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智能体基准测试真的测了它们声称测的东西吗?

Do Agent Benchmarks Do What They Say?

AI 导读

一项审计发现,四个基准测试中 34 个会改变状态的工具里有 7 个存在缺陷,另有一项评测器属性问题,且缺陷在每次重跑时都会影响分数。检查器在注入缺陷时 25 次标记无误报,33 个漏检中有 29 个是条款覆盖了缺陷但探针未能触发。最明显的案例是一个临床基准:其工具在接口未披露的“无写入”设计下仍告知智能体写入已执行,评测器据此判定成功,实际记录的是请求是否携带预期载荷,而非记录是否真的改变。

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Abstract:Tool-using agents are entering settings where a wrong action carries real cost, and the benchmarks certifying them grade what each simulated tool call reports having done, assuming the tool did what its interface advertises. The audit taxonomies we survey publish no category for that assumption, and a defect beneath a score is present on every rerun. We treat a tool's advertised surfaces as an executable contract, check the implementation against it, and trace each score's provenance through the task files and evaluator code to the verdicts that derive from state a defective tool should have written. Across 34 audited mutating tools in four benchmarks we confirm seven tool defects and one evaluator property at pinned commits. On injected defects the checker raised no false positive in 25 flags, flagged 2 of 5 negative controls, and missed most: in 29 of 33 scored misses a clause covered the defect but no probe revealed it. The checker's own static half, run alone, flags 14 of 17 confirmed sites, so on these findings the dynamic half confirms and traces rather than discovers. Twelve further AgentDojo tools, with six held-out tools and the seven audited first, complete its 25-tool mutating surface, on which at least 5 tools diverge from their advertised surface as our contracts read it, a rate for AgentDojo alone. No gold trajectory reaches either tau2-bench defect; on 1,120 paths built to isolate the telecom defect, a number fixed by construction, the evaluator rewards a refuel of a suspended line and fails the repaired tool. The clearest case is a clinical benchmark whose tool tells the agent each write executed under a documented no-write design its interface does not disclose; its grader takes that message as evidence, so its action success rate records whether a request carried the expected payload, not whether any record changed.
Comments: 10 pages. Submitted to IEEE BigData 2026, Intelligent Data Mining special session. Code, contracts and data: this https URL
Subjects: Software Engineering (cs.SE); Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
ACM classes: D.2.5; I.2.6
Cite as: arXiv:2609.37315 [cs.SE]
  (or arXiv:2609.37315v1 [cs.SE] for this version)
  https://doi.org/10.48550/arXiv.2609.37315

arXiv-issued DOI via DataCite (pending registration)

Submission history

From: Rohith Reddy Bellibatlu [view email]
[v1] Tue, 29 Sep 2026 11:50:55 UTC (34 KB)

来源:Hacker News · arxiv.org