P2R builds LLM-generated structured expertise profiles and uses coarse-to-fine hybrid retrieval plus rubric-scoring committees to match papers to reviewers, outperforming paper-to-paper baselines on NeurIPS, SIGIR, and SciRepEval.
Reciprocal rank fusion outperforms condorcet and individual rank learning methods,
3 Pith papers cite this work. Polarity classification is still indexing.
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SkillGraph-Service builds a provenance-preserving knowledge graph from multiple competency frameworks and achieves nDCG@5 above 0.94 with sub-200 ms latency via KG-first hybrid retrieval and constrained LLM explanations.
BLUEmed combines hybrid RAG with structured multi-agent debate and a safety filter to detect terminology substitution errors in clinical notes, reaching 69.13% accuracy under few-shot prompting and outperforming single-agent and debate-only baselines.
citing papers explorer
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Beyond Paper-to-Paper: Structured Profiling and Rubric Scoring for Paper-Reviewer Matching
P2R builds LLM-generated structured expertise profiles and uses coarse-to-fine hybrid retrieval plus rubric-scoring committees to match papers to reviewers, outperforming paper-to-paper baselines on NeurIPS, SIGIR, and SciRepEval.
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KG-First, LLM-Fallback: A Hybrid Microservice for Grounded Skill Search and Explanation
SkillGraph-Service builds a provenance-preserving knowledge graph from multiple competency frameworks and achieves nDCG@5 above 0.94 with sub-200 ms latency via KG-first hybrid retrieval and constrained LLM explanations.
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BLUEmed: Retrieval-Augmented Multi-Agent Debate for Clinical Error Detection
BLUEmed combines hybrid RAG with structured multi-agent debate and a safety filter to detect terminology substitution errors in clinical notes, reaching 69.13% accuracy under few-shot prompting and outperforming single-agent and debate-only baselines.