AI在千种工具中规划任务，一遇故障就崩
现在的AI助手能调用大量工具完成任务，但一旦工具失效或找不到，它们就手足无措。新基准PlanBench-XL模拟了真实场景：AI要在1665种工具中自己找、自己试，还要应对工具突然失灵或返回错误。测试发现，最强模型GPT-5.4在无故障时准确率51.9%，但遇到最严重的工具故障时，准确率暴跌至11.36%。问题在于，AI很难从失败中恢复，尤其是当错误没有明确信号时。这提醒我们，别指望AI在复杂工具环境中能可靠地自主规划——它不是你明天能用上的。
📄 原文摘要(英文)
LLM agents increasingly operate in large tool ecosystems, where real-world tasks require discovering relevant tools, inferring implicit sub-goals, and adapting to dynamic environments over long horizons. However, existing benchmarks rarely evaluate planning under retrieval-limited tool visibility. To address this gap, we introduce PlanBench-XL, an interactive benchmark of 327 retail tasks over 1,665 tools that tests whether agents can iteratively retrieve usable tools, invoke them to uncover intermediate evidence for subsequent calls toward the final goal. PlanBench-XL further features an optional blocking mechanism that simulates real-world unpredictability through missing, failing, or distracting tool functions, forcing agents to detect disrupted paths and adapt at runtime. Experiments on ten leading LLMs show that massive-tool planning remains challenging: while GPT-5.4 achieves 51.90% accuracy in block-free settings, it collapses to 11.36% under the most severe blocking condition. Further analysis shows that agents are especially vulnerable when failures lack explicit error signals or when recovery requires longer alternative tool-use paths. These results establish PlanBench-XL as a testbed for diagnosing agentic planning failures and highlight the need for robust adaptive planning in long-horizon tasks with large, imperfect tool environments.