ReContext: Recursive Evidence Replay as LLM Harness for Long-Context Reasoning
Researchers propose RECONTEXT, a training-free inference method to improve long-context reasoning in large language models. RECONTEXT uses model-internal relevance signals to construct and replay relevant evidence.
Understanding and reasoning over long contexts has become a key requirement for deploying large language models (LLMs) in realistic applications. Although recent LLMs support increasingly long context windows, they often fail to use relevant evidence that is already present in the input, revealing a gap between context access and effective context utilization. In this work, we propose Recursive Evidence Replay as LLM Harness for Long-Context Reasoning (RECONTEXT), a training-free inference method for improving long-context reasoning. RECONTEXT uses model-internal relevance signals to construct
Source: ReContext: Recursive Evidence Replay as LLM Harness for Long-Context Reasoning. Read the full piece at the source.
Summary and analysis generated by AI (groq). Always verify against the original sources.
Measuring the Economic Effects of AI - Economic Innovation Group
