Chenyan Zhao

Thinking in a Crowd: How Auxiliary Information Shapes LLM Reasoning

The capacity of Large Language Models (LLMs) to reason is fundamental to their application in complex, knowledge-intensive domains. In real-world scenarios, LLMs are often augmented with external information that can be helpful, irrelevant, or even misleading. This paper investigates the causal impa…