AI RESEARCH
Parallel LLM Reasoning for Bias-Resilient, Robust Conceptual Abstraction
arXiv CS.LG
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ArXi:2605.20194v1 Announce Type: cross Large language models (LLMs) have been increasingly used to analyze text. However, they are often plagued with contextual reasoning limitations when analyzing long documents. When long documents are processed sequentially, early or dominant concepts can overshadow less visible but meaningful interpretations, leading to cumulative analytical bias, omission error, and over-generalization. Additionally, independently generated outputs are often merged without systematic grounding,