SPIN Unprocessed July 8, 2026 ai_technology research
When Should LLMs Search? Counterfactual Supervision for Search Routing
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arXiv:2607.05752v1 Announce Type: new Abstract: Search-augmented language models can use external evidence to compensate for limitations in parametric knowledge, but search is not uniformly beneficial: models may call search for questions they can already answer, or rely on noisy evidence when correction, clarification, or abstention would be more appropriate. We formulate this as an instance-level search-routing problem: deciding whether search is needed to improve task success relative to a no
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