Less is More: Compact Clue Selection for Efficient Retrieval-Augmented Generation Reasoning

Current RAG retrievers are designed primarily for human readers, emphasizing complete, readable, and coherent paragraphs. However, LLMs benefit more from precise, compact, and well-structured input, which enhances reasoning quality and efficiency. Existing methods often rely on reranking or summarization to identify key sentences, but may suffer from semantic breaks and unfaithfulness. Thus, efficiently extracting and organizing answer-relevant clues from large-scale documents while reducing LLM reasoning costs remains a challenge for RAG. Inspired by Occam's razor, we frame LLM-centric retrieval as a MinMax optimization: maximizing the extraction of potential clues and reranking them for well-organization, while minimizing reasoning costs by truncating to the smallest sufficient clues set. In this paper, we propose CompSelect, a Compact clue Selection mechanism for LLM-centric RAG, consisting of a clue extractor, a reranker, and a truncator. (1) The clue extractor first uses answer-containing sentences as fine-tuning targets, aiming to extract sufficient potential clues; (2) The reranker is trained to prioritize effective clues based on real LLM feedback; (3) The truncator uses the truncated text containing the minimum sufficient clues for answering the question as fine-tuning targets, thereby enabling efficient RAG reasoning. Experiments on three QA datasets show that CompSelect improves QA performance by approximately 11\% and reduces Total Latency and Online Latency by approximately 17\% and 67\% compared to various baseline methods on both LLaMA3 and Qwen3. Further analysis confirms its robustness to unreliable retrieval and generalization across different scenarios, offering a scalable and cost-efficient solution for web-scale RAG applications.