AdaGReS:Adaptive Greedy Context Selection via Redundancy-Aware Scoring for Token-Budgeted RAG

📝 Original Info

• Title: AdaGReS:Adaptive Greedy Context Selection via Redundancy-Aware Scoring for Token-Budgeted RAG
• ArXiv ID: 2512.25052
• Date: 2025-12-31
• Authors: Chao Peng, Bin Wang, Zhilei Long, Jinfang Sheng

📝 Abstract

Retrieval-augmented generation (RAG) is highly sensitive to the quality of selected context, yet standard top-k retrieval often returns redundant or near-duplicate chunks that waste token budget and degrade downstream generation. We present AdaGReS, a redundancy-aware context selection framework for token-budgeted RAG that optimizes a setlevel objective combining query-chunk relevance and intra-set redundancy penalties. Ada-GReS performs greedy selection under a tokenbudget constraint using marginal gains derived from the objective, and introduces a closedform, instance-adaptive calibration of the relevance-redundancy trade-off parameter to eliminate manual tuning and to adapt to candidatepool statistics and budget limits. We further provide a theoretical analysis showing that the proposed objective exhibits ε-approximate submodularity under practical embedding similarity conditions, yielding near-optimality guarantees for greedy selection. Experiments on opendomain question answering (Natural Questions) and a high-redundancy biomedical (drug) corpus demonstrate consistent improvements in redundancy control and context quality, translating to better end-to-end answer quality and robustness across settings.

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