ReContext
Models now support 128K context windows yet still fail to use evidence already sitting in the prompt. ReContext is a training-free inference harness for long-context reasoning that uses model-internal relevance signals to build a query-conditioned evidence pool, then replays it right before final generation while preserving the full original context.
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Memory framing of context: It treats the context as a memory store, the question as a retrieval cue, attention as cue-trace association, and replay as trace reactivation, a clean cognitive analogy that drives the design.
No training, no pruning: There is no fine-tuning, no external memory, and no pruning of the original context, so the method drops into existing models without changing weights or losing information.
Consistent gains across backbones: Across eight 128K long-context datasets it improves evidence utilization on Qwen3-4B, Qwen3-8B, and Llama3-8B, taking the best average rank on all three, with public code.
Why it matters: It targets the real long-context failure, using evidence that is already present, and fixes it at inference time, a cheap and general lever for reasoning over long prompts.