ReContext: Training-Free Long-Context Reasoning Framework
dair_ai · x · 2026-07-07
A new paper introduces ReContext, a training-free framework for long-context reasoning. It leverages the model's internal relevance signals to construct a query-conditioned evidence pool, which is replayed before final generation while preserving the full original context—requiring no training, external memory, or pruning. The author draws parallels between context and memory storage, questions and retrieval cues, attention and cue-trace associations, and replay and trace reactivation.
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