ReaLM-Retrieve uses step-level uncertainty to trigger retrievals during reasoning, achieving 10.1% better F1 scores and 47% fewer calls on multi-hop QA benchmarks.
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Large-scale log study of 14M+ agentic searches finds short sessions, intent-specific repetition patterns, and that 54% of new query terms trace to prior retrieved evidence.
NeocorRAG uses Evidence Chains to achieve SOTA retrieval quality in RAG on HotpotQA, 2WikiMultiHopQA, MuSiQue, and NQ for 3B and 70B models while using under 20% of the tokens of comparable methods.
Marketplace Evaluation uses repeated-interaction simulations to assess information access systems with marketplace-level metrics such as retention and market share that complement traditional accuracy measures.
KnowSA_CKP uses comparative knowledge probing to selectively augment LLM prompts for items with knowledge gaps, improving recommendation accuracy and context efficiency.
LTRR learns to rank a pool of retrievers by their expected contribution to RAG answer correctness and shows that query-dependent selection beats the best single retriever on QA benchmarks.
citing papers explorer
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When to Retrieve During Reasoning: Adaptive Retrieval for Large Reasoning Models
ReaLM-Retrieve uses step-level uncertainty to trigger retrievals during reasoning, achieving 10.1% better F1 scores and 47% fewer calls on multi-hop QA benchmarks.
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Agentic Search in the Wild: Intents and Trajectory Dynamics from 14M+ Real Search Requests
Large-scale log study of 14M+ agentic searches finds short sessions, intent-specific repetition patterns, and that 54% of new query terms trace to prior retrieved evidence.
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NeocorRAG: Less Irrelevant Information, More Explicit Evidence, and More Effective Recall via Evidence Chains
NeocorRAG uses Evidence Chains to achieve SOTA retrieval quality in RAG on HotpotQA, 2WikiMultiHopQA, MuSiQue, and NQ for 3B and 70B models while using under 20% of the tokens of comparable methods.
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Evaluation of Agents under Simulated AI Marketplace Dynamics
Marketplace Evaluation uses repeated-interaction simulations to assess information access systems with marketplace-level metrics such as retention and market share that complement traditional accuracy measures.
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Filling the Gaps: Selective Knowledge Augmentation for LLM Recommenders
KnowSA_CKP uses comparative knowledge probing to selectively augment LLM prompts for items with knowledge gaps, improving recommendation accuracy and context efficiency.
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LTRR: Learning To Rank Retrievers for LLMs
LTRR learns to rank a pool of retrievers by their expected contribution to RAG answer correctness and shows that query-dependent selection beats the best single retriever on QA benchmarks.