arXiv:2606.06284v1 Announce Type: new Abstract: Large language model agents increasingly rely on external tools, but larger tool menus can reduce reliability and efficiency by increasing wrong-tool calls, premature actions, and token cost. Existing tool-selection methods often optimize semantic relevance, exposing tools whose names or descriptions match the user request. We argue that relevance is insufficient: a tool may be related to the task while still being unnecessary or premature at the current step. We propose Causal Minimal Tool Filtering (CMTF), a training-free method that selects to
As LLM agents become more sophisticated and tool ecosystems expand, the challenge of efficient and reliable tool selection is intensifying, making this research timely.
Improving the reliability and efficiency of LLM agents by reducing errors and costs in tool selection directly impacts their commercial viability and adoption across various industries.
This provides a new, training-free method for LLMs to select tools more effectively, potentially leading to more robust and economical agentic systems without requiring extensive fine-tuning.
- · AI Agent developers
- · Cloud providers (reduced compute cost)
- · Enterprises adopting AI Agents
- · LLM researchers
- · Developers of less efficient tool selection methods
- · Companies with high token usage due to poor tool selection
LLM agents will be able to perform complex tasks with fewer errors and lower operational costs.
Increased adoption of autonomous AI agents across industries due to enhanced reliability and efficiency, accelerating automation.
More sophisticated and interconnected agentic systems could lead to new forms of white-collar task automation and potentially displace existing SaaS providers.
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Read at arXiv cs.AI