recontext-evidence-replay

name	recontext-evidence-replay
description	Recursive Evidence Replay as LLM Harness for Long-Context Reasoning. Training-free inference method that uses model-internal relevance signals to construct query-conditioned evidence pool and replays before final generation, improving long-context reasoning without training, external memory, or context pruning.
trigger	recursive evidence replay, ReContext, long context reasoning, evidence retrieval, context utilization
category	ai_collection/collection/skills

ReContext: Recursive Evidence Replay for Long-Context Reasoning

Overview

RECONTEXT (Recursive Evidence Replay as LLM Harness for Long-Context Reasoning) is a training-free inference method that separates evidence organization from answer generation to improve LLM long-context reasoning.

Core Mechanism

1. Evidence Pool Construction: Use model-internal relevance signals to identify query-relevant passages from the full context
2. Recursive Replay: Replay the evidence pool before final generation while preserving the original full context
3. Associative Memory Theory:
   • Context = memory store
   • Question = retrieval cue
   • Attention = cue-trace association
   • Replay = trace reactivation

Implementation Steps

1. Feed the full context + query to the LLM
2. Extract relevance signals from model attention weights or internal states
3. Construct a query-conditioned evidence pool from the most relevant passages
4. Prepend this evidence pool to the original prompt and generate the final answer
5. Optionally repeat the process (recursive) for multi-hop reasoning

Key Benefits

• Training-free: No model fine-tuning required
• No external memory: Works with existing model parameters
• No context pruning: Preserves full original context
• Consistent improvement: Works across Qwen3-4B, Qwen3-8B, and Llama3-8B
• 128K context: Tested on long-context datasets

Activation Keywords

recursive evidence replay, long context reasoning, evidence pool, context utilization, attention retrieval, associative memory

Source

arXiv: 2607.02509 (2026-07-02)