mlsanityData to 14 Oct 2026

Beyond Task Completion: A Verification-vs.-Conformance Gap in Tool-Evolving Agents

arXiv 2604.00392 ↗First posted 1 Apr 2026cs.SE cs.AIcited by 1

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Abstract (arXiv)

Agents that synthesize their own tools ship a second artifact alongside each answer: a software library that future tasks reuse, compose, and depend on. Task completion (TC) certifies the answer; it does not certify the library. On a Claude Haiku 4.5 pilot, we patch the harness to preserve per-tool source and replay every synthesised tool against a held-out conformance suite. Across 222 preserved tools and three protocols, 96.8% record per-tool correctness C=0.00: two protocols silent-rot at 100%, one at 91.7%. Hand-written reference implementations score C=1.00 on all 16 capability suites. The synthesised tools execute cleanly and the in-session verifier raises no flag; on held-out inputs they simply return wrong answers. This is the verification-vs.-conformance gap a pass-rate benchmark cannot see. EvolveTool-Bench makes that gap measurable. Sessions are structured into seed, gap, variant, composition, regression, and adversarial roles; runs emit per-tool manifests, verified-subset TC, correct-vs.-incorrect reuse, and audit traces for post-deployment monitoring. In the Haiku pilot over five protocols (3 seeds, 8 sessions, 51 verified decisions per pass), TC alone does not separate the protocols; the audit layer does. One pre-specified contrast shows the adapted decision-split synthesis protocol underperforming one-shot synthesis (-7.1 pp, 95% CI [-13.9,-0.1]; not significant after BH correction) -- a hypothesis-generating signal, not a confirmed effect. We release the harness, per-tool source preservation, conformance replay, and reproducibility manifests. An audit schema of this shape -- verifier coverage, reuse decomposition, held-out conformance -- is the minimum a deployed tool-evolving agent should emit between releases.

Key facts. Cited by 1 paper in the map (data through 14 Oct 2026). Builds on 8 of its 19 references that are in the map. First posted on arXiv 1 Apr 2026.

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8 of 19 references.

References · 8

  1. 2023ToolLLM: Facilitating Large Language Models to Master 16000+ Real-world APIscited by 1,947
  2. 2026SkillsBench: Benchmarking How Well Agent Skills Work Across Diverse Taskscited by 315
  3. 2023Large Language Models as Tool Makerscited by 303
  4. 2026EvoSkill: Automated Skill Discovery for Multi-Agent Systemscited by 107
  5. 2023CREATOR: Tool Creation for Disentangling Abstract and Concrete Reasoning of Large Language Modelscited by 93
  6. 2025Vending-Bench: A Benchmark for Long-Term Coherence of Autonomous Agentscited by 80
  7. 2025LLM Agents Making Agent Toolscited by 48
  8. 2024NoFunEval: Funny How Code LMs Falter on Requirements Beyond Functional Correctnesscited by 26

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