Reader-Oriented Evidence Reshaping: Improving RAG from the Reader’s Perspective

Retrieval-Augmented Generation (RAG) enhances large language models by incorporating external evidence. However, existing post-retrieval processing methods remain largely evidence-centric approaches that tend to ignore how the target Reader interprets and utilizes the resulting representation. Insights from prompt engineering suggest that different representations of the same information lead to significant variations in model outputs. Therefore, we propose Reader-Oriented Evidence Reshaping (ROER), a framework designed to explicitly align the representation of evidence with the intrinsic reasoning preferences of the target Reader. Instead of relying on a fixed processing strategy, our framework represents evidence organization as executable Evidence Organization Programs (EOPs) and evolves candidate programs guided by feedback from the target Reader. These evolved programs dynamically determine how retrieved evidence is selected, structured, and presented. To support this, we introduce a multi-view retrieval module that integrates evidence from multiple perspectives, supplying diverse evolutionary materials for subsequent evidence reshaping. Experiments on CosmosQA, QuALITY, and LongBench-v2 demonstrate that ROER delivers consistent improvements over competitive baselines across short-context, document-level, and ultra-long-context reasoning settings. In particular, ROER achieves a 21.21% relative accuracy improvement over the strongest baseline in the ultra-long-context setting, highlighting its particular strength in complex long-context reasoning.