New Framework for Bias-Resilient LLM Reasoning in Long Documents

A new study proposes a structured framework to improve large language model (LLM) reasoning on long documents by reducing cumulative bias, omission errors, and over-generalization. The method involves dividing texts into semantically coherent chunks for parallel processing, then consolidating interpretations with evidence anchoring and prioritization. This approach aims to prevent early or dominant concepts from overshadowing less visible but meaningful interpretations, and to reduce redundancy and conceptual drift when merging independent outputs. The paper is available on arXiv under ID 2605.20194.