A sequential decision framework unifies evaluation of LLM-guided search policies
_reachsumit · x · 2026-10-30
A new arXiv paper models LLM-guided search (iterative query-generation/retrieval in RAG and deep research) as a sequential decision problem over ranked lists, with two primitive actions: select (advance judgment in an existing ranking) and spawn (generate a new ranking). Existing retrieval strategies become policies in a common environment, objectives become reward/weighting functions, and the formulation admits an RL interpretation, illustrated on high-recall information retrieval.
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