MemReread: Enhancing Agentic Long-Context Reasoning via Memory-Guided Rereading
Summary
MemReread introduces a method for long-context reasoning that avoids intermediate retrieval by decomposing questions and rereading text to recover discarded information, achieving linear time complexity. It outperforms baseline frameworks on long-context reasoning tasks.
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Paper page - MemReread: Enhancing Agentic Long-Context Reasoning via Memory-Guided Rereading
Source: https://huggingface.co/papers/2605.10268
Abstract
MemReread addresses long-context reasoning challenges by avoiding intermediate retrieval and employing question decomposition with rereading to recover discarded information, maintaining linear time complexity.
To tacklelong-context reasoningtasks without the quadratic complexity of standardattention mechanisms, approaches based onagent memoryhave emerged, which typically maintain a dynamically updated memory when linearly processing document chunks. To mitigate the potential loss of latent evidence in this memorize-while-reading paradigm, recent works have integratedretrieval modulesthat allow agents to recall information previously discarded duringmemory overwriting. However, retrieval-based recall suffers from both evidence loss during memory formation and interference induced by invalid queries. To overcome these limitations, we propose MemReread. Built uponstreaming reading, MemReread circumvents intermediate retrieval. It triggersquestion decompositionandrereadingwhen the final memory is insufficient, enabling the recovery of indirect facts that were prematurely discarded. This design supports non-linear reasoning while preserving the inherent logical flow of document comprehension. To further enhance practicality, we introduce areinforcement learningframework that enhanceslength extrapolationcapability while dynamically determining the number ofrereadingpasses based on task complexity, thereby flexibly controlling computational overhead. Extensive experiments demonstrate that MemReread consistently outperforms baseline frameworks onlong-context reasoningtasks, while maintaining linear time complexity with respect to context length.
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