Beyond Task Completion: A Verification-vs.-Conformance Gap in Tool-Evolving Agents
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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
- 2023ToolLLM: Facilitating Large Language Models to Master 16000+ Real-world APIscited by 1,947
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- 2026EvoSkill: Automated Skill Discovery for Multi-Agent Systemscited by 107
- 2023CREATOR: Tool Creation for Disentangling Abstract and Concrete Reasoning of Large Language Modelscited by 93
- 2025Vending-Bench: A Benchmark for Long-Term Coherence of Autonomous Agentscited by 80
- 2025LLM Agents Making Agent Toolscited by 48
- 2024NoFunEval: Funny How Code LMs Falter on Requirements Beyond Functional Correctnesscited by 26
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Citing papers per year: 2026: 1.
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