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REVIEW 2 major objections 6 minor 14 references

Task completion certifies an agent’s answer, not the tool library it leaves behind—and almost all synthesised tools fail held-out tests for their own capability.

Reviewed by Pith at T0; open to challenge. T0 means a machine referee read the full paper against a public rubric. the ladder, T0–T4 →

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2026-07-13 15:08 UTC pith:DQDNX7CU

load-bearing objection Clear in-regime measurement of a verification-vs-conformance gap that pass-rate agent benchmarks miss; pilot is small but the evidence is clean and the release is usable. the 2 major comments →

arxiv 2604.00392 v2 pith:DQDNX7CU submitted 2026-04-01 cs.SE cs.AI

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

Alibek Kaliyev , Artem Maryanskyy This is my paper
classification cs.SE cs.AI
keywords agentic AItool usetool synthesisevaluationbenchmarkauditabilityverificationconformance
verification ladder T0 review T1 audit T2 compute T3 formal T4 reserved

The pith

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

When an agent writes its own tools, it ships a second product: a software library that later tasks reuse and depend on. A task-completion score only says whether the current answer was accepted; it says nothing about whether that library is correct, stable, or safe to call again. This paper shows the gap is real and large. On a controlled pilot that preserved every synthesised tool and replayed it against held-out tests for the capability it was built for, 96.8% of 222 tools scored zero correctness—while the same suites are passed perfectly by hand-written references, the tools run without errors, and the in-session verifier never flags them. The authors release EvolveTool-Bench, a session design and audit schema that separates creation, reuse, composition, regression, and conformance so that gap becomes measurable and, they argue, the minimum a deployed tool-evolving agent should emit between releases.

Core claim

There is a systematic verification-versus-conformance gap in tool-evolving agents: tools can satisfy the in-session task verifier, execute cleanly, and support non-trivial task-completion rates while returning wrong answers on every held-out input for the capability they were synthesised for. In the Haiku pilot, 215 of 222 preserved tools recorded per-tool correctness C=0.00 on those suites; hand-written reference implementations scored C=1.00 on all 16 capability suites.

What carries the argument

EvolveTool-Bench: sessions structured into seed, gap, variant, composition, regression, and adversarial roles, plus an audit layer that preserves per-tool source, decomposes correct versus incorrect reuse, and replays tools against capability-aligned held-out conformance suites.

Load-bearing premise

The held-out conformance suites (and the hand-written references that pass them perfectly) correctly define what each synthesised tool was supposed to implement; if those suites are misaligned with the intended contract, a zero score is not silent rot.

What would settle it

Replay the same preserved tool sources on the same held-out capability suites with a stronger model or native tool-creation loop and measure whether the fraction of tools with C=0.00 collapses toward the reference baseline of C=1.00; if it does not, the gap claim strengthens; if it does, the Haiku-scale silent-rot rate does not generalise.

Watch this falsifier — get emailed when new claim-graph text bears on it.

If this is right

  • Pass-rate benchmarks for tool-evolving agents cannot certify the library; they must report held-out conformance and reuse quality separately.
  • Deployed agents that write tools should re-emit verifier coverage, correct-versus-incorrect reuse, and capability-aligned conformance between releases.
  • Three concrete release-gate signals become available: silent rot (high TC, falling held-out conformance), reuse trap (rising raw reuse, falling correct-reuse rate), and regression delta (regress-role failure after a prior gap-role pass).
  • Verifier coverage itself is a first-class property of any such benchmark; unverified tasks must not inflate completion claims.
  • Protocol comparisons that look identical on task completion can still separate on the audit layer.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

  • If the gap holds at larger models, tool-evolving agents may need promotion gates that treat a new function as untrusted until it passes capability-aligned hidden tests, not just the task that created it.
  • The same audit schema could serve as a cross-model regression test: re-running an unchanged harness after a model upgrade would reveal whether library quality tracked the model or silently degraded.
  • Incorrect reuse as a first-class metric suggests library-management policies (retirement, deduplication, versioning) may matter as much as better synthesis prompts.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit.

Referee Report

2 major / 6 minor

Summary. The paper argues that agents which synthesize tools leave behind a reusable software library that task-completion (TC) scores do not certify. On a Claude Haiku 4.5 verified-subset pilot, the authors preserve per-tool source and replay 222 synthesised tools from three tool-creating protocols against held-out capability-aligned conformance suites: 96.8% (215/222) record per-tool correctness C=0.00, while hand-written reference implementations score C=1.00 on all 16 suites. The tools execute cleanly and in-session verifiers raise no flag. EvolveTool-Bench structures sessions into seed/gap/variant/compose/regress/adversarial roles and emits audit traces (verifier coverage, correct-vs-incorrect reuse, held-out conformance). Over five protocols (3 seeds, 8 sessions, 51 verified decisions), TC alone does not separate protocols; one pre-registered contrast (adapted CREATOR-style vs one-shot) is nominally negative but does not survive BH correction. The authors release harness, source preservation, conformance replay, and manifests, and propose the audit schema as a minimum post-deployment monitor.

Significance. If the verification-vs-conformance gap holds under the stated regime, the paper identifies a measurement blind spot that pass-rate agent benchmarks systematically miss: a library can satisfy in-session verifiers while failing the capability contracts later tasks will depend on. That is a concrete, actionable contribution for agent evaluation and for release monitoring of tool-evolving systems. Strengths that raise the work above a pure negative result include: (i) direct artifact-level measurement (222 preserved tools, C=0.00 vs C=1.00 references), (ii) pre-registration of contrasts, bootstrap parameters, BH correction, and verified-subset definition, (iii) strict separation of verified vs unverified tasks so TC is not inflated by plausibility heuristics, (iv) correct/incorrect reuse decomposition, and (v) release of harness, per-tool source preservation, conformance replay, and reproducibility manifests. The protocol-comparison result is appropriately framed as hypothesis-generating rather than confirmatory. The work is scoped as an in-regime pilot, not a universal law.

major comments (2)
  1. [§5.3 / conformance design] §5.3 and the conformance-replay design: the central C=0.00 claim is load-bearing on the premise that the 16 held-out capability-aligned suites (and the hand-written references that pass them at C=1.00) correctly define the contracts the synthesised tools were asked to implement. The paper shows tools execute cleanly, in-session verifiers are silent, session TC is 0.31–0.34, and Supplement D gives a self-validating tool that fails every held-out input—strong supporting evidence. What is still under-specified in the main text is the operational mapping: how each synthesised tool is assigned to a capability, how suite inputs relate to the task prompts the agent actually saw, and whether suites test the same contract the agent was instructed to implement or a broader one. A short subsection (or appendix table) listing, for each of the 16 capabilities, the prompt-level contract, suite size/ad
  2. [Table 2, §3.2, §5] Table 2 and §3.2: compose (50%) and adversarial (56.2%) verifier coverage are partial, while the diagnostic framework leans on those roles for composition and edge-case claims. The gap result in §5.3 does not depend on full role coverage, and the authors correctly exclude unverified tasks from TC. However, claims that “the audit layer does” separate protocols (§1, §5) currently rest mainly on reuse decomposition and the conformance probe, not on fully powered compose/adversarial role isolation. The manuscript should either (a) restrict audit-layer separation claims to the signals that are fully measured in the Haiku pilot (TC, tools created, correct/incorrect reuse, C), or (b) report per-role pass rates where coverage allows and explicitly mark compose/adversarial as incomplete. This is a scoping fix, not a redesign.
minor comments (6)
  1. [§5.3 / Table 5] Table 5 is cited for per-protocol silent-rot rates (100% / 100% / 91.7%) but is not fully reproduced in the main body in the submitted text; ensure the table appears with n per protocol and the exact C definition.
  2. [Figure 1, §6] Figure 1 (audit-trace schema) is described as redacted; a non-redacted example field list (or a synthetic filled record) would make the post-deployment monitoring claim in §6 concrete for readers.
  3. [§4 / Supplement C] The fairness ledger (Supplement Table 7) is important for adapted protocols; a one-paragraph pointer in the main §4 would help readers who skip the supplement understand that underperformance is not a verdict on native CREATOR/ToolMaker/EvoSkill.
  4. [Abstract, §5.3] Notation: define C formally once (e.g., fraction of held-out suite cases passed) before first use in the abstract/intro, and state whether C is micro-averaged over cases or a binary all-pass indicator when reporting C=0.00 rates.
  5. [§6] §6 monitoring signals (silent rot, reuse trap, regression delta) are useful; a single sentence on recommended thresholds or on how a frozen task slice is chosen would make the CI-gate proposal more operational without expanding scope.
  6. [Abstract, §4] Minor prose: “unvalidated one-shot synthesis” vs “One-Shot” naming should be consistent across abstract, §4, and Table 4; a few garbled placeholder strings appear in the compiled text and should be cleaned before camera-ready.

Circularity Check

0 steps flagged

No circular derivation: the verification-vs.-conformance gap is an empirical measurement against held-out suites, not a result forced by definition or self-citation.

full rationale

This is an empirical systems/benchmark paper, not a first-principles derivation. The central claim (96.8% of 222 preserved tools score C=0.00 on held-out capability suites while session TC is 0.31–0.34 and in-session verifiers raise no flag) is measured by replaying synthesised tool source against suites the agent never saw; hand-written references score C=1.00 on the same suites. TC and C are deliberately separated metrics, so neither is defined in terms of the other. Protocol contrasts are pre-registered paired bootstrap comparisons on verified-subset decisions, not fitted parameters renamed as predictions. Related-work citations (CREATOR, ToolMaker, EvoSkill, etc.) are external prior systems used only as protocol inspirations with explicit adaptation notes; there is no load-bearing self-citation, uniqueness theorem imported from the authors, or ansatz smuggled via prior work. That the authors designed both harness and suites is ordinary experimental design, not circularity under the enumerated patterns. The result is falsifiable by re-running the released harness on other models or by inspecting the released per-tool sources against the released suites.

Axiom & Free-Parameter Ledger

4 free parameters · 5 axioms · 3 invented entities

The central gap claim rests on experimental design choices and domain assumptions about what counts as a capability contract, not on fitted physical constants or new particles. Free parameters are the usual pilot knobs (seeds, bootstrap B, temperature). Axioms are standard software-engineering and evaluation assumptions plus the paper's verification policy. Invented entities are the benchmark, the C metric, and the audit schema itself; they have independent handles via the released harness and the hand-written reference implementations.

free parameters (4)
  • bootstrap_B = 10000
    Number of bootstrap resamples (B=10,000) used for percentile CIs on protocol contrasts; chosen by pre-registration, not fitted to produce significance.
  • temperature = 1.0
    Decoding temperature fixed at 1.0 for all Haiku runs; a free experimental choice that affects synthesis diversity.
  • integer_seeds = 3 seeds
    Three integer seeds used for the verified-subset pilot; small sample that limits power and is acknowledged as such.
  • sandbox_wall_clock_limit = 5s
    Five-second in-process execution limit for synthesised tools; an operational cutoff that could truncate long-running but correct tools.
axioms (5)
  • ad hoc to paper A task contributes to strict TC only if it has an explicit expected output or a custom deterministic verifier; unverified tasks are never credited.
    Stated verification policy (§3.2); defines the 51-decision verified subset and excludes the API-orchestration session from main TC.
  • domain assumption Held-out capability-aligned conformance suites correctly define the intended contracts of synthesised tools.
    Load-bearing for interpreting C=0.00 as silent rot rather than suite mismatch; supported by hand-written references scoring C=1.00 but still an assumption about suite completeness.
  • ad hoc to paper Reuse is correct when a prior tool is used and the task passes, incorrect when a prior tool is used and the task fails.
    Operational definition in §3.3; deliberately avoids calling the rate 'precision' because no false-positive denominator is defined.
  • domain assumption Library accumulation is per-session; harness state resets between sessions.
    Experimental setup (§4); means the pilot does not measure cross-session library evolution over long horizons.
  • standard math Standard bootstrap percentile CIs and Benjamini–Hochberg correction apply to the three pre-specified protocol contrasts.
    Pre-registered statistical procedure; conventional and not paper-specific.
invented entities (3)
  • EvolveTool-Bench independent evidence
    purpose: Structured benchmark of 99 tasks (51 verified) with seed/gap/variant/compose/regress/adversarial roles for diagnosing tool-library evolution.
    New evaluation instrument introduced by the paper; independent evidence via released harness and role tables.
  • per-tool correctness C independent evidence
    purpose: Fraction of held-out conformance-suite inputs a synthesised tool answers correctly for its claimed capability.
    Primary metric that surfaces the gap; defined operationally by replay against suites that references pass at 1.00.
  • audit schema (silent rot / reuse trap / regression delta) independent evidence
    purpose: Post-deployment monitoring signals derived from verifier coverage, reuse decomposition, and held-out conformance.
    Proposed minimum emission for deployed tool-evolving agents (§6); falsifiable as thresholded release gates once the harness is public.

pith-pipeline@v1.1.0-grok45 · 14701 in / 3811 out tokens · 36885 ms · 2026-07-13T15:08:34.904559+00:00 · methodology

0 comments
read the original abstract

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.

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Reference graph

Works this paper leans on

14 extracted references · 8 linked inside Pith

  1. [1]

    [Alzubi et al.(2026)] Salaheddin Alzubi, Noah Provenzano, Jaydon Bingham, Weiyuan Chen, and Tu Vu

    2026

  2. [2]

    [Backlund and Petersson(2025)] Axel Backlund and Lukas Petersson

    EvoSkill: Automated Skill Discovery for Multi-Agent Systems.arXiv preprint arXiv:2603.02766(2026). [Backlund and Petersson(2025)] Axel Backlund and Lukas Petersson

    Pith/arXiv arXiv 2026

  3. [3]

    [Cai et al.(2023)] Tianle Cai, Xuezhi Wang, Tengyu Ma, Xinyun Chen, and Denny Zhou

    Vending- Bench: A Benchmark for Long-Term Coherence of Autonomous Agents.arXiv preprint arXiv:2502.15840(2025). [Cai et al.(2023)] Tianle Cai, Xuezhi Wang, Tengyu Ma, Xinyun Chen, and Denny Zhou

    Pith/arXiv arXiv 2025

  4. [4]

    [Ellis et al.(2021)] Kevin Ellis, Catherine Wong, Maxwell Nye, Mathias Sablé-Meyer, Lucas Morales, Luke Hewitt, Luc Cary, Armando Solar-Lezama, and Joshua B

    Large Language Models as Tool Makers.arXiv preprint arXiv:2305.17126(2023). [Ellis et al.(2021)] Kevin Ellis, Catherine Wong, Maxwell Nye, Mathias Sablé-Meyer, Lucas Morales, Luke Hewitt, Luc Cary, Armando Solar-Lezama, and Joshua B. Tenenbaum

    Pith/arXiv arXiv 2023

  5. [5]

    InProceedings of the 42nd ACM SIGPLAN International Conference on Programming Language Design and Implementation

    DreamCoder: Bootstrapping Inductive Program Synthesis with Wake-Sleep Library Learning. InProceedings of the 42nd ACM SIGPLAN International Conference on Programming Language Design and Implementation. [Li et al.(2026)] Xiangyi Li, Wenbo Chen, Yimin Liu, Shenghan Zheng, Xiaokun Chen, et al

    2026

  6. [6]

    [Liu et al.(2024)] Xiao Liu, Hao Yu, Hanchen Zhang, Yifan Xu, Xuanyu Lei, Hanyu Lai, Yu Gu, Hangliang Ding, Kaiwen Men, Kejuan Yang, et al

    SkillsBench: Benchmarking How Well Agent Skills Work Across Diverse Tasks.arXiv preprint arXiv:2602.12670(2026). [Liu et al.(2024)] Xiao Liu, Hao Yu, Hanchen Zhang, Yifan Xu, Xuanyu Lei, Hanyu Lai, Yu Gu, Hangliang Ding, Kaiwen Men, Kejuan Yang, et al

    Pith/arXiv arXiv 2026

  7. [7]

    InInternational Conference on Learning Representa- tions

    AgentBench: Evaluating LLMs as Agents. InInternational Conference on Learning Representa- tions. [Qian et al.(2023)] Cheng Qian, Chi Han, Yi R. Fung, Yujia Qin, Zhiyuan Liu, and Heng Ji

    2023

  8. [8]

    [Qin et al.(2023)] Yujia Qin, Shihao Liang, Yining Ye, Kunlun Zhu, Lan Yan, Yaxi Lu, Yankai Lin, et al

    2023

  9. [9]

    [Singhal et al.(2024)] Manav Singhal, Tushar Aggarwal, Abhijeet Awasthi, Nagarajan Natarajan, and Aditya Kanade

    ToolLLM: Facilitating Large Language Models to Master 16000+ Real-world APIs.arXiv preprint arXiv:2307.16789(2023). [Singhal et al.(2024)] Manav Singhal, Tushar Aggarwal, Abhijeet Awasthi, Nagarajan Natarajan, and Aditya Kanade

    Pith/arXiv arXiv 2023

  10. [10]

    [Wang et al.(2023)] Guanzhi Wang, Yuqi Xie, Yunfan Jiang, Ajay Mandlekar, Chaowei Xiao, Yuke Zhu, Linxi Fan, and Anima Anandkumar

    NoFunEval: Funny How Code LMs Falter on Requirements Beyond Functional Correctness.arXiv preprint arXiv:2401.15963 (2024). [Wang et al.(2023)] Guanzhi Wang, Yuqi Xie, Yunfan Jiang, Ajay Mandlekar, Chaowei Xiao, Yuke Zhu, Linxi Fan, and Anima Anandkumar

    Pith/arXiv arXiv 2024

  11. [11]

    [Wölflein et al.(2025)] Georg Wölflein, Dyke Ferber, Daniel Truhn, Ognjen Arand- jelović, and Jakob Nikolas Kather

    Voyager: An Open- Ended Embodied Agent with Large Language Models.Advances in Neural Infor- mation Processing Systems(2023). [Wölflein et al.(2025)] Georg Wölflein, Dyke Ferber, Daniel Truhn, Ognjen Arand- jelović, and Jakob Nikolas Kather

    2023

  12. [12]

    [Xia et al.(2026)] Bowei Xia, Mengkang Hu, Shijian Wang, Jiarui Jin, Wenxiang Jiao, Yuan Lu, Kexin Li, and Ping Luo

    LLM Agents Making Agent Tools.arXiv preprint arXiv:2502.11705(2025). [Xia et al.(2026)] Bowei Xia, Mengkang Hu, Shijian Wang, Jiarui Jin, Wenxiang Jiao, Yuan Lu, Kexin Li, and Ping Luo

    Pith/arXiv arXiv 2025

  13. [13]

    [Zhang et al.(2026)] Hanrong Zhang, Shicheng Fan, Henry Peng Zou, Yankai Chen, Zhenting Wang, Jiayu Zhou, et al

    Tool-Genesis: A Task-Driven Tool Creation Benchmark for Self-Evolving Language Agents.arXiv preprint arXiv:2603.05578 (2026). [Zhang et al.(2026)] Hanrong Zhang, Shicheng Fan, Henry Peng Zou, Yankai Chen, Zhenting Wang, Jiayu Zhou, et al

    arXiv 2026

  14. [14]

    Supplement §D

    CoEvoSkills: Self-Evolving Agent Skills via Co-Evolutionary Verification.arXiv preprint arXiv:2604.01687(2026). ������ ������� ��� ����� ���������� Supplementary Material The following appendices reproduce the supplementary material for the verified-subset pilot. References in the main text to “Supplement §D” and “Supplement §E” point to Appendices D and ...

    Pith/arXiv arXiv 2026