让AI更懂学术分歧，自动生成有共识的论文评审报告
Bridging Social Psychology and LLM Reasoning: Conflict-Aware Meta-Review Generation via Cognitive Alignment
- 用双进程认知模型模拟人类思考，分三步处理评审意见冲突
- 评审情感一致性提升19.47%，内容一致性提高12.95%
- 适合需要公正、综合多专家意见的期刊审稿与科研评估
学术论文投稿量激增已使传统同行评审系统不堪重负，亟需智能化自动化手段以维持科学严谨性。尽管大语言模型（LLMs）在自动生成稿件评审方面展现潜力，但其在整合高风险元评审——需具备冲突感知推理与共识推导能力——方面仍显不足。现有方法难以有效处理不同意见间的矛盾，常引入锚定效应、从众偏见等认知偏差。为克服上述局限，我们提出认知对齐框架（CAF），一种基于卡尼曼双过程理论的双进程架构，将LLMs转化为自适应科学仲裁者。CAF通过三步认知流程：评审初始化、渐进式融合与认知对齐，实现对复杂评审意见的智能整合。实证验证表明，相较于现有基于LLM的方法，CAF在情感一致性上提升最高达19.47%，内容一致性最高提升12.95%。
原文摘要 · Abstract (English)
The rapid growth of scholarly submissions has overwhelmed traditional peer review systems, driving the need for intelligent automation to preserve scientific rigor. While large language models (LLMs) show promise in automating manuscript critiques, their ability to synthesize high-stakes meta-reviews, which require conflict-aware reasoning and consensus derivation, remains underdeveloped. Existing methods fail to effectively handle conflicting viewpoints within differing opinions, and often introduce additional cognitive biases, such as anchoring effects and conformity bias.To overcome these limitations, we propose the Cognitive Alignment Framework (CAF), a dual-process architecture that transforms LLMs into adaptive scientific arbitrators. By operationalizing Kahneman's dual-process theory, CAF introduces a three-step cognitive pipeline: review initialization, incremental integration, and cognitive alignment.Empirical validation shows that CAF outperforms existing LLM-based methods, with sentiment consistency gains reaching up to 19.47\% and content consistency improving by as much as 12.95\%.
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