Skip to content
OpenTrain AIFor AI Companies

HFEPX · Eval paper review

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

Yang Tian, Zhengpeng Shi, Yu Zhou, Bo Zhao

Published

Jun 24, 2026

Citations

0

Trust level

Moderate

Usefulness score

25/100 (Low)

Extraction confidence

55% (Moderate)

Derived from extracted protocol signals and abstract evidence.

Rater population

Not reported

Signals refreshed

Jun 27, 2026

Open arXiv Find implementation Shortlist (0) RSS feed

Tags

Automatic Metrics Coding Long Horizon Medicine Tool Use

Should you rely on this paper?

This paper is adjacent to HFEPX scope and is best used for background context, not as a primary protocol reference.

Use this for comparison and orientation, not as your only source.

Best use

Background context only

Use if you need

A benchmark-and-metrics comparison anchor.

What to verify

Validate the evaluation procedure and quality controls in the full paper before operational use.

Main weakness

No major weakness surfaced.

Human feedback signal
Not explicit
Not explicit in abstract metadata
Evaluation signal
Detected
Eval setup described
Usefulness for eval research
25/100
Adjacent candidate

Treat as adjacent context, not a core eval-method reference.

If you are doing eval pipeline work, start here

Human Eval Papers LLM-as-Judge Papers Pairwise Preference Papers Tool-Use Evaluation Search

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.

What we could verify

These are the protocol signals we could actually recover from the available paper metadata. Use them to decide whether this paper is worth deeper reading.

Human Feedback Types

missing

None explicit

No explicit feedback protocol extracted.

"Large language models are increasingly deployed as agents that solve tasks by interacting with external tool environments."

Evaluation Modes

strong

Automatic Metrics

Includes extracted eval setup.

"Large language models are increasingly deployed as agents that solve tasks by interacting with external tool environments."

Quality Controls

missing

Not reported

No explicit QC controls found.

"Large language models are increasingly deployed as agents that solve tasks by interacting with external tool environments."

Benchmarks / Datasets

strong

ToolBench

Useful for quick benchmark comparison.

"We introduce ToolBench-X, a benchmark for evaluating agents under recoverable reliability hazards."

Reported Metrics

strong

Accuracy

Useful for evaluation criteria comparison.

"These results suggest that tool-use evaluation should move beyond function-call accuracy toward task completion under unreliable tool environments."

Benchmarks and datasets

ToolBench

Reported metrics

accuracy
Human feedback details
Uses human feedback
No
Feedback types
None
Rater population
Not reported
Expertise required
Medicine, Coding
Evaluation details
Evaluation modes
Automatic Metrics
Agentic eval
Tool Use, Long Horizon
Quality controls
Not reported
Evidence quality
Moderate
Use this page as
Background context only

Research brief

Metadata summary

Large language models are increasingly deployed as agents that solve tasks by interacting with external tool environments.

Based on abstract + metadata only. Check the source paper before making high-confidence protocol decisions.

Key takeaways

  • 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.

Researcher actions

  • Compare this paper against nearby papers in the same arXiv category before using it for protocol decisions.
  • Validate inferred eval signals (Automatic metrics, Simulation environment) against the full paper.
  • Use related-paper links to find stronger protocol-specific references.

Caveats

  • Generated from abstract + metadata only; no PDF parsing.
  • Signals below are heuristic and may miss details reported outside the abstract.

Recommended queries

Accuracy

Contribution summary

  • Large language models are increasingly deployed as agents that solve tasks by interacting with external tool environments.
  • We introduce ToolBench-X, a benchmark for evaluating agents under recoverable reliability hazards.
  • These results suggest that tool-use evaluation should move beyond function-call accuracy toward task completion under unreliable tool environments.

Why it matters for eval

  • We introduce ToolBench-X, a benchmark for evaluating agents under recoverable reliability hazards.
  • These results suggest that tool-use evaluation should move beyond function-call accuracy toward task completion under unreliable tool environments.

Researcher checklist

  • Human feedback protocol is explicit

    No explicit human feedback protocol detected.

  • Evaluation mode is explicit

    Detected: Automatic Metrics

  • Quality control reporting appears

    No calibration/adjudication/IAA control explicitly detected.

  • Benchmark or dataset anchors are present

    Detected: ToolBench

  • Metric reporting is present

    Detected: accuracy