ToolMisuseBench: An Offline Deterministic Benchmark for Tool Misuse and Recovery in Agentic Systems
On the map: LLM agents and tool use → Tool learning and function calling
Abstract (arXiv)
Tool using agents often fail for operational reasons even when language understanding is strong. Common causes include invalid arguments, interface drift, weak recovery, and inefficient retry behavior. We introduce ToolMisuseBench, an offline deterministic benchmark for evaluating tool misuse and recovery under explicit step, call, and retry budgets. The benchmark covers CRUD, retrieval, file, and scheduling environments with replayable fault injection. It reports success, invalid call behavior, policy violations, recovery quality, and budgeted efficiency. We release a public dataset with 6800 tasks and a reproducible evaluation pipeline. Baseline results show fault specific recovery gains for schema aware methods, while overall success remains limited under the released authorization and hard failure settings.
Key facts. Cited by 3 papers in the map (data through 7 Oct 2026). Placed in Tool learning and function calling, one of 16 directions in LLM agents and tool use. Builds on 7 of its 10 references that are in the map. First posted on arXiv 2 Apr 2026.
Builds on
7 of 10 references.
Closest ancestors · 3
Same direction, newest first
- 2026Schema First Tool APIs for LLM Agents: A Controlled Study of Tool Misuse, Recovery, and Budgeted Performance12 Mar
- 2023Gorilla: Large Language Model Connected with Massive APIs24 May
- 2023Toolformer: Language Models Can Teach Themselves to Use Tools9 Feb
Other references · 4
- 2022ReAct: Synergizing Reasoning and Acting in Language Modelscited by 8,355
- 2022Beyond the Imitation Game: Quantifying and extrapolating the capabilities of language modelscited by 1,970
- 2023AgentBench: Evaluating LLMs as Agentscited by 1,211
- 2026Guardrails as Infrastructure: Policy-First Control for Tool-Orchestrated Workflowscited by 1
Built on by
Citing papers per year: 2026: 3.
3 papers.
Similar, not cited
Close in wording, no citation either way
- 2026Beyond Function Calling: Benchmarking Tool-Using Agents under Tool-Environment Unreliability
- 2026ParaRecover: A Process-Level Benchmark for Error Localization and Recovery in Parallel Tool-Use Agents
- 2026Loud Failures, Quiet Failures: Fault Detection and Recovery in Tool-Using Language Model Agents
- 2026When Tools Fail: Benchmarking Dynamic Replanning and Anomaly Recovery in LLM Agents
- 2026ToolRobustBench: Stage-Wise Perturbation Evaluation and Failure Diagnosis for Tool-Calling Agents
- 2025PALADIN: Self-Correcting Language Model Agents to Cure Tool-Failure Cases
- 2026One Readout, Many Repairs: Diffusion-Guided Hierarchical Search for Tool-Agent Repair
- 2026AgentProcessBench: Diagnosing Step-Level Process Quality in Tool-Using Agents