Contextual Information Policy Optimization for Search Agents

Search agents extend large language models beyond static parametric memory by enabling them to acquire and use ex ternal evidence during multi-step reasoning. For knowledge intensive tasks involving complex or evolving information, their reliability depends not only on retrieving relevant ev idence but also on using it to guide subsequent reasoning. However, existing methods primarily reward final-answer cor rectness or intermediate progress, without directly assessing whether post-retrieval act

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ethics.ai (6 August 2026), “Contextual Information Policy Optimization for Search Agents,” evidence record 17071, https://ethics.ai/record/17071 (originally published by arXiv).

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