mlsanityData to 14 Oct 2026

Do Agent Benchmarks Do What They Say? An Executable-Contract Audit of Tool-Using Agent Environments

arXiv 2609.37315 ↗First posted 29 Sep 2026cs.SE cs.AI cs.LGcited by 2

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Abstract (arXiv)

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.

Key facts. Cited by 2 papers in the map (data through 14 Oct 2026). Builds on 30 of its 49 references that are in the map. First posted on arXiv 29 Sep 2026.

Builds on

30 of 49 references.

References · 30

  1. 2023WebArena: A Realistic Web Environment for Building Autonomous Agentscited by 2,006
  2. 2024τ-bench: A Benchmark for Tool-Agent-User Interaction in Real-World Domainscited by 1,402
  3. 2024OSWorld: Benchmarking Multimodal Agents for Open-Ended Tasks in Real Computer Environmentscited by 1,195
  4. 2025τ2-Bench: Evaluating Conversational Agents in a Dual-Control Environmentcited by 627
  5. 2024AndroidWorld: A Dynamic Benchmarking Environment for Autonomous Agentscited by 468
  6. 2024AppWorld: A Controllable World of Apps and People for Benchmarking Interactive Coding Agentscited by 401
  7. 2024AI Agents That Mattercited by 219
  8. 2025Measuring what Matters: Construct Validity in Large Language Model Benchmarkscited by 119
  9. 2024BetterBench: Assessing AI Benchmarks, Uncovering Issues, and Establishing Best Practicescited by 114
  10. 2025Establishing Best Practices for Building Rigorous Agentic Benchmarkscited by 102
  11. 2025UTBoost: Rigorous Evaluation of Coding Agents on SWE-Benchcited by 45
  12. 2026Agent Behavioral Contracts: Formal Specification and Runtime Enforcement for Reliable Autonomous AI Agentscited by 19
18 more
  1. 2025ToolFuzz -- Automated Agent Tool Testingcited by 18
  2. 2024Defining and Detecting the Defects of the Large Language Model-based Autonomous Agentscited by 16
  3. 2026ToolGate: Contract-Grounded and Verified Tool Execution for LLMscited by 15
  4. 2026BenchGuard: Who Guards the Benchmarks? Automated Auditing of LLM Agent Benchmarkscited by 15
  5. 2026From Confident Closing to Silent Failure: Characterizing False Success in LLM Agentscited by 15
  6. 2026Agent-Diff: Benchmarking LLM Agents on Enterprise API Tasks via Code Execution with State-Diff-Based Evaluationcited by 13
  7. 2026Characterizing Faults in Agentic AI: A Taxonomy of Types, Symptoms, and Root Causescited by 12
  8. 2026Automated Benchmark Auditing for AI Agents and Large Language Modelscited by 12
  9. 2026LiveClawBench: Benchmarking LLM Agents on Complex, Real-World Assistant Taskscited by 10
  10. 2026Auditing Automated Evaluation, Error Propagation, and Runtime Mitigation in Tool-Using Language Agentscited by 10
  11. 2026JudgeSense: A Benchmark for Prompt Sensitivity in LLM-as-a-Judge Systemscited by 10
  12. 2026Benchmarking the Benchmarks: A Validity Audit of Tool-Calling Evaluationcited by 8
  13. 2026Can Agent Benchmarks Support Their Scores? Evidence-Supported Bounds for Interactive-Agent Evaluationcited by 5
  14. 2026Contract2Tool: Learning Preconditions and Effects for Reliable Tool-Augmented LLM Agentscited by 5
  15. 2026MM-ToolSandBox: A Unified Framework for Evaluating Visual Tool-Calling Agentscited by 3
  16. 2026Who Tests the Testers? Systematic Enumeration and Coverage Audit of LLM Agent Tool Call Safetycited by 2
  17. 2026ContractBench: Can LLM Agents Preserve Observation Contracts?cited by 2
  18. 2026Systematic API Testing Through Model Checking and Executable Contractscited by 1

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