pith. sign in

arxiv: 2606.25819 · v2 · pith:6OMJZYBFnew · submitted 2026-06-24 · 💻 cs.CL · cs.SE

Beyond Function Calling: Benchmarking Tool-Using Agents under Tool-Environment Unreliability

Yang Tian , Zhengpeng Shi , Yu Zhou , Bo Zhao This is my paper

Pith reviewed 2026-06-30 09:52 UTC · model grok-4.3

classification 💻 cs.CL cs.SE
keywords tool-use agentsbenchmarkreliability hazardsrecovery strategiesToolBench-Xlanguage model agentsunreliable toolstask completion
0
0 comments X

The pith

Agents that succeed with reliable tools often fail when those tools include recoverable hazards, mainly from poor diagnosis and recovery.

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

The paper creates ToolBench-X to evaluate language model agents on multi-step tasks when tool environments include five types of recoverable hazards instead of clean conditions. It finds a clear reliability gap: strong performance on reliable tools does not carry over when hazards appear, even though each case has at least one valid recovery path such as retrying or cross-checking. Failures trace more to weak hazard recognition and ineffective recovery than to how many tools are called or how much inference budget is used. Targeted hints about recovery improve results more than simply scaling test-time compute. The work argues that future tool-use benchmarks must test task completion under unreliable conditions rather than isolated function-call accuracy.

Core claim

Starting from clean tool environments, ToolBench-X injects five structured hazard types—Specification Drift, Invocation Error, Execution Failure, Output Drift, and Cross-source Conflict—each remaining solvable through at least one valid recovery path such as retrying, fallback, verification, or cross-checking. Experiments reveal a substantial reliability gap: agents that perform well with reliable tools often fail under recoverable hazards. Further analysis shows that failures are driven less by tool-use volume or inference budget than by limited hazard diagnosis and ineffective recovery. Targeted recovery hints recover many failed tasks, while test-time scaling yields more limited gains.

What carries the argument

ToolBench-X benchmark that pairs executable multi-step tasks across domains and workflows with deterministic tools, then injects five hazard types while guaranteeing each instance has at least one valid recovery path for automatic evaluation.

If this is right

  • Targeted recovery hints recover many failed tasks that agents otherwise leave unsolved.
  • Test-time scaling produces smaller gains than direct recovery support under hazards.
  • Tool-use evaluation needs to shift focus from function-call accuracy to full task completion under unreliable conditions.
  • Hazard diagnosis ability matters more for success than raw tool-call volume or inference budget.

Where Pith is reading between the lines

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

  • Agents may need explicit training on hazard detection and recovery sequences rather than only on clean tool use.
  • Real deployments could require built-in verification layers that the current benchmark does not test.
  • Extending the benchmark to include unrecoverable hazards would reveal whether the observed gap widens further.

Load-bearing premise

The five hazard types and their injection method adequately represent real-world tool unreliability while keeping each instance solvable via at least one valid recovery path.

What would settle it

A run of current top agents on ToolBench-X that shows no drop in task completion rate relative to clean-tool versions, or where adding recovery hints produces no measurable improvement.

Figures

Figures reproduced from arXiv: 2606.25819 by Bo Zhao, Yang Tian, Yu Zhou, Zhengpeng Shi.

Figure 1
Figure 1. Figure 1: Benchmark construction pipeline. We first define seven topic categories, generate diverse task scenarios for each topic, [PITH_FULL_IMAGE:figures/full_fig_p003_1.png] view at source ↗
Figure 2
Figure 2. Figure 2: Overview of the benchmark distribution across task domains, reliability hazard types, and number of tools per task, [PITH_FULL_IMAGE:figures/full_fig_p004_2.png] view at source ↗
Figure 3
Figure 3. Figure 3: Post-error behaviors after the first failed tool re [PITH_FULL_IMAGE:figures/full_fig_p005_3.png] view at source ↗
Figure 4
Figure 4. Figure 4: Overall accuracy on the 200-task subset across five [PITH_FULL_IMAGE:figures/full_fig_p006_4.png] view at source ↗
Figure 6
Figure 6. Figure 6: Failure behavior taxonomy by model. Error Analysis Final-answer accuracy indicates whether an agent succeeds, but not how failure unfolds. To diagnose failed no-hint trajec￾tories, we use four observable signals: final-answer validity, whether tool use continues after the first error, the number of post-error calls At, and the call expansion ratio Rt. With me￾dian thresholds of At = 3 and Rt = 1.00, we gro… view at source ↗
Figure 7
Figure 7. Figure 7: Representative failure and recovery trajectories under structured tool uncertainty. [PITH_FULL_IMAGE:figures/full_fig_p014_7.png] view at source ↗
read the original abstract

Large language models are increasingly deployed as agents that solve tasks by interacting with external tool environments. Although recent tool-use benchmarks increasingly cover complex task settings, they still largely assume clean, stable, and trustworthy tool environments, leaving tool-environment unreliability insufficiently examined. We introduce ToolBench-X, a benchmark for evaluating agents under recoverable reliability hazards. ToolBench-X contains executable multi-step tasks across diverse domains and sequential, parallel, and mixed workflows, each paired with deterministic tools and a canonical final answer for automatic evaluation. Starting from clean tool environments, ToolBench-X injects five structured hazard types: Specification Drift, Invocation Error, Execution Failure, Output Drift, and Cross-source Conflict. Crucially, each injected instance remains solvable through at least one valid recovery path, such as retrying, fallback, verification, or cross-checking. Experiments reveal a substantial reliability gap: agents that perform well with reliable tools often fail under recoverable hazards. Further analysis shows that failures are driven less by tool-use volume or inference budget than by limited hazard diagnosis and ineffective recovery. Targeted recovery hints recover many failed tasks, while test-time scaling yields more limited gains. These results suggest that tool-use evaluation should move beyond function-call accuracy toward task completion under unreliable tool environments. The code and data is available at https://github.com/Foreverskyou/ToolBench-X.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Referee Report

2 major / 3 minor

Summary. The paper introduces ToolBench-X, a benchmark extending tool-use evaluation to unreliable tool environments. It defines five recoverable hazard types (Specification Drift, Invocation Error, Execution Failure, Output Drift, Cross-source Conflict) injected into multi-step tasks with sequential, parallel, and mixed workflows. Each task has deterministic tools and a canonical answer. Experiments show agents succeeding on clean tools fail substantially under hazards, with failures attributed primarily to poor hazard diagnosis and recovery rather than tool-use volume or inference budget; recovery hints improve performance more than test-time scaling. The work concludes that tool-use benchmarks should prioritize task completion under unreliability and releases code and data.

Significance. If the central empirical claims hold, the benchmark provides a valuable stress test for agent robustness in realistic settings where tool environments are noisy. The public code and data at the GitHub link are a clear strength for reproducibility. The distinction between volume/budget limits and diagnosis/recovery failures, if substantiated, offers actionable guidance for agent design beyond current function-calling metrics.

major comments (2)
  1. [Benchmark construction (abstract and §3)] Benchmark construction (abstract and §3): The claim that 'each injected instance remains solvable through at least one valid recovery path' is load-bearing for interpreting the reliability gap as agent limitations rather than unsolvable instances. No explicit verification procedure is described for parallel or mixed workflows under Specification Drift, Output Drift, or Cross-source Conflict, where drift or unresolved conflicts could eliminate all paths to the canonical answer. This directly affects the validity of the diagnosis/recovery attribution.
  2. [Experimental setup (§4)] Experimental setup (§4): The abstract and results attribute failures to 'limited hazard diagnosis and ineffective recovery' rather than volume or budget, yet the manuscript provides insufficient detail on the exact agent selection criteria, hazard injection implementation, automatic evaluation metrics, and statistical controls (e.g., variance across runs or hazard severity). Without these, the gap size and causal attribution lack verifiable support.
minor comments (3)
  1. [Results figures/tables] Table or figure captions should explicitly state the number of tasks per workflow type and per hazard to allow readers to assess coverage.
  2. [§2 or §3] Notation for the five hazard types is introduced without a compact summary table; adding one would improve readability when comparing recovery strategies.
  3. [Abstract and conclusion] The GitHub link is given, but the manuscript should include a brief statement on which components (task definitions, hazard injectors, evaluation scripts) are released to support the reproducibility claim.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for the constructive feedback on our manuscript. We address each major comment below and will revise the paper to provide the requested clarifications and additional details.

read point-by-point responses

  1. Referee: [Benchmark construction (abstract and §3)] The claim that 'each injected instance remains solvable through at least one valid recovery path' is load-bearing for interpreting the reliability gap as agent limitations rather than unsolvable instances. No explicit verification procedure is described for parallel or mixed workflows under Specification Drift, Output Drift, or Cross-source Conflict, where drift or unresolved conflicts could eliminate all paths to the canonical answer. This directly affects the validity of the diagnosis/recovery attribution.

    Authors: We agree that an explicit verification procedure should have been described. In the revised manuscript we will add a dedicated subsection to §3 that details the verification process: for every hazard type and workflow variant (including parallel and mixed), we enumerate recovery strategies (retry, fallback, cross-check), run automated simulation to confirm at least one path reaches the canonical answer, and perform targeted manual inspection on a sample of parallel/mixed cases under Specification Drift, Output Drift, and Cross-source Conflict. Concrete examples of verified recovery paths will be included. revision: yes

  2. Referee: [Experimental setup (§4)] The abstract and results attribute failures to 'limited hazard diagnosis and ineffective recovery' rather than volume or budget, yet the manuscript provides insufficient detail on the exact agent selection criteria, hazard injection implementation, automatic evaluation metrics, and statistical controls (e.g., variance across runs or hazard severity). Without these, the gap size and causal attribution lack verifiable support.

    Authors: We acknowledge that the current §4 lacks sufficient implementation detail. In the revision we will expand this section to specify: (i) exact agent selection criteria and prompting configurations, (ii) hazard injection code-level details and parameters for each workflow type, (iii) the complete automatic evaluation protocol (including canonical-answer matching rules), and (iv) statistical controls such as run-to-run variance, hazard-severity ablations, and explicit budget-vs-diagnosis comparisons. These additions will make the reported gaps and attributions reproducible and verifiable. revision: yes

Circularity Check

0 steps flagged

No circularity: empirical benchmark with no derivations or fitted predictions

full rationale

The paper introduces ToolBench-X as an empirical benchmark for agent reliability under injected hazards, with all claims grounded in experimental results rather than any mathematical derivation chain. No equations, parameters fitted to subsets, self-citations used as load-bearing uniqueness theorems, or ansatzes smuggled via prior work appear in the provided text. The design choice that each hazard instance has at least one recovery path is an explicit benchmark assumption (not derived from or equivalent to the reported outcomes), and the public code allows external verification. This is a standard non-circular empirical study; the central reliability-gap claim rests on observed agent performance differences, not on quantities defined by the authors' own inputs.

Axiom & Free-Parameter Ledger

0 free parameters · 1 axioms · 1 invented entities

The benchmark rests on the domain assumption that tasks can be made executable with deterministic tools and canonical answers even after hazard injection; the five hazard types are newly postulated constructs without independent evidence outside this work.

axioms (1)
  • domain assumption Tasks can be designed with deterministic tools and canonical final answers for automatic evaluation even after hazard injection
    Enables objective scoring of recovery success as described in the benchmark construction.
invented entities (1)
  • Specification Drift, Invocation Error, Execution Failure, Output Drift, Cross-source Conflict no independent evidence
    purpose: To simulate distinct recoverable reliability hazards in tool environments
    These five types are defined and injected by the paper as the core of ToolBench-X.

pith-pipeline@v0.9.1-grok · 5773 in / 1356 out tokens · 40775 ms · 2026-06-30T09:52:55.411240+00:00 · methodology

Review history (2 revisions) →

discussion (0)

Sign in with ORCID, Apple, or X to comment. Anyone can read and Pith papers without signing in.

Reference graph

Works this paper leans on

22 extracted references · 1 canonical work pages

  1. [1]

    tasks": [ {

    Agentnoisebench: Benchmarking robustness of tool- using llm agents under noisy condition.arXiv preprint arXiv:2602.11348. Xi,Z.;Liang,S.;Liu,Q.;Zhang,J.;Peng,L.;Nan,F.;Nayim, M.; Zhang, T.; Mundada, R.; Qin, L.; et al. 2026. ToolGym: an Open-world Tool-using Environment for Scalable Agent TestingandDataCuration.arXivpreprintarXiv:2601.06328. Yang,A.;Li,A....

    work page arXiv 2026

  2. [2]

    Generate exactly one function for each name in ‘tools_used‘

  3. [3]

    Do not add unrelated functions, classes, or helpers

    Function names must exactly match the requested tool names. Do not add unrelated functions, classes, or helpers

  4. [4]

    Use Python 3.9-compatible syntax and ensure that all outputs are deterministic and JSON-serializable

  5. [5]

    Respect the specified ‘sequential‘, ‘parallel‘, or ‘mixture‘ workflow and maintain compatible data contracts across tools

  6. [6]

    Derive outputs from function inputs and handle unseen but valid inputs through general logic and deterministic fallbacks

  7. [7]

    Preserve a guarded benchmark-context path that reproduces the benchmark answer exactly without forcing that answer for unrelated inputs

  8. [8]

    When external information is required, prefer public no-key sources and provide deterministic fallback behavior

  9. [9]

    Include provenance, confidence, evidence, fallback, and error information where applicable

  10. [10]

    Ensure that each downstream required parameter can be obtained from an upstream output or the original user request. [... omitted: standardized return schemas, robust input normalization, benchmark-context detection, external-data policies, inter-tool contracts, canonicalization rules, and validation checks ...] # Output Requirements Return Python code on...

  11. [11]

    Fix only the failing parts

  12. [12]

    Preserve function names and signatures unless required for correctness

  13. [13]

    Table 12: Prompt for hazard injection and hint generation

    Return corrected Python code only. Table 12: Prompt for hazard injection and hint generation. # Role You are a Reliability Stress-Test Prompt Engineer for agent-tool evaluation. # Mission Patch an existing successful Python module in place by adding deterministic reliability hazards. Preserve its function names, signatures, schemas, and original behavior ...

  14. [14]

    When injection is disabled, the patched module must preserve its original benchmark-correct behavior and output schema

  15. [15]

    ‘strict_no_hint_profile‘ must introduce an observable and meaningful failure or behavior drift

  16. [16]

    ‘guided_with_hint_profile‘ must retain the same fault schedule and may provide diagnostic information about the observed failure to support recovery, without revealing the final answer

  17. [17]

    # Hazard Categories Assign exactly one category to each task: Specification, Invocation, Execution, Output, or Cross-Source Uncertainty

    At least one reachable and verifiable path to the correct answer must remain available. # Hazard Categories Assign exactly one category to each task: Specification, Invocation, Execution, Output, or Cross-Source Uncertainty. All failpoints and hints within the task must use this category. # Core Requirements

  18. [18]

    Select failpoints deterministically using task identity, tool name, call slot, failpoint, and ‘FAIL_SEED‘

  19. [19]

    Record observable injection events without overwriting the original business payload

  20. [20]

    Do not silently swallow exceptions or represent hard failures as successful results

  21. [21]

    Block premature completion when evidence is incomplete or contradictory, and require final-answer canonicalization before ‘FINISH‘

  22. [22]

    task_type

    Keep the injected fault schedule reproducible across no-hint and with-hint conditions to support controlled comparison. [... omitted: activation schemas, failure families, event contracts, recovery rules, and anti-regression checks ...] # Runtime Input { "task_type": "<workflow type>", "id": "<task id>", "user_prompt": "<user request>", "tools_used": ["<t...