arXiv:2605.24660v1 Announce Type: cross Abstract: Before an LLM agent can use a tool, a retrieval system must decide which candidate tools to show to the agent. How long should that shortlist be? Show too many tools and the model struggles to choose. Show too few and the correct tool may not appear. Most systems apply a fixed shortlist size to every query, but no standard metric exists to evaluate whether that size was appropriate. We treat the number of tools shown to an LLM agent as the object of evaluation and we apply Bits-over-Random (BoR), a chance-corrected metric that asks whether succ
The proliferation of LLM agents and the complexity of tool integration necessitates more sophisticated methods for optimizing agent performance and resource utilization.
This research provides a critical metric for evaluating and improving the efficiency of LLM agents, directly impacting their real-world applicability and cost-effectiveness.
The adoption of 'Bits-over-Random' or similar chance-corrected metrics for tool selection will lead to more intelligent and adaptive LLM agent systems, moving away from fixed shortlist sizes.
- · AI Agent Developers
- · Enterprises deploying LLM agents
- · Efficiency-focused AI researchers
- · AI systems using naive tool selection
- · Inefficient LLM agent designs
LLM agents will become more adept at selecting and utilizing tools, leading to improved task completion rates and reduced computational overhead.
This improved efficiency will accelerate the deployment and integration of AI agents across various industries, enhancing automation workflows.
The increased utility of agents could lead to new business models built around highly specialized and efficient AI agent services.
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Read at arXiv cs.LG