Looking Is Not Picking: An Attention-Segment Account of Tool-Selection Failures in LLM Agents
arXiv:2606.16364v1 Announce Type: new Abstract: LLM agents mis-call tools, and the natural guess is that the model failed to see the right tool in a crowded harness. We show the opposite through a lens concurrent work sets aside -- the model's attention to labeled tool-definition segments. On real BFCL failures, by per-candidate attention argmax the model attends most to the correct tool 80% of the time (vs. 21% chance), and the gold is the under-attended segment on only 10%: it looks at the right tool and still picks wrong. This directly refutes the intuitive "crowded-harness / lost-in-the-mi
This research provides a deeper, albeit counter-intuitive, understanding of LLM agent failures in tool selection, moving beyond superficial explanations of 'lost in the crowded harness'.
Understanding the precise mechanisms of LLM agent failures, even when attention is correctly focused, is crucial for developing robust, reliable, and more effective autonomous AI systems.
This research reframes the problem of LLM agent mis-tooling from an issue of attention deficit to one of decision-making failure even with correct attention, requiring new research directions for improvement.
- · AI researchers focusing on attention mechanisms
- · Developers of LLM agents
- · Enterprises deploying autonomous AI systems
- · Simplified debugging approaches for LLM agents
- · The 'crowded harness' theory of failure
This finding will direct new research into the post-attention decision-making processes of LLM agents.
Improved understanding could lead to new architectural designs or training methodologies for more reliable autonomous agents.
More reliable autonomous agents will accelerate the deployment of AI in complex workflow automation, potentially impacting various industries.
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Read at arXiv cs.AI