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A novel reference-free preference optimization framework D UAL R EFORM that generates pseudo reference passages from commonly-encountered conversational datasets containing only queries and responses that achieves 96.9–99.1% of the retrieval accuracy attainable only with reference passages.
TimelyRAG is proposed, a retriever-agnostic framework that incorporates temporal distance into ranking to align queries with version-appropriate documents, and TimelyQABench is introduced, the first benchmark for regulation-heavy domains with overlapping-evolving challenges.
This work presents Q U DAR, a dual-perspective adaptive retrieval framework motivated by a systematic analysis of retrieval behavior across retriever type and query format, enabling effective integration of complementary retrieval signals while mitigating noise.
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