让AI锐评本届 NeurIPS 2025 最佳论文会得到什么结果？ | 锦秋AI实验室

Core Insights - The article discusses the evaluation of AI models in the context of the NeurIPS 2025 conference, focusing on how AI can assess research papers through a blind review process [2][10]. Group 1: Evaluation Methodology - The evaluation involved several AI models, including GPT5, Claude 4.5, and others, to conduct blind reviews of selected NeurIPS award-winning papers [7][8]. - Three complementary assessment scenarios were designed: full paper review, abstract-only review, and adversarial review to test the models' sensitivity to different framing [9][10]. Group 2: AI Review Outcomes - In the full paper review, the paper "Gated Attention for Large Language Models" received high scores, with GPT5 rating it as a Best Paper [13][16]. - The paper "1000 Layer Networks for Self-Supervised RL" also received favorable evaluations, with GPT5 giving it a score of 8.3 and recommending it for a poster presentation [21][43]. - The paper "Does Reinforcement Learning Really Incentivize Reasoning Capacity in LLMs Beyond the Base Model?" was rated highly by multiple models, with Minimax even suggesting it as a Best Paper [28][46]. Group 3: Summary of Findings - The AI models generally agreed on the quality of the papers, with most scoring above 8 for technical correctness and significance [30][32]. - However, in adversarial reviews, the same papers faced significant criticism, leading to lower scores and recommendations for rejection, highlighting the models' varying perspectives based on the review context [55][57]. - The evaluations revealed a divergence between human and AI assessments, particularly in the adversarial setting, where AI reviewers were more critical [55][60].