Parallel LLM Reasoning for Bias-Resilient, Robust Conceptual Abstraction

This study proposes a structured framework to improve LLM reasoning when analyzing long documents, addressing issues like contextual bias and omission error. It combines parallel chunk-level processing with evidence-anchored consolidation to generate more robust and bias-resilient conceptual abstractions.