Framing LLM agent loops as a Context Gathering Decision Process POMDP yields a predicate-based belief state that boosts multi-hop reasoning up to 11.4% and an exhaustion gate that cuts token use up to 39% with no performance loss.
FAIR-RAG: Faithful adaptive iterative refinement for retrieval-augmented generation
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Iterative RAG outperforms Gold Context RAG by up to 25.6 points on ChemKGMultiHopQA across 11 LLMs, mainly by staging retrieval to avoid context overload and correct hypothesis drift.
AdaRankLLM shows adaptive listwise reranking outperforms fixed-depth retrieval for most LLMs by acting as a noise filter for weak models and an efficiency optimizer for strong ones, with lower context use.
citing papers explorer
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The Context Gathering Decision Process: A POMDP Framework for Agentic Search
Framing LLM agent loops as a Context Gathering Decision Process POMDP yields a predicate-based belief state that boosts multi-hop reasoning up to 11.4% and an exhaustion gate that cuts token use up to 39% with no performance loss.
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When Iterative RAG Beats Ideal Evidence: A Diagnostic Study in Scientific Multi-hop Question Answering
Iterative RAG outperforms Gold Context RAG by up to 25.6 points on ChemKGMultiHopQA across 11 LLMs, mainly by staging retrieval to avoid context overload and correct hypothesis drift.
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Rethinking the Necessity of Adaptive Retrieval-Augmented Generation through the Lens of Adaptive Listwise Ranking
AdaRankLLM shows adaptive listwise reranking outperforms fixed-depth retrieval for most LLMs by acting as a noise filter for weak models and an efficiency optimizer for strong ones, with lower context use.