RAG Context Compression

RAG: Retrieval-Augmented Generation

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12 papers0 papers in the last four weeks, down 100% on the four weeks before. 0.0% of all new papers.

The Attribution-Compression Frontier in Retrieval-Augmented Generation
Sep 13, 2026Deepanshu ModyRAG Context Compression RAG Citation Faithfulness
Center for Data Science New York University
REVA: Reusable Evidence View Aggregation for Context-Efficient RAG Serving
Sep 10, 2026Tuan Nguyen, Qiran Hu, Banruo Liu +3LLM Inference Efficiency Retrieval-Augmented Generation
VinUni-Illinois Smart Health Center, VinUniversity, Vietnam · University of Illinois Urbana-Champaign, USA
Compression Beyond the Uncompressed: A Two-Stage Training Recipe for Soft Context Compression in RAG
Sep 4, 2026Shuyu Guo, Shuo Zhang, Zhaochun RenRL for RAG LLM Inference Acceleration
Shandong University Qingdao, China · Bloomberg London, United Kingdom · Leiden University Leiden, The Netherlands
From Retrieved Context to Runtime Control: Adaptive Compression for Edge-based RAG
Aug 20, 2026Zlatan Feric, Amir Taherin, Yanzhi Wang +1RAG Context Compression Context Compression
Northeastern University Boston, Massachusetts USA
AdaMem: Adaptive Memory Token Allocation for Soft Compression in Retrieval-Augmented Generation
Aug 12, 2026Artem Sakhno, Grigorii Davydenko, Omar Zoloev +3RAG Context Compression Context Compression
RAGOCR: Optical Compression of Retrieval-Augmented Text via Visual Representation
Aug 1, 2026Jiayang Yu, Jialun Zhong, Lei ZouMultimodal RAG Retrieval-Augmented Generation
Wangxuan Institute of Computer Technology, Peking University Beijing, China
Fixed RAG Compression Collapses Measured Reader Scaling
Jun 20, 2026Sugam Panthi, Rabab AbdelfattahRetrieval-Augmented Generation LLM Evaluation
The University of Southern Mississippi
ConCise: Training-Free Conclusion-Chain State Compression for Cost-Efficient Multi-Step RAG Services
Jun 13, 2026Kuan Yan, Zhiqing Tang, Tian Wang +1Retrieval-Augmented Generation Cost-Aware Inference
Faculty of Arts and Sciences, Beijing Normal University, Zhuhai, China · Institute of Artificial Intelligence and Future Networks, Beijing Normal University, Zhuhai, China · Guangdong Key Lab of AI & Multi-Modal Data Processing, Beijing Normal-Hong Kong Baptist University, Zhuhai, China
Context Compression Is Not One Thing: Readable Symbolic Re-expression vs. Coherent Summary at Matched Budget
Jun 12, 2026Sisong Bei, Mikhail L. Arbuzov, Ziwei Dong +2Multi-Hop QA Context Compression
Independent Researcher · Palo Alto Networks
GRC: Unifying Reasoning-Driven Generation, Retrieval and Compression
May 9, 2026Zhongtao Miao, Qiyu Wu, Yoshimasa TsuruokaRetrieval-Augmented Generation Memory-Augmented Language Models
The University of Tokyo · Tsuruoka Lab. · Independent Researcher
EnComp: Lightweight Encoder-Only Context Compression for Retrieval-Augmented Question Answering
Mar 10, 2026Thao Do, Dinh Phu Tran, An Vo +2Efficient Language Model Inference Knowledge-Intensive QA
Korea Advanced Institute of Science and Technology (KAIST), Daejeon 34141, South Korea
AutoPrunedRetriever: Persistent Reasoning Graphs for Efficient and Extensible Retrieval-Augmented Generation
Feb 4, 2026Ning Wang, Kuanyan Zhu, Daniel Yuehwoon Yee +4RAG Context Compression Retrieval-Augmented Generation
Cornell University · University of Cambridge · The University of Hong Kong +2

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The Attribution-Compression Frontier in Retrieval-Augmented Generation

Deepanshu Mody

Center for Data Science New York University

RAG Context Compression RAG Citation Faithfulness Retrieval-Augmented Generation RAG Evaluation

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Context compression reduces generator input in retrieval-augmented generation, but answer quality alone does not characterize citation attribution. We measure citation attribution across compression methods and budgets, comparing reranking, extractive selection, abstractive summarization, token pruning, and an extract-cluster-rewrite construction on ASQA and QASPER under a fixed generator and primary entailment evaluator. On ASQA at a nominal 0.25 budget (achieved compression 0.08), a RECOMP-style compressor's citations score 0.86 precision against its summaries but 0.12 against source spans under our re-attributability protocol. These estimates depend on a shared NLI model for span recovery and citation scoring and lack independent human calibration. Extractive selection's observed grounded precision ranges from 0.43 to 0.49 across nominal budgets from one-half to one-tenth of the ASQA context, while answer quality declines. For the same RECOMP setting, claim verification after source recovery yields an unsupported rate of 0.88 versus 0.17 when checking summaries. This gap persists beyond structural rejection of missing provenance, but remains evaluator-dependent. A 200-question TRUE T5-XXL audit also finds emitted--grounded gaps under both fixed and recomputed source mappings, without establishing human-calibrated support rates.