elif j < len(pattern) and pattern[j] == '*':\n star_idx = j\n match = i\n j += 1\n elif star_idx != -1:\n j = star_idx + 1\n match += 1\n i = match\n else:\n return False\n \n while j < len(pattern) and pattern[j] == '*':\n j += 1\n \n return j == len(pattern)\n```", "usage": {"prompt_tokens": 84, "completion_tokens": 1667, "total_tokens": 1751, "cost": 0.00216064, "is_byok": false, "prompt_tokens_details": {"cached_tokens": 0, "cache_write_tokens": 0, "audio_tokens": 0, "video_tokens": 0}, "cost_details": {"upstream_inference_cost": 0.00216064, "upstream_inference_prompt_cost": 2.688e-05, "upstream_inference_completions_cost": 0.00213376}, "completion_tokens_details": {"reasoning_tokens": 1479, "image_tokens": 0, "audio_tokens": 0}}, "response_id": "gen-1781628676-dl1Z2sLEUDjhTIrD4cU6", "finish_reason": "stop", "score": {"passed": true, "score": 1.0, "details": {"returncode": 0, "stdout": "", "stderr": ""}}, "error": null} {"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": "qwen/qwen3.7-plus", "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": "2026-06-16T16:51:45.453602+00:00", "latency_ms": 28620, "output": "```python\ndef solve(tree: dict) -> list[str]:\n def dfs(d, path=\"\"):\n for k, v in d.items():\n p = f\"{path}.{k}\" if path else str(k)\n if isinstance(v, dict):\n yield from dfs(v, p)\n else:\n yield f\"{p}={v}\"\n return sorted(dfs(tree))\n```", "usage": {"prompt_tokens": 106, "completion_tokens": 1531, "total_tokens": 1637, "cost": 0.0019936, "is_byok": false, "prompt_tokens_details": {"cached_tokens": 0, "cache_write_tokens": 0, "audio_tokens": 0, "video_tokens": 0}, "cost_details": {"upstream_inference_cost": 0.0019936, "upstream_inference_prompt_cost": 3.392e-05, "upstream_inference_completions_cost": 0.00195968}, "completion_tokens_details": {"reasoning_tokens": 1437, "image_tokens": 0, "audio_tokens": 0}}, "response_id": "gen-1781628707-DvxvY10ptsDPz4ioOCJy", "finish_reason": "stop", "score": {"passed": true, "score": 1.0, "details": {"returncode": 0, "stdout": "", "stderr": ""}}, "error": null} {"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": "qwen/qwen3.7-plus", "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": "2026-06-16T16:52:14.110648+00:00", "latency_ms": 319136, "output": "```python\ndef solve(nums: list[int]) -> int:\n dp0 = nums[0]\n dp1 = 0\n ans = float('-inf')\n for i in range(1, len(nums)):\n dp1 = max(dp0, dp1 + nums[i])\n dp0 = max(nums[i], dp0 + nums[i])\n ans = max(ans, dp1)\n return ans\n```", "usage": {"prompt_tokens": 88, "completion_tokens": 17656, "total_tokens": 17744, "cost": 0.02262784, "is_byok": false, "prompt_tokens_details": {"cached_tokens": 0, "cache_write_tokens": 0, "audio_tokens": 0, "video_tokens": 0}, "cost_details": {"upstream_inference_cost": 0.02262784, "upstream_inference_prompt_cost": 2.816e-05, "upstream_inference_completions_cost": 0.02259968}, "completion_tokens_details": {"reasoning_tokens": 17556, "image_tokens": 0, "audio_tokens": 0}}, "response_id": "gen-1781628736-W8g9FLtEAtGv0wzowS8f", "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-g2po5qf_/test_solution.py\", line 4, in \n assert solution.solve([1,-2,-2,3]) == 3\nAssertionError\n"}}, "error": null} {"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": "qwen/qwen3.7-plus", "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": "2026-06-16T16:57:33.290420+00:00", "latency_ms": 16548, "output": "Bug：当前的列表推导式缺少过滤条件，导致 `strip()` 后变为空字符串的字段依然被保留在结果列表中。\n\n最小修复：在列表推导式末尾添加 `if part.strip()` 条件，以过滤掉空字符串。\n\nFIX: return [part.strip() for part in s.split(',') if part.strip()]", "usage": {"prompt_tokens": 107, "completion_tokens": 839, "total_tokens": 946, "cost": 0.00110816, "is_byok": false, "prompt_tokens_details": {"cached_tokens": 0, "cache_write_tokens": 0, "audio_tokens": 0, "video_tokens": 0}, "cost_details": {"upstream_inference_cost": 0.00110816, "upstream_inference_prompt_cost": 3.424e-05, "upstream_inference_completions_cost": 0.00107392}, "completion_tokens_details": {"reasoning_tokens": 764, "image_tokens": 0, "audio_tokens": 0}}, "response_id": "gen-1781629055-GUrjC1fXKEOiVCYDiwuJ", "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} {"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": "qwen/qwen3.7-plus", "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": "2026-06-16T16:57:49.841260+00:00", "latency_ms": 42898, "output": "**Bug**：`r['ts']` 是字符串类型，`sort` 会按字典序进行比较。如果 `ts` 是 Unix 时间戳的字符串形式（例如 `\"1000\"` 和 `\"200\"`），字典序会导致 `\"1000\" < \"200\"`，从而产生错误的排序结果。\n\n**最小修复**：在排序时，将字符串显式转换为数值类型（如 `float` 或 `int`）再进行比较。\n\nFIX: rows.sort(key=lambda r: float(r['ts']))", "usage": {"prompt_tokens": 95, "completion_tokens": 2314, "total_tokens": 2409, "cost": 0.00299232, "is_byok": false, "prompt_tokens_details": {"cached_tokens": 0, "cache_write_tokens": 0, "audio_tokens": 0, "video_tokens": 0}, "cost_details": {"upstream_inference_cost": 0.00299232, "upstream_inference_prompt_cost": 3.04e-05, "upstream_inference_completions_cost": 0.00296192}, "completion_tokens_details": {"reasoning_tokens": 2188, "image_tokens": 0, "audio_tokens": 0}}, "response_id": "gen-1781629072-vS1InAWrYBHRnFT57JFv", "finish_reason": "stop", "score": {"passed": true, "score": 1.0, "details": {"matched": ["float(r['ts'])", "key=lambda r: float(r['ts'])"]}}, "error": null} {"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": "qwen/qwen3.7-plus", "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": "2026-06-16T16:58:32.741346+00:00", "latency_ms": 15873, "output": "Bug 在于使用了 `or` 运算符。在 Python 中，`0` 是 falsy 值，因此当 `limit=0` 时，`limit or 10` 会计算为 `10`，导致误判。\n\n最小修复是显式检查 `limit` 是否为 `None`，而不是依赖其布尔值。\n\nFIX: limit = 10 if limit is None else limit", "usage": {"prompt_tokens": 109, "completion_tokens": 828, "total_tokens": 937, "cost": 0.00109472, "is_byok": false, "prompt_tokens_details": {"cached_tokens": 0, "cache_write_tokens": 0, "audio_tokens": 0, "video_tokens": 0}, "cost_details": {"upstream_inference_cost": 0.00109472, "upstream_inference_prompt_cost": 3.488e-05, "upstream_inference_completions_cost": 0.00105984}, "completion_tokens_details": {"reasoning_tokens": 732, "image_tokens": 0, "audio_tokens": 0}}, "response_id": "gen-1781629115-xo0RfdrSPf9HMIvPSx9M", "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} {"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": "qwen/qwen3.7-plus", "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": "2026-06-16T16:58:48.617351+00:00", "latency_ms": 22979, "output": "Bug：`dict(config)` 仅执行浅拷贝，嵌套的字典（如 `config['db']`）仍指向原对象的引用，因此修改副本时会影响原配置。\n\n最小修复：使用 `copy` 模块的 `deepcopy` 进行深拷贝（需在文件顶部添加 `import copy`）。\n\nFIX: copied = copy.deepcopy(config)", "usage": {"prompt_tokens": 113, "completion_tokens": 1230, "total_tokens": 1343, "cost": 0.00161056, "is_byok": false, "prompt_tokens_details": {"cached_tokens": 0, "cache_write_tokens": 0, "audio_tokens": 0, "video_tokens": 0}, "cost_details": {"upstream_inference_cost": 0.00161056, "upstream_inference_prompt_cost": 3.616e-05, "upstream_inference_completions_cost": 0.0015744}, "completion_tokens_details": {"reasoning_tokens": 1149, "image_tokens": 0, "audio_tokens": 0}}, "response_id": "gen-1781629130-0PXXv3DwE9Yeb0FRchGr", "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} {"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": "qwen/qwen3.7-plus", "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": "2026-06-16T16:59:11.598793+00:00", "latency_ms": 15422, "output": "Bug：`next()` 函数在没有提供默认值时，如果生成器为空（即找不到匹配项），会抛出 `StopIteration` 异常。\n\n最小修复：为 `next()` 函数添加第二个参数 `None` 作为默认返回值，并将生成器表达式用括号括起来以满足语法要求。\n\nFIX: return next((u for u in users if u['name'] == name), None)", "usage": {"prompt_tokens": 108, "completion_tokens": 802, "total_tokens": 910, "cost": 0.00106112, "is_byok": false, "prompt_tokens_details": {"cached_tokens": 0, "cache_write_tokens": 0, "audio_tokens": 0, "video_tokens": 0}, "cost_details": {"upstream_inference_cost": 0.00106112, "upstream_inference_prompt_cost": 3.456e-05, "upstream_inference_completions_cost": 0.00102656}, "completion_tokens_details": {"reasoning_tokens": 711, "image_tokens": 0, "audio_tokens": 0}}, "response_id": "gen-1781629153-5eWOD7HkCjVagxeGhnjd", "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} {"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": "qwen/qwen3.7-plus", "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": "2026-06-16T16:59:27.023514+00:00", "latency_ms": 27041, "output": "**Bug 指出**：\nPython 中的可变默认参数（如 `[]`）在函数定义时仅被求值并初始化一次。这会导致所有未显式传入 `logs` 参数的调用共享同一个列表实例，从而产生意外的状态累积。\n\n**最小修复**：\n将默认参数改为不可变的 `None`，并在函数内部判断，若为 `None` 则初始化为新的空列表 `[]`。\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\nFIX: def log_request(req, logs=None):", "usage": {"prompt_tokens": 108, "completion_tokens": 1394, "total_tokens": 1502, "cost": 0.00181888, "is_byok": false, "prompt_tokens_details": {"cached_tokens": 0, "cache_write_tokens": 0, "audio_tokens": 0, "video_tokens": 0}, "cost_details": {"upstream_inference_cost": 0.00181888, "upstream_inference_prompt_cost": 3.456e-05, "upstream_inference_completions_cost": 0.00178432}, "completion_tokens_details": {"reasoning_tokens": 1247, "image_tokens": 0, "audio_tokens": 0}}, "response_id": "gen-1781629169-Ziqpq0PLpXCJawq356PB", "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} {"case_id": "challenge-reasoning-006", "suite": "reasoning", "case_version": "0.2.0", "tags": ["symbolic", "sequence"], "scorer": "exact", "expected": {"answer": "3", "normalize": true}, "model": "qwen/qwen3.7-plus", "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": "2026-06-16T16:59:54.066520+00:00", "latency_ms": 7394, "output": "3", "usage": {"prompt_tokens": 96, "completion_tokens": 352, "total_tokens": 448, "cost": 0.00048128, "is_byok": false, "prompt_tokens_details": {"cached_tokens": 0, "cache_write_tokens": 0, "audio_tokens": 0, "video_tokens": 0}, "cost_details": {"upstream_inference_cost": 0.00048128, "upstream_inference_prompt_cost": 3.072e-05, "upstream_inference_completions_cost": 0.00045056}, "completion_tokens_details": {"reasoning_tokens": 347, "image_tokens": 0, "audio_tokens": 0}}, "response_id": "gen-1781629196-MRLIn4wzUZv2LdyxGNLj", "finish_reason": "stop", "score": {"passed": true, "score": 1.0, "details": {"expected": "3", "actual": "3"}}, "error": null} {"case_id": "challenge-reasoning-007", "suite": "reasoning", "case_version": "0.2.0", "tags": ["percentage", "metrics"], "scorer": "exact", "expected": {"answer": "120", "normalize": true}, "model": "qwen/qwen3.7-plus", "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": "2026-06-16T17:00:01.463019+00:00", "latency_ms": 5220, "output": "120", "usage": {"prompt_tokens": 75, "completion_tokens": 237, "total_tokens": 312, "cost": 0.00032736, "is_byok": false, "prompt_tokens_details": {"cached_tokens": 0, "cache_write_tokens": 0, "audio_tokens": 0, "video_tokens": 0}, "cost_details": {"upstream_inference_cost": 0.00032736, "upstream_inference_prompt_cost": 2.4e-05, "upstream_inference_completions_cost": 0.00030336}, "completion_tokens_details": {"reasoning_tokens": 229, "image_tokens": 0, "audio_tokens": 0}}, "response_id": "gen-1781629203-tIPpzV8pvVyuBUOCWX5c", "finish_reason": "stop", "score": {"passed": true, "score": 1.0, "details": {"expected": "120", "actual": "120"}}, "error": null} {"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": "qwen/qwen3.7-plus", "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": "2026-06-16T17:00:06.684956+00:00", "latency_ms": 5415, "output": "讨论“模型真实能力”应优先引用 **ability mode**；讨论“低预算可用性”应优先引用 **efficiency mode**。", "usage": {"prompt_tokens": 144, "completion_tokens": 254, "total_tokens": 398, "cost": 0.0003712, "is_byok": false, "prompt_tokens_details": {"cached_tokens": 0, "cache_write_tokens": 0, "audio_tokens": 0, "video_tokens": 0}, "cost_details": {"upstream_inference_cost": 0.0003712, "upstream_inference_prompt_cost": 4.608e-05, "upstream_inference_completions_cost": 0.00032512}, "completion_tokens_details": {"reasoning_tokens": 221, "image_tokens": 0, "audio_tokens": 0}}, "response_id": "gen-1781629208-D4Xbw5zRcC8wTBbFLGJb", "finish_reason": "stop", "score": {"passed": true, "score": 1.0, "details": {"missing": [], "missing_any": [], "present_forbidden": [], "char_count": 69, "length_ok": true, "length_scored": false}}, "error": null} {"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": "qwen/qwen3.7-plus", "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": "2026-06-16T17:00:12.101299+00:00", "latency_ms": 3005, "output": "这次失败的根因是“擅自补类型假设”。", "usage": {"prompt_tokens": 102, "completion_tokens": 117, "total_tokens": 219, "cost": 0.0001824, "is_byok": false, "prompt_tokens_details": {"cached_tokens": 0, "cache_write_tokens": 0, "audio_tokens": 0, "video_tokens": 0}, "cost_details": {"upstream_inference_cost": 0.0001824, "upstream_inference_prompt_cost": 3.264e-05, "upstream_inference_completions_cost": 0.00014976}, "completion_tokens_details": {"reasoning_tokens": 101, "image_tokens": 0, "audio_tokens": 0}}, "response_id": "gen-1781629214-gx4QqrLlvqi5iUY2e6I2", "finish_reason": "stop", "score": {"passed": false, "score": 0.0, "details": {"missing": ["key"], "missing_any": [["字符串", "类型不匹配"], ["整数", "int"]], "present_forbidden": [], "char_count": 18, "length_ok": true, "length_scored": false}}, "error": null} {"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": "qwen/qwen3.7-plus", "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": "2026-06-16T17:00:15.107943+00:00", "latency_ms": 7350, "output": "不支持。\n\n依据：资料明确指出“资料没有提到图表功能”，且列出的报告展示内容（Summary、By Suite、Failure Examples 和 Case Details）中均未包含图表。", "usage": {"prompt_tokens": 114, "completion_tokens": 345, "total_tokens": 459, "cost": 0.00047808, "is_byok": false, "prompt_tokens_details": {"cached_tokens": 0, "cache_write_tokens": 0, "audio_tokens": 0, "video_tokens": 0}, "cost_details": {"upstream_inference_cost": 0.00047808, "upstream_inference_prompt_cost": 3.648e-05, "upstream_inference_completions_cost": 0.0004416}, "completion_tokens_details": {"reasoning_tokens": 302, "image_tokens": 0, "audio_tokens": 0}}, "response_id": "gen-1781629217-KkXK2qVG1bTrWCapBRkM", "finish_reason": "stop", "score": {"passed": true, "score": 1.0, "details": {"missing": [], "missing_any": [], "present_forbidden": [], "char_count": 96, "length_ok": true, "length_scored": false}}, "error": null} {"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": "qwen/qwen3.7-plus", "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": "2026-06-16T17:00:22.459563+00:00", "latency_ms": 12381, "output": "{\"mode\":\"ability\",\"limits\":{\"max_tokens\":4096,\"temperature\":0},\"warnings\":[\"Warning 1\",\"Warning 2\"]}", "usage": {"prompt_tokens": 87, "completion_tokens": 635, "total_tokens": 722, "cost": 0.00084064, "is_byok": false, "prompt_tokens_details": {"cached_tokens": 0, "cache_write_tokens": 0, "audio_tokens": 0, "video_tokens": 0}, "cost_details": {"upstream_inference_cost": 0.00084064, "upstream_inference_prompt_cost": 2.784e-05, "upstream_inference_completions_cost": 0.0008128}, "completion_tokens_details": {"reasoning_tokens": 601, "image_tokens": 0, "audio_tokens": 0}}, "response_id": "gen-1781629224-4Alo7RdQV8p60DpQqGPI", "finish_reason": "stop", "score": {"passed": true, "score": 1.0, "details": {"errors": [], "value": {"mode": "ability", "limits": {"max_tokens": 4096, "temperature": 0}, "warnings": ["Warning 1", "Warning 2"]}}}, "error": null} {"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]*$", "flags": "is"}, "model": "qwen/qwen3.7-plus", "provider": null, "prompt": "只输出 Markdown 表格，必须有 3 列：模式、用途、风险。必须包含 ability mode 和 efficiency mode 两行。不能添加表格外说明。", "parameters": {"temperature": 0, "max_tokens": 16384, "seed": null}, "prompt_hash": "0a28a65a8a714a5c613c2e01f5bf2b72b7b18cdd17059ad9d38cea38a6e1ff4f", "started_at": "2026-06-16T17:00:34.842423+00:00", "latency_ms": 15001, "output": "| 模式 | 用途 | 风险 |\n|---|---|---|\n| ability mode | 处理复杂任务，提供深度分析与详细解答 | 响应延迟较高，计算资源消耗大 |\n| efficiency mode | 快速响应简单任务，提供简洁直接的答案 | 可能缺乏深度，忽略复杂细节或上下文 |", "usage": {"prompt_tokens": 76, "completion_tokens": 786, "total_tokens": 862, "cost": 0.0010304, "is_byok": false, "prompt_tokens_details": {"cached_tokens": 0, "cache_write_tokens": 0, "audio_tokens": 0, "video_tokens": 0}, "cost_details": {"upstream_inference_cost": 0.0010304, "upstream_inference_prompt_cost": 2.432e-05, "upstream_inference_completions_cost": 0.00100608}, "completion_tokens_details": {"reasoning_tokens": 710, "image_tokens": 0, "audio_tokens": 0}}, "response_id": "gen-1781629237-kOCjXFtraekLyWaErXyE", "finish_reason": "stop", "score": {"passed": true, "score": 1.0, "details": {"pattern": "^\\s*\\|\\s*模式\\s*\\|\\s*用途\\s*\\|\\s*风险\\s*\\|\\s*\\n\\s*\\|\\s*-+\\s*\\|\\s*-+\\s*\\|\\s*-+\\s*\\|[\\s\\S]*ability mode[\\s\\S]*efficiency mode[\\s\\S]*$"}}, "error": null} {"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": "qwen/qwen3.7-plus", "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": "2026-06-16T17:00:49.845594+00:00", "latency_ms": 28386, "output": "评估模型为何要同时展示原题、模型输出与错误解释？原题还原真实场景，模型输出呈现实际表现，错误解释则精准剖析逻辑漏洞。三者结合，让评估过程透明、客观且完全可复查。这不仅能帮助开发者精准定位缺陷，更能告别盲目猜测，高效驱动模型迭代升级。", "usage": {"prompt_tokens": 86, "completion_tokens": 1518, "total_tokens": 1604, "cost": 0.00197056, "is_byok": false, "prompt_tokens_details": {"cached_tokens": 0, "cache_write_tokens": 0, "audio_tokens": 0, "video_tokens": 0}, "cost_details": {"upstream_inference_cost": 0.00197056, "upstream_inference_prompt_cost": 2.752e-05, "upstream_inference_completions_cost": 0.00194304}, "completion_tokens_details": {"reasoning_tokens": 1447, "image_tokens": 0, "audio_tokens": 0}}, "response_id": "gen-1781629252-HcPB7LwDyhSprsVlWGTl", "finish_reason": "stop", "score": {"passed": true, "score": 1.0, "details": {"missing": [], "missing_any": [], "present_forbidden": [], "char_count": 116, "length_ok": true, "length_scored": false}}, "error": null} {"case_id": "challenge-reasoning-008", "suite": "reasoning", "case_version": "0.3.0", "tags": ["state-tracking", "branching"], "scorer": "exact", "expected": {"answer": "15", "normalize": true}, "model": "qwen/qwen3.7-plus", "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": "2026-06-16T17:03:31.102001+00:00", "latency_ms": 130547, "output": "```python\ndef solve(line: str) -> list[str]:\n result = []\n current = []\n in_quotes = False\n start_of_field = True\n i = 0\n n = len(line)\n \n while i < n:\n c = line[i]\n if start_of_field and c == '\"':\n in_quotes = True\n start_of_field = False\n elif in_quotes:\n if c == '\"':\n if i + 1 < n and line[i + 1] == '\"':\n current.append('\"')\n i += 1\n else:\n in_quotes = False\n else:\n current.append(c)\n else:\n if c == ',':\n result.append(\"\".join(current))\n current = []\n start_of_field = True\n else:\n current.append(c)\n start_of_field = False\n i += 1\n \n result.append(\"\".join(current))\n return result\n```", "usage": {"prompt_tokens": 108, "completion_tokens": 7194, "total_tokens": 7302, "cost": 0.00924288, "is_byok": false, "prompt_tokens_details": {"cached_tokens": 0, "cache_write_tokens": 0, "audio_tokens": 0, "video_tokens": 0}, "cost_details": {"upstream_inference_cost": 0.00924288, "upstream_inference_prompt_cost": 3.456e-05, "upstream_inference_completions_cost": 0.00920832}, "completion_tokens_details": {"reasoning_tokens": 6967, "image_tokens": 0, "audio_tokens": 0}}, "response_id": "gen-1781629413-B0GjO1ekhhpuZqgKPQQ6", "finish_reason": "stop", "score": {"passed": true, "score": 1.0, "details": {"returncode": 0, "stdout": "", "stderr": ""}}, "error": null} {"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": "qwen/qwen3.7-plus", "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": "2026-06-16T17:05:41.689277+00:00", "latency_ms": 23069, "output": "```python\ndef solve(intervals: list[tuple[int, int]]) -> list[tuple[int, int]]:\n if not intervals:\n return []\n intervals.sort(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": 115, "completion_tokens": 991, "total_tokens": 1106, "cost": 0.00130528, "is_byok": false, "prompt_tokens_details": {"cached_tokens": 0, "cache_write_tokens": 0, "audio_tokens": 0, "video_tokens": 0}, "cost_details": {"upstream_inference_cost": 0.00130528, "upstream_inference_prompt_cost": 3.68e-05, "upstream_inference_completions_cost": 0.00126848}, "completion_tokens_details": {"reasoning_tokens": 859, "image_tokens": 0, "audio_tokens": 0}}, "response_id": "gen-1781629544-aGyuyrHvwmA41FflwG0X", "finish_reason": "stop", "score": {"passed": true, "score": 1.0, "details": {"returncode": 0, "stdout": "", "stderr": ""}}, "error": null} {"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": "qwen/qwen3.7-plus", "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": "2026-06-16T17:06:04.775750+00:00", "latency_ms": 20084, "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 res = []\n for user, ts in events:\n q = user_events[user]\n while q and q[0] <= ts - window:\n q.popleft()\n if len(q) < limit:\n q.append(ts)\n res.append(True)\n else:\n res.append(False)\n return res\n```", "usage": {"prompt_tokens": 124, "completion_tokens": 1027, "total_tokens": 1151, "cost": 0.00135424, "is_byok": false, "prompt_tokens_details": {"cached_tokens": 0, "cache_write_tokens": 0, "audio_tokens": 0, "video_tokens": 0}, "cost_details": {"upstream_inference_cost": 0.00135424, "upstream_inference_prompt_cost": 3.968e-05, "upstream_inference_completions_cost": 0.00131456}, "completion_tokens_details": {"reasoning_tokens": 900, "image_tokens": 0, "audio_tokens": 0}}, "response_id": "gen-1781629567-e0GtRV9TYSjPoW9Rtpaj", "finish_reason": "stop", "score": {"passed": true, "score": 1.0, "details": {"returncode": 0, "stdout": "", "stderr": ""}}, "error": null} {"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": "qwen/qwen3.7-plus", "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": "2026-06-16T17:06:24.899983+00:00", "latency_ms": 21265, "output": "```python\ndef solve(path: str) -> str:\n stack = []\n for part in path.split('/'):\n if part == '' or part == '.':\n continue\n if part == '..':\n if stack:\n stack.pop()\n else:\n stack.append(part)\n return '/' + '/'.join(stack)\n```", "usage": {"prompt_tokens": 106, "completion_tokens": 1133, "total_tokens": 1239, "cost": 0.00148416, "is_byok": false, "prompt_tokens_details": {"cached_tokens": 0, "cache_write_tokens": 0, "audio_tokens": 0, "video_tokens": 0}, "cost_details": {"upstream_inference_cost": 0.00148416, "upstream_inference_prompt_cost": 3.392e-05, "upstream_inference_completions_cost": 0.00145024}, "completion_tokens_details": {"reasoning_tokens": 1049, "image_tokens": 0, "audio_tokens": 0}}, "response_id": "gen-1781629587-8TX0WIk997UkULsvGMAF", "finish_reason": "stop", "score": {"passed": true, "score": 1.0, "details": {"returncode": 0, "stdout": "", "stderr": ""}}, "error": null} {"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": "qwen/qwen3.7-plus", "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": "2026-06-16T17:06:46.204722+00:00", "latency_ms": 32804, "output": "def solve(words: list[str]) -> list[list[str]]:\n groups = {}\n first_idx = {}\n for i, w in enumerate(words):\n k = \"\".join(sorted(w))\n if k not in first_idx:\n first_idx[k] = i\n groups[k] = []\n groups[k].append(w)\n return [groups[k] for k in sorted(first_idx, key=first_idx.get)]", "usage": {"prompt_tokens": 90, "completion_tokens": 1751, "total_tokens": 1841, "cost": 0.00227008, "is_byok": false, "prompt_tokens_details": {"cached_tokens": 0, "cache_write_tokens": 0, "audio_tokens": 0, "video_tokens": 0}, "cost_details": {"upstream_inference_cost": 0.00227008, "upstream_inference_prompt_cost": 2.88e-05, "upstream_inference_completions_cost": 0.00224128}, "completion_tokens_details": {"reasoning_tokens": 1651, "image_tokens": 0, "audio_tokens": 0}}, "response_id": "gen-1781629608-d1g0FI4Rv4l2JbnoSpHT", "finish_reason": "stop", "score": {"passed": true, "score": 1.0, "details": {"returncode": 0, "stdout": "", "stderr": ""}}, "error": null} {"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": "qwen/qwen3.7-plus", "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": "2026-06-16T17:07:19.048485+00:00", "latency_ms": 49225, "output": "def solve(text: str) -> dict:\n lines = text.splitlines()\n if not lines or lines[0] != '---':\n return {}\n \n end_idx = -1\n for i in range(1, len(lines)):\n if lines[i] == '---':\n end_idx = i\n break\n \n if end_idx == -1:\n return {}\n \n result = {}\n for i in range(1, end_idx):\n line = lines[i]\n if ':' not in line:\n continue\n key, value = line.split(':', 1)\n key = key.strip()\n value = value.strip()\n \n if value.startswith('[') and value.endswith(']'):\n inner = value[1:-1].strip()\n if not inner:\n result[key] = []\n else:\n result[key] = [x.strip() for x in inner.split(',')]\n else:\n result[key] = value\n \n return result", "usage": {"prompt_tokens": 123, "completion_tokens": 2665, "total_tokens": 2788, "cost": 0.00345056, "is_byok": false, "prompt_tokens_details": {"cached_tokens": 0, "cache_write_tokens": 0, "audio_tokens": 0, "video_tokens": 0}, "cost_details": {"upstream_inference_cost": 0.00345056, "upstream_inference_prompt_cost": 3.936e-05, "upstream_inference_completions_cost": 0.0034112}, "completion_tokens_details": {"reasoning_tokens": 2435, "image_tokens": 0, "audio_tokens": 0}}, "response_id": "gen-1781629641-7V7h7lWjauAoQJeqL8eg", "finish_reason": "stop", "score": {"passed": true, "score": 1.0, "details": {"returncode": 0, "stdout": "", "stderr": ""}}, "error": null} {"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": "qwen/qwen3.7-plus", "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": "2026-06-16T17:08:08.316933+00:00", "latency_ms": 22281, "output": "```python\ndef solve(items: list[str], k: int) -> list[str]:\n counts = {}\n first_seen = {}\n for i, item in enumerate(items):\n if item not in first_seen:\n first_seen[item] = i\n counts[item] = counts.get(item, 0) + 1\n return sorted(counts.keys(), key=lambda x: (-counts[x], first_seen[x]))[:k]\n```", "usage": {"prompt_tokens": 93, "completion_tokens": 971, "total_tokens": 1064, "cost": 0.00127264, "is_byok": false, "prompt_tokens_details": {"cached_tokens": 0, "cache_write_tokens": 0, "audio_tokens": 0, "video_tokens": 0}, "cost_details": {"upstream_inference_cost": 0.00127264, "upstream_inference_prompt_cost": 2.976e-05, "upstream_inference_completions_cost": 0.00124288}, "completion_tokens_details": {"reasoning_tokens": 869, "image_tokens": 0, "audio_tokens": 0}}, "response_id": "gen-1781629690-N8MwfjgBjhdgpEqoY0un", "finish_reason": "stop", "score": {"passed": true, "score": 1.0, "details": {"returncode": 0, "stdout": "", "stderr": ""}}, "error": null} {"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": "qwen/qwen3.7-plus", "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": "2026-06-16T17:08:30.638223+00:00", "latency_ms": 33043, "output": "```python\nimport copy\n\ndef solve(config: dict, overrides: dict[str, object]) -> dict:\n result = copy.deepcopy(config)\n for path, value in overrides.items():\n keys = path.split('.')\n current = result\n for key in keys[:-1]:\n if not isinstance(current.get(key), dict):\n current[key] = {}\n current = current[key]\n \n last_key = keys[-1]\n if value is None:\n current.pop(last_key, None)\n else:\n current[last_key] = value\n \n return result\n```", "usage": {"prompt_tokens": 101, "completion_tokens": 1781, "total_tokens": 1882, "cost": 0.002312, "is_byok": false, "prompt_tokens_details": {"cached_tokens": 0, "cache_write_tokens": 0, "audio_tokens": 0, "video_tokens": 0}, "cost_details": {"upstream_inference_cost": 0.002312, "upstream_inference_prompt_cost": 3.232e-05, "upstream_inference_completions_cost": 0.00227968}, "completion_tokens_details": {"reasoning_tokens": 1646, "image_tokens": 0, "audio_tokens": 0}}, "response_id": "gen-1781629712-tL1CfW3pgWB5UD6JjPqW", "finish_reason": "stop", "score": {"passed": true, "score": 1.0, "details": {"returncode": 0, "stdout": "", "stderr": ""}}, "error": null} {"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": "qwen/qwen3.7-plus", "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": "2026-06-16T17:09:03.698819+00:00", "latency_ms": 36273, "output": "```python\nfrom urllib.parse import parse_qsl\n\ndef solve(query: str) -> dict[str, list[str]]:\n res = {}\n for k, v in parse_qsl(query, keep_blank_values=True):\n res.setdefault(k, []).append(v)\n return res\n```", "usage": {"prompt_tokens": 111, "completion_tokens": 1965, "total_tokens": 2076, "cost": 0.00255072, "is_byok": false, "prompt_tokens_details": {"cached_tokens": 0, "cache_write_tokens": 0, "audio_tokens": 0, "video_tokens": 0}, "cost_details": {"upstream_inference_cost": 0.00255072, "upstream_inference_prompt_cost": 3.552e-05, "upstream_inference_completions_cost": 0.0025152}, "completion_tokens_details": {"reasoning_tokens": 1897, "image_tokens": 0, "audio_tokens": 0}}, "response_id": "gen-1781629745-DhnBhLPqHA4l6IvxDjaf", "finish_reason": "stop", "score": {"passed": true, "score": 1.0, "details": {"returncode": 0, "stdout": "", "stderr": ""}}, "error": null} {"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": "qwen/qwen3.7-plus", "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": "2026-06-16T17:09:40.019739+00:00", "latency_ms": 24321, "output": "import re\n\ndef solve(html: str) -> bool:\n stack = []\n for m in re.finditer(r'<(/?)([a-z]+)(/?)>', html):\n if m.group(3) == '/':\n continue\n if m.group(1) == '/':\n if not stack or stack[-1] != m.group(2):\n return False\n stack.pop()\n else:\n stack.append(m.group(2))\n return not stack", "usage": {"prompt_tokens": 99, "completion_tokens": 1302, "total_tokens": 1401, "cost": 0.00169824, "is_byok": false, "prompt_tokens_details": {"cached_tokens": 0, "cache_write_tokens": 0, "audio_tokens": 0, "video_tokens": 0}, "cost_details": {"upstream_inference_cost": 0.00169824, "upstream_inference_prompt_cost": 3.168e-05, "upstream_inference_completions_cost": 0.00166656}, "completion_tokens_details": {"reasoning_tokens": 1189, "image_tokens": 0, "audio_tokens": 0}}, "response_id": "gen-1781629782-KTi9YM9IDqmLxzWt1Ne5", "finish_reason": "stop", "score": {"passed": true, "score": 1.0, "details": {"returncode": 0, "stdout": "", "stderr": ""}}, "error": null} {"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": "qwen/qwen3.7-plus", "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": "2026-06-16T17:10:04.384539+00:00", "latency_ms": 44438, "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 = next(((r, c) for r in range(rows) for c in range(cols) if grid[r][c] == 'S'), None)\n if not start:\n return -1\n \n q = deque([(start[0], start[1], 0)])\n visited = {start}\n \n while q:\n r, c, d = q.popleft()\n if grid[r][c] == 'E':\n return d\n for dr, dc in [(-1, 0), (1, 0), (0, -1), (0, 1)]:\n nr, nc = r + dr, c + dc\n if 0 <= nr < rows and 0 <= nc < cols and grid[nr][nc] != '#' and (nr, nc) not in visited:\n visited.add((nr, nc))\n q.append((nr, nc, d + 1))\n return -1\n```", "usage": {"prompt_tokens": 107, "completion_tokens": 2415, "total_tokens": 2522, "cost": 0.00312544, "is_byok": false, "prompt_tokens_details": {"cached_tokens": 0, "cache_write_tokens": 0, "audio_tokens": 0, "video_tokens": 0}, "cost_details": {"upstream_inference_cost": 0.00312544, "upstream_inference_prompt_cost": 3.424e-05, "upstream_inference_completions_cost": 0.0030912}, "completion_tokens_details": {"reasoning_tokens": 2153, "image_tokens": 0, "audio_tokens": 0}}, "response_id": "gen-1781629806-2SKCgfq41uThOCA3H5Yc", "finish_reason": "stop", "score": {"passed": true, "score": 1.0, "details": {"returncode": 0, "stdout": "", "stderr": ""}}, "error": null} {"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": "qwen/qwen3.7-plus", "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": "2026-06-16T17:10:48.864116+00:00", "latency_ms": 27570, "output": "import bisect\n\ndef solve(nums: list[int], k: int) -> list[float]:\n if not nums or k == 0:\n return []\n \n window = sorted(nums[:k])\n res = []\n mid = k // 2\n \n if k % 2 == 1:\n res.append(float(window[mid]))\n else:\n res.append((window[mid - 1] + window[mid]) / 2.0)\n \n for i in range(k, len(nums)):\n out_num = nums[i - k]\n in_num = nums[i]\n \n idx = bisect.bisect_left(window, out_num)\n window.pop(idx)\n bisect.insort(window, in_num)\n \n if k % 2 == 1:\n res.append(float(window[mid]))\n else:\n res.append((window[mid - 1] + window[mid]) / 2.0)\n \n return res", "usage": {"prompt_tokens": 99, "completion_tokens": 1470, "total_tokens": 1569, "cost": 0.00191328, "is_byok": false, "prompt_tokens_details": {"cached_tokens": 0, "cache_write_tokens": 0, "audio_tokens": 0, "video_tokens": 0}, "cost_details": {"upstream_inference_cost": 0.00191328, "upstream_inference_prompt_cost": 3.168e-05, "upstream_inference_completions_cost": 0.0018816}, "completion_tokens_details": {"reasoning_tokens": 1251, "image_tokens": 0, "audio_tokens": 0}}, "response_id": "gen-1781629851-4gPn3WrxPOntNzmVThLA", "finish_reason": "stop", "score": {"passed": true, "score": 1.0, "details": {"returncode": 0, "stdout": "", "stderr": ""}}, "error": null} {"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": "qwen/qwen3.7-plus", "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": "2026-06-16T17:11:16.474445+00:00", "latency_ms": 41267, "output": "```python\ndef solve(versions: list[str]) -> list[str]:\n def get_key(v: str):\n if '-' in v:\n core, label = v.split('-', 1)\n m, n, p = map(int, core.split('.'))\n return (m, n, p, 0, label)\n m, n, p = map(int, v.split('.'))\n return (m, n, p, 1, \"\")\n return sorted(versions, key=get_key)\n```", "usage": {"prompt_tokens": 115, "completion_tokens": 2233, "total_tokens": 2348, "cost": 0.00289504, "is_byok": false, "prompt_tokens_details": {"cached_tokens": 0, "cache_write_tokens": 0, "audio_tokens": 0, "video_tokens": 0}, "cost_details": {"upstream_inference_cost": 0.00289504, "upstream_inference_prompt_cost": 3.68e-05, "upstream_inference_completions_cost": 0.00285824}, "completion_tokens_details": {"reasoning_tokens": 2113, "image_tokens": 0, "audio_tokens": 0}}, "response_id": "gen-1781629878-BGpDTR33trpi8r8KJhDd", "finish_reason": "stop", "score": {"passed": true, "score": 1.0, "details": {"returncode": 0, "stdout": "", "stderr": ""}}, "error": null} {"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": "qwen/qwen3.7-plus", "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": "2026-06-16T17:11:57.780904+00:00", "latency_ms": 41507, "output": "```python\nfrom collections import deque\n\ndef solve(deps: dict[str, list[str]], changed: list[str]) -> list[str]:\n rev_deps = {}\n for pkg, dependencies in deps.items():\n for dep in dependencies:\n rev_deps.setdefault(dep, []).append(pkg)\n \n affected = set(changed)\n queue = deque(changed)\n \n while queue:\n curr = queue.popleft()\n for pkg in rev_deps.get(curr, []):\n if pkg not in affected:\n affected.add(pkg)\n queue.append(pkg)\n \n return sorted(affected)\n```", "usage": {"prompt_tokens": 112, "completion_tokens": 2247, "total_tokens": 2359, "cost": 0.002912, "is_byok": false, "prompt_tokens_details": {"cached_tokens": 0, "cache_write_tokens": 0, "audio_tokens": 0, "video_tokens": 0}, "cost_details": {"upstream_inference_cost": 0.002912, "upstream_inference_prompt_cost": 3.584e-05, "upstream_inference_completions_cost": 0.00287616}, "completion_tokens_details": {"reasoning_tokens": 2107, "image_tokens": 0, "audio_tokens": 0}}, "response_id": "gen-1781629920-maUTEMLh7s94YYqwcKKw", "finish_reason": "stop", "score": {"passed": true, "score": 1.0, "details": {"returncode": 0, "stdout": "", "stderr": ""}}, "error": null} {"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": "qwen/qwen3.7-plus", "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": "2026-06-16T17:12:39.328889+00:00", "latency_ms": 11948, "output": "下一步应该新增 50 道题。不应该立刻跑模型（应先生成审题 HTML，确认后再跑）。", "usage": {"prompt_tokens": 147, "completion_tokens": 598, "total_tokens": 745, "cost": 0.00081248, "is_byok": false, "prompt_tokens_details": {"cached_tokens": 0, "cache_write_tokens": 0, "audio_tokens": 0, "video_tokens": 0}, "cost_details": {"upstream_inference_cost": 0.00081248, "upstream_inference_prompt_cost": 4.704e-05, "upstream_inference_completions_cost": 0.00076544}, "completion_tokens_details": {"reasoning_tokens": 569, "image_tokens": 0, "audio_tokens": 0}}, "response_id": "gen-1781629961-7aYX2wYoShS7EPqepY8D", "finish_reason": "stop", "score": {"passed": true, "score": 1.0, "details": {"missing": [], "missing_any": [], "present_forbidden": [], "char_count": 42, "length_ok": true, "length_scored": false}}, "error": null} {"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": "qwen/qwen3.7-plus", "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": "2026-06-16T17:12:51.278102+00:00", "latency_ms": 3432, "output": "不能。", "usage": {"prompt_tokens": 99, "completion_tokens": 133, "total_tokens": 232, "cost": 0.00020192, "is_byok": false, "prompt_tokens_details": {"cached_tokens": 0, "cache_write_tokens": 0, "audio_tokens": 0, "video_tokens": 0}, "cost_details": {"upstream_inference_cost": 0.00020192, "upstream_inference_prompt_cost": 3.168e-05, "upstream_inference_completions_cost": 0.00017024}, "completion_tokens_details": {"reasoning_tokens": 127, "image_tokens": 0, "audio_tokens": 0}}, "response_id": "gen-1781629973-wKPxqAG49IZLnOwmxwkU", "finish_reason": "stop", "score": {"passed": false, "score": 0.0, "details": {"missing": ["用户满意度"], "missing_any": [["没有包含", "没有提供", "资料没有", "未包含"]], "present_forbidden": [], "char_count": 3, "length_ok": true, "length_scored": false}}, "error": null} {"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": "qwen/qwen3.7-plus", "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": "2026-06-16T17:12:54.711717+00:00", "latency_ms": 5881, "output": "资料更支持选择模型 B。模型 B 的平均延迟最低（4 秒），且没有超时失败，符合低延迟且稳定的要求。", "usage": {"prompt_tokens": 166, "completion_tokens": 273, "total_tokens": 439, "cost": 0.00040256, "is_byok": false, "prompt_tokens_details": {"cached_tokens": 0, "cache_write_tokens": 0, "audio_tokens": 0, "video_tokens": 0}, "cost_details": {"upstream_inference_cost": 0.00040256, "upstream_inference_prompt_cost": 5.312e-05, "upstream_inference_completions_cost": 0.00034944}, "completion_tokens_details": {"reasoning_tokens": 238, "image_tokens": 0, "audio_tokens": 0}}, "response_id": "gen-1781629976-kdO3SCsAUGTVvNdnrJ2u", "finish_reason": "stop", "score": {"passed": true, "score": 1.0, "details": {"missing": [], "missing_any": [], "present_forbidden": [], "char_count": 50, "length_ok": true, "length_scored": false}}, "error": null} {"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": "qwen/qwen3.7-plus", "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": "2026-06-16T17:13:00.593860+00:00", "latency_ms": 14026, "output": "不会。根据资料，max_chars 只作为诊断信息记录，length_scored 为 false，只有缺少 required、required_any 或出现 forbidden 才会导致失败。", "usage": {"prompt_tokens": 119, "completion_tokens": 725, "total_tokens": 844, "cost": 0.00096608, "is_byok": false, "prompt_tokens_details": {"cached_tokens": 0, "cache_write_tokens": 0, "audio_tokens": 0, "video_tokens": 0}, "cost_details": {"upstream_inference_cost": 0.00096608, "upstream_inference_prompt_cost": 3.808e-05, "upstream_inference_completions_cost": 0.000928}, "completion_tokens_details": {"reasoning_tokens": 684, "image_tokens": 0, "audio_tokens": 0}}, "response_id": "gen-1781629982-33AEgy0W9HSVVz3LhLgw", "finish_reason": "stop", "score": {"passed": true, "score": 1.0, "details": {"missing": [], "missing_any": [], "present_forbidden": [], "char_count": 98, "length_ok": true, "length_scored": false}}, "error": null} {"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": "qwen/qwen3.7-plus", "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": "2026-06-16T17:13:14.621416+00:00", "latency_ms": 3956, "output": "应写 46/50。", "usage": {"prompt_tokens": 132, "completion_tokens": 169, "total_tokens": 301, "cost": 0.00025856, "is_byok": false, "prompt_tokens_details": {"cached_tokens": 0, "cache_write_tokens": 0, "audio_tokens": 0, "video_tokens": 0}, "cost_details": {"upstream_inference_cost": 0.00025856, "upstream_inference_prompt_cost": 4.224e-05, "upstream_inference_completions_cost": 0.00021632}, "completion_tokens_details": {"reasoning_tokens": 155, "image_tokens": 0, "audio_tokens": 0}}, "response_id": "gen-1781629996-Vl0Mv6pYWoChxopSCZAw", "finish_reason": "stop", "score": {"passed": true, "score": 1.0, "details": {"missing": [], "missing_any": [], "present_forbidden": [], "char_count": 9, "length_ok": true, "length_scored": false}}, "error": null} {"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": "qwen/qwen3.7-plus", "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": "2026-06-16T17:13:18.579276+00:00", "latency_ms": 4678, "output": "复现评测主要应该依赖的记录字段包括：model、case_id、prompt_hash、parameters、scorer details、output、latency_ms 和 usage。", "usage": {"prompt_tokens": 103, "completion_tokens": 186, "total_tokens": 289, "cost": 0.00027104, "is_byok": false, "prompt_tokens_details": {"cached_tokens": 0, "cache_write_tokens": 0, "audio_tokens": 0, "video_tokens": 0}, "cost_details": {"upstream_inference_cost": 0.00027104, "upstream_inference_prompt_cost": 3.296e-05, "upstream_inference_completions_cost": 0.00023808}, "completion_tokens_details": {"reasoning_tokens": 148, "image_tokens": 0, "audio_tokens": 0}}, "response_id": "gen-1781630000-kt86BXExmbxPSEQ4giM3", "finish_reason": "stop", "score": {"passed": true, "score": 1.0, "details": {"missing": [], "missing_any": [], "present_forbidden": [], "char_count": 96, "length_ok": true, "length_scored": false}}, "error": null} {"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": "qwen/qwen3.7-plus", "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": "2026-06-16T17:13:23.258335+00:00", "latency_ms": 4207, "output": "16384", "usage": {"prompt_tokens": 127, "completion_tokens": 177, "total_tokens": 304, "cost": 0.0002672, "is_byok": false, "prompt_tokens_details": {"cached_tokens": 0, "cache_write_tokens": 0, "audio_tokens": 0, "video_tokens": 0}, "cost_details": {"upstream_inference_cost": 0.0002672, "upstream_inference_prompt_cost": 4.064e-05, "upstream_inference_completions_cost": 0.00022656}, "completion_tokens_details": {"reasoning_tokens": 167, "image_tokens": 0, "audio_tokens": 0}}, "response_id": "gen-1781630005-QcvYPH7vybKltzuBEbNd", "finish_reason": "stop", "score": {"passed": true, "score": 1.0, "details": {"missing": [], "missing_any": [], "present_forbidden": [], "char_count": 5, "length_ok": true, "length_scored": false}}, "error": null} {"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": "qwen/qwen3.7-plus", "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": "2026-06-16T17:13:27.467084+00:00", "latency_ms": 19760, "output": "根据资料，评分规则本身存在缺陷或未能涵盖某些同义表达（如 rag-006 中规则未把 int 类型视作“整数”）的失败更需要人工复查评分规则。\n\ncoding-004 不属于这种情况，因为它的失败是代码运行时错误（ValueError），而非评分规则问题。", "usage": {"prompt_tokens": 126, "completion_tokens": 1036, "total_tokens": 1162, "cost": 0.0013664, "is_byok": false, "prompt_tokens_details": {"cached_tokens": 0, "cache_write_tokens": 0, "audio_tokens": 0, "video_tokens": 0}, "cost_details": {"upstream_inference_cost": 0.0013664, "upstream_inference_prompt_cost": 4.032e-05, "upstream_inference_completions_cost": 0.00132608}, "completion_tokens_details": {"reasoning_tokens": 962, "image_tokens": 0, "audio_tokens": 0}}, "response_id": "gen-1781630009-Jx4GI8QLrow9OSQvWDOg", "finish_reason": "stop", "score": {"passed": true, "score": 1.0, "details": {"missing": [], "missing_any": [], "present_forbidden": [], "char_count": 128, "length_ok": true, "length_scored": false}}, "error": null} {"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": "qwen/qwen3.7-plus", "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": "2026-06-16T17:13:47.228536+00:00", "latency_ms": 8681, "output": "不能。资料明确指出报告没有供应商真实利润率，也没有供应商内部成本。", "usage": {"prompt_tokens": 88, "completion_tokens": 435, "total_tokens": 523, "cost": 0.00058496, "is_byok": false, "prompt_tokens_details": {"cached_tokens": 0, "cache_write_tokens": 0, "audio_tokens": 0, "video_tokens": 0}, "cost_details": {"upstream_inference_cost": 0.00058496, "upstream_inference_prompt_cost": 2.816e-05, "upstream_inference_completions_cost": 0.0005568}, "completion_tokens_details": {"reasoning_tokens": 415, "image_tokens": 0, "audio_tokens": 0}}, "response_id": "gen-1781630029-nMiAXeldUu0RQqC7Sn2i", "finish_reason": "stop", "score": {"passed": true, "score": 1.0, "details": {"missing": [], "missing_any": [], "present_forbidden": [], "char_count": 33, "length_ok": true, "length_scored": false}}, "error": null} {"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": "qwen/qwen3.7-plus", "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": "2026-06-16T17:13:55.910562+00:00", "latency_ms": 23281, "output": "根据资料：\n1. **是否应该展示旧版本结果**：资料未说明是否“应该”，但明确指出之前的版本结果不显示在 index 页。\n2. **当前展示范围**：仅显示最后这版 50 道题的 v0.2-challenge50 结果，且 token limit 统一为 ability16384。", "usage": {"prompt_tokens": 122, "completion_tokens": 1235, "total_tokens": 1357, "cost": 0.00161984, "is_byok": false, "prompt_tokens_details": {"cached_tokens": 0, "cache_write_tokens": 0, "audio_tokens": 0, "video_tokens": 0}, "cost_details": {"upstream_inference_cost": 0.00161984, "upstream_inference_prompt_cost": 3.904e-05, "upstream_inference_completions_cost": 0.0015808}, "completion_tokens_details": {"reasoning_tokens": 1152, "image_tokens": 0, "audio_tokens": 0}}, "response_id": "gen-1781630038-4LmofLEy7dhRtNGccrBp", "finish_reason": "stop", "score": {"passed": true, "score": 1.0, "details": {"missing": [], "missing_any": [], "present_forbidden": [], "char_count": 143, "length_ok": true, "length_scored": false}}, "error": null} {"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": "qwen/qwen3.7-plus", "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": "2026-06-16T17:14:19.193131+00:00", "latency_ms": 12115, "output": "归因为：输出被截断导致没有形成可执行函数，而非算法逻辑错误。", "usage": {"prompt_tokens": 105, "completion_tokens": 608, "total_tokens": 713, "cost": 0.00081184, "is_byok": false, "prompt_tokens_details": {"cached_tokens": 0, "cache_write_tokens": 0, "audio_tokens": 0, "video_tokens": 0}, "cost_details": {"upstream_inference_cost": 0.00081184, "upstream_inference_prompt_cost": 3.36e-05, "upstream_inference_completions_cost": 0.00077824}, "completion_tokens_details": {"reasoning_tokens": 584, "image_tokens": 0, "audio_tokens": 0}}, "response_id": "gen-1781630061-X2cOj5nf1K4fbAhqfSDP", "finish_reason": "stop", "score": {"passed": true, "score": 1.0, "details": {"missing": [], "missing_any": [], "present_forbidden": [], "char_count": 30, "length_ok": true, "length_scored": false}}, "error": null} {"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": "qwen/qwen3.7-plus", "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": "2026-06-16T17:14:31.309719+00:00", "latency_ms": 18131, "output": "根因：Python 的闭包存在延迟绑定（late binding）特性。`lambda` 函数在**调用时**才会去查找变量 `i` 的值，而不是在定义时。循环结束后，`i` 的最终值为 2，因此所有函数调用时都返回 2。\n\n最小修复：利用函数默认参数在定义时求值的特性，将当前循环的 `i` 值绑定到 `lambda` 的局部作用域中。\n\nFIX: funcs.append(lambda i=i: i)", "usage": {"prompt_tokens": 123, "completion_tokens": 941, "total_tokens": 1064, "cost": 0.00124384, "is_byok": false, "prompt_tokens_details": {"cached_tokens": 0, "cache_write_tokens": 0, "audio_tokens": 0, "video_tokens": 0}, "cost_details": {"upstream_inference_cost": 0.00124384, "upstream_inference_prompt_cost": 3.936e-05, "upstream_inference_completions_cost": 0.00120448}, "completion_tokens_details": {"reasoning_tokens": 825, "image_tokens": 0, "audio_tokens": 0}}, "response_id": "gen-1781630073-oWOCmEOMM4lD0SSy97eb", "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} {"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": "qwen/qwen3.7-plus", "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": "2026-06-16T17:14:49.442998+00:00", "latency_ms": 16892, "output": "Bug：`__exit__` 方法返回 `True` 会抑制（吞掉）异常，阻止其向外抛出。\n最小修复：将 `return True` 改为 `return False`（或 `return None`），以允许异常正常传播。\n\nFIX: return False", "usage": {"prompt_tokens": 120, "completion_tokens": 886, "total_tokens": 1006, "cost": 0.00117248, "is_byok": false, "prompt_tokens_details": {"cached_tokens": 0, "cache_write_tokens": 0, "audio_tokens": 0, "video_tokens": 0}, "cost_details": {"upstream_inference_cost": 0.00117248, "upstream_inference_prompt_cost": 3.84e-05, "upstream_inference_completions_cost": 0.00113408}, "completion_tokens_details": {"reasoning_tokens": 821, "image_tokens": 0, "audio_tokens": 0}}, "response_id": "gen-1781630091-6oJFXLADVh2OYEcGK6wJ", "finish_reason": "stop", "score": {"passed": true, "score": 1.0, "details": {"pattern": "FIX:\\s*`?\\s*return(?:\\s+False|\\s+None)?\\s*`?"}}, "error": null} {"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": "qwen/qwen3.7-plus", "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": "2026-06-16T17:15:06.337414+00:00", "latency_ms": 22650, "output": "Bug：`items = []` 被定义为类属性，导致所有 `Bag` 实例共享同一个列表对象。\n\n修复：删除类级别的 `items = []`，并添加 `__init__` 方法将其初始化为实例属性。\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": 114, "completion_tokens": 1162, "total_tokens": 1276, "cost": 0.00152384, "is_byok": false, "prompt_tokens_details": {"cached_tokens": 0, "cache_write_tokens": 0, "audio_tokens": 0, "video_tokens": 0}, "cost_details": {"upstream_inference_cost": 0.00152384, "upstream_inference_prompt_cost": 3.648e-05, "upstream_inference_completions_cost": 0.00148736}, "completion_tokens_details": {"reasoning_tokens": 1060, "image_tokens": 0, "audio_tokens": 0}}, "response_id": "gen-1781630108-IVYClQ9w427he5cGXKtX", "finish_reason": "stop", "score": {"passed": true, "score": 1.0, "details": {"pattern": "FIX:\\s*`?\\s*self\\.items\\s*=\\s*\\[\\]\\s*`?"}}, "error": null} {"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": "qwen/qwen3.7-plus", "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": "2026-06-16T17:15:28.989802+00:00", "latency_ms": 41489, "output": "**Bug 指出**：\n在 Python 中，函数的默认参数只在函数定义时（模块加载时）计算一次。因此 `datetime.utcnow()` 仅在定义时执行一次，导致后续不传 `ts` 调用该函数时，始终返回那个固定的旧时间，而不是每次调用时的当前时间。\n\n**最小修复**：\n将默认参数改为 `None`，并在函数体内部判断，若 `ts` 为 `None` 则赋值为 `datetime.utcnow()`。\n\n修复后的完整代码：\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\nFIX: def mark(ts=None):", "usage": {"prompt_tokens": 110, "completion_tokens": 2247, "total_tokens": 2357, "cost": 0.00291136, "is_byok": false, "prompt_tokens_details": {"cached_tokens": 0, "cache_write_tokens": 0, "audio_tokens": 0, "video_tokens": 0}, "cost_details": {"upstream_inference_cost": 0.00291136, "upstream_inference_prompt_cost": 3.52e-05, "upstream_inference_completions_cost": 0.00287616}, "completion_tokens_details": {"reasoning_tokens": 2086, "image_tokens": 0, "audio_tokens": 0}}, "response_id": "gen-1781630131-u72N1PAtXlr1atzZ28oJ", "finish_reason": "stop", "score": {"passed": true, "score": 1.0, "details": {"pattern": "FIX:\\s*`?\\s*def\\s+mark\\(ts=None\\)\\s*:"}}, "error": null} {"case_id": "challenge-debugging-019", "suite": "debugging", "case_version": "0.3.0", "tags": ["python", "method-call", "debugging"], "scorer": "regex", "expected": {"pattern": "FIX:\\s*`?\\s*return\\s+s\\.strip\\(\\)\\.lower\\(\\)\\s*`?", "flags": "is"}, "model": "qwen/qwen3.7-plus", "provider": null, "prompt": "下面代码期望返回小写字符串，但实际返回的是方法对象。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 normalize(s):\n return s.strip().lower\n```", "parameters": {"temperature": 0, "max_tokens": 16384, "seed": null}, "prompt_hash": "e9d483dafd7852b396cee6b27c6c25c888983460dba83c871f7f9a239dd601ef", "started_at": "2026-06-16T17:16:10.479835+00:00", "latency_ms": 8199, "output": "Bug：`lower` 方法没有被调用，缺少括号 `()`，导致返回的是方法对象本身。\n\n最小修复：在 `lower` 后添加 `()` 以调用该方法。\n\nFIX: return s.strip().lower()", "usage": {"prompt_tokens": 95, "completion_tokens": 312, "total_tokens": 407, "cost": 0.00042976, "is_byok": false, "prompt_tokens_details": {"cached_tokens": 0, "cache_write_tokens": 0, "audio_tokens": 0, "video_tokens": 0}, "cost_details": {"upstream_inference_cost": 0.00042976, "upstream_inference_prompt_cost": 3.04e-05, "upstream_inference_completions_cost": 0.00039936}, "completion_tokens_details": {"reasoning_tokens": 258, "image_tokens": 0, "audio_tokens": 0}}, "response_id": "gen-1781630172-mxYJkNmeMFrQZqXJrGhx", "finish_reason": "stop", "score": {"passed": true, "score": 1.0, "details": {"pattern": "FIX:\\s*`?\\s*return\\s+s\\.strip\\(\\)\\.lower\\(\\)\\s*`?"}}, "error": null} {"case_id": "challenge-if-006", "suite": "instruction_following", "case_version": "0.3.0", "tags": ["json", "nested", "strict"], "scorer": "json_schema_lite", "expected": {"schema": {"type": "object", "required": ["version", "ready", "cases"], "additionalProperties": false, "properties": {"version": {"type": "string", "const": "v0.3"}, "ready": {"type": "boolean", "const": true}, "cases": {"type": "array", "minItems": 2, "maxItems": 2, "items": {"type": "object", "required": ["id", "weight", "active"], "additionalProperties": false, "properties": {"id": {"type": "string", "pattern": "^case-[12]$"}, "weight": {"type": "integer"}, "active": {"type": "boolean"}}}}}}}, "model": "qwen/qwen3.7-plus", "provider": null, "prompt": "只输出 JSON 对象，不要 Markdown。字段 version 必须是 \"v0.3\"；字段 ready 必须是 true；字段 cases 必须是正好两个对象组成的数组。cases 中每个对象只能包含 id、weight、active 三个字段；id 是字符串且匹配 case-1 或 case-2；weight 是整数；active 是布尔值。", "parameters": {"temperature": 0, "max_tokens": 16384, "seed": null}, "prompt_hash": "7aa2b7b97ec6a230b8e08a0949edc3f573a3979a488949456e1106dcbad11695", "started_at": "2026-06-16T17:16:18.680422+00:00", "latency_ms": 17814, "output": "{\"version\":\"v0.3\",\"ready\":true,\"cases\":[{\"id\":\"case-1\",\"weight\":1,\"active\":true},{\"id\":\"case-2\",\"weight\":2,\"active\":false}]}", "usage": {"prompt_tokens": 118, "completion_tokens": 936, "total_tokens": 1054, "cost": 0.00123584, "is_byok": false, "prompt_tokens_details": {"cached_tokens": 0, "cache_write_tokens": 0, "audio_tokens": 0, "video_tokens": 0}, "cost_details": {"upstream_inference_cost": 0.00123584, "upstream_inference_prompt_cost": 3.776e-05, "upstream_inference_completions_cost": 0.00119808}, "completion_tokens_details": {"reasoning_tokens": 888, "image_tokens": 0, "audio_tokens": 0}}, "response_id": "gen-1781630180-XL6k6jb61OO751E0w45t", "finish_reason": "stop", "score": {"passed": true, "score": 1.0, "details": {"errors": [], "value": {"version": "v0.3", "ready": true, "cases": [{"id": "case-1", "weight": 1, "active": true}, {"id": "case-2", "weight": 2, "active": false}]}}}, "error": null} {"case_id": "challenge-zh-writing-004", "suite": "chinese_writing", "case_version": "0.3.0", "tags": ["wechat", "summary"], "scorer": "text_rules", "expected": {"required": ["推理题", "边界条件", "人工复查"], "forbidden": ["排行榜"], "max_chars": 160}, "model": "qwen/qwen3.7-plus", "provider": null, "prompt": "写一段不超过 160 个汉字的公众号小结，主题是“为什么 v0.3 要扩到 100 题”。必须包含“推理题”“边界条件”“人工复查”，不能出现“排行榜”。", "parameters": {"temperature": 0.2, "max_tokens": 16384, "seed": null}, "prompt_hash": "2e406239f22e7454c39c9cc970d8388b92c83522c1edacf056286806f5933790", "started_at": "2026-06-16T17:16:36.495790+00:00", "latency_ms": 18884, "output": "v0.3 版本将题库扩充至 100 题，旨在更全面评估模型能力。新增的推理题能深度考察逻辑推演水平；同时，扩充题库有助于覆盖更多复杂的边界条件，防止模型靠简单模式匹配得分。每一道新题均经过严格的人工复查，确保题目质量与答案准确。这不仅提升了测试严谨性，也为开发者提供了更可靠的优化方向。", "usage": {"prompt_tokens": 91, "completion_tokens": 1000, "total_tokens": 1091, "cost": 0.00130912, "is_byok": false, "prompt_tokens_details": {"cached_tokens": 0, "cache_write_tokens": 0, "audio_tokens": 0, "video_tokens": 0}, "cost_details": {"upstream_inference_cost": 0.00130912, "upstream_inference_prompt_cost": 2.912e-05, "upstream_inference_completions_cost": 0.00128}, "completion_tokens_details": {"reasoning_tokens": 910, "image_tokens": 0, "audio_tokens": 0}}, "response_id": "gen-1781630198-9GF60xTZn3rmXs7gkBgU", "finish_reason": "stop", "score": {"passed": true, "score": 1.0, "details": {"missing": [], "missing_any": [], "present_forbidden": [], "char_count": 143, "length_ok": true, "length_scored": false}}, "error": null}