Looking Is Not Picking: An Attention-Segment Account of Tool-Selection Failures in LLM Agents
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-middle" explanation: the failure is at the decision readout, not the harness, and we pin it there three ways. (1) Input vs. readout: repairing the ...
| Authors | Shiyang Chen |
|---|---|
| Primary category | cs.AI |
| Published | 15 June 2026 |
| Last updated at source | 27 June 2026 |
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