ReContext: Recursive Evidence Replay as LLM Harness for Long-Context Reasoning

Modern LLMs can technically process very long documents, but they often fail to actually use relevant details buried within them—a gap between having access to information and effectively reasoning over it. ReContext tackles this with a training-free method that uses the model's own internal attention signals to identify and "replay" the most relevant evidence before generating a final answer, without discarding the original context. Grounded in an associative-memory theoretical framework, it consistently improves performance across multiple model backbones on eight long-context benchmarks. This is useful for applications like legal document review, research synthesis, or long conversation analysis. Authors: Yanjun Zhao, Ruizhong Qiu, Tianxin Wei, Yuanchen Bei, Zhining Liu, Lingjie Chen, Ismini Lourentzou, Hanghang Tong, Jingrui He Paper: https://arxiv.org/abs/2607.02509v1