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Large language model agents have shown promise in bioinformatics, but most existing systems focus primarily on producing final answers, treating planning, tool use, and code execution as transient interactions. This design is poorly suited to long-horizon bioinformatics tasks, where conclusions must remain connected to...
This work exposes lazy grounding by injecting nearby evidence from answer-changing rewrites of benchmark questions into the search corpora, and shows that robust search agents must defend against not only misinformation but also the misapplication of factual evidence.
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