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Research questionHow can soft context compression reduce RAG inference cost without inheriting the uncompressed system’s performance ceiling?Retrieved documents can make RAG inference expensive, and compressing them by imitating an uncompressed system may limit the compressed model to that system’s capabilities.
Inference Optimization
Information Retrieval
Natural Language Processing
Reinforcement Learning
Retrieval-Augmented Generation
Latest papersRecent research connected to this question, newest first.Compression Beyond the Uncompressed: A Two-Stage Training Recipe for Soft Context Compression in RAGThe source concerns soft compression of retrieved RAG documents into shorter embedding sequences for open-domain question answering. Evidence covers five benchmarks at retrieval depths from top-5 to top-30, reporting 16× context compression and 4×–24× inference acceleration relative to uncompressed RAG; the input does not establish broader deployment performance.research paper · Sep 4, 2026
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