通过截断思维链审计，揭示LLM教育导师是否存在"答案驱动"的推理偏差，提升AI教学可信度。
arXiv:2607.04572v1 Announce Type: new Abstract: Large language model (LLM) tutors often produce fluent step-by-step explanations, but a correct and pe…
用逻辑网格谜题精准探测大模型推理中的隐性偏见，一场理性与偏好的博弈实验
arXiv:2511.06160v2 Announce Type: replace Abstract: While recent safety guardrails effectively suppress overtly biased outputs, subtler forms of socia…
研究发现LLM能轻易纠正别人错误，却对自己推理错误“睁一只眼闭一只眼”，揭示自我纠正的认知错觉。
arXiv:2606.05976v1 Announce Type: new Abstract: Recent work shows that LLM agents struggle to correct errors in their own reasoning traces yet show ma…
LLM多智能体系统执行无误却规划失效？这篇论文提出“认知校准”新方法，破解隐藏的推理偏差。
arXiv:2605.23414v1 Announce Type: new Abstract: LLM-based multi-agent systems can fail even when planned actions are executed correctly because agents…