arXiv:2607.04686v1 Announce Type: cross Abstract: Tool calling is central to modern language model agents, but aggregate benchmark scores often hide where tool use fails. A model that never calls a needed tool and a model that calls the tool but ignores the result can look similar under final task accuracy. We introduce ToolFailBench, a diagnostic benchmark for measuring tool-use failures across 1,000 tasks in finance, medicine, law, cybersecurity, and real estate. Tool-required tasks return values the model wouldn't guess, forcing it to trust the tool while control tasks attach the same tools
The rapid advancement and deployment of LLM agents make understanding and mitigating their failure modes a critical research area, especially as their use extends into sensitive domains.
This research provides a diagnostic tool to improve the reliability and trustworthiness of LLM agents, which is essential for their adoption in high-stakes fields like finance, medicine, and cybersecurity.
The ability to accurately diagnose specific tool-use failures in LLM agents shifts focus from aggregate performance metrics to granular operational deficiencies, enabling more targeted development and improvement.
- · LLM developers
- · Enterprises deploying AI agents
- · AI safety researchers
- · Security sector
- · Developers neglecting agent reliability
- · Companies with opaque AI systems
Improved benchmarks will lead to more robust and reliable AI agents capable of performing complex tasks.
Increased trust in AI agents will accelerate their integration into critical business processes, particularly in highly regulated industries.
The enhanced reliability of AI agents could lead to new regulatory frameworks specifically addressing agent autonomy and error mitigation in sensitive domains.
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Read at arXiv cs.AI