ToolMenuBench: Benchmarking Tool-Menu Filtering Strategies for Reliable and Efficient LLM Agents
arXiv:2606.15508v1 Announce Type: new Abstract: Tool-augmented large language model agents increasingly operate over large tool libraries, but existing evaluations often focus on whether a model can call a tool correctly rather than how the visible tool menu shapes reliability, efficiency, and safety-relevant risk exposure. We introduce ToolMenuBench, a benchmark for evaluating tool-menu construction in multi-step LLM agents. ToolMenuBench varies tool-menu size, distractor type, state-dependent task structure, and risk exposure, and reports both filter-level and downstream agent metrics, inclu
The proliferation of tool-augmented LLM agents necessitates robust evaluation methodologies for their increasing complexity and deployment in real-world scenarios.
Improving tool-menu filtering directly impacts the reliability, efficiency, and safety of LLM agents, which are becoming critical components of automated workflows.
The introduction of ToolMenuBench provides a standardized framework to systematically evaluate and refine how LLM agents interact with large tool libraries, moving beyond simple tool calling success.
- · AI agent developers
- · Enterprises deploying LLM agents
- · AI safety researchers
- · Tool library providers
- · Inefficient LLM agent architectures
- · Systems with inadequate tool management
- · Organizations ignoring agent reliability and safety
More reliable and efficient LLM agents become available for various applications.
Increased adoption of LLM agents in critical enterprise functions, automating more complex tasks.
A shift in competitive advantage towards companies with superior agentic tool-management capabilities.
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Read at arXiv cs.AI