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The paper compares five LLM-based review paradigms across three input representations and three LLMs on 232 ICLR 2025 submissions, finding that direct rating matches comparative ranking after recalibration with far fewer API calls, abstract-only input beats full-paper summaries, and human scores still predict citations better than any LLM paradigm except Group Rating.
A call for 2 emergency reviewers for ICLR, requiring expertise in RL, agent memory, and/or information theory, with reviews due by Nov 2 AoE.
ICLR 2027 continues to issue desk rejects for submissions, with the community being thanked for helping guard against low-quality AI-generated papers flooding the review process.
研究者Peter Henderson在ICLR审稿中表达对AI冲击学术同行评审体系的担忧，指出大量疑似AI生成的投稿正导致审稿质量下降与评审系统的死亡螺旋。
A Hacker News discussion帖吐槽 ICLR 审稿中泛滥的晦涩 AI 写作，并附上一段匿名论文的典型模糊表述，呼吁社区讨论如何在不简单指责“AI 写作”的前提下批评这类糟糕行文的实质问题。
A researcher shares their frustration after being desk rejected from ICLR because half a sentence spilled onto page 10, criticizing the process and questioning the value of the provided LLM feedback for catching formatting errors.
An ICLR reviewer reports that all three assigned papers appear to be unfiltered Claude-generated slop sharing the same core idea of comparing predictor errors via paired differences to derive tighter bounds, raising concerns about automated paper-generation at top conferences.
A discussion post asking the community about their experience reviewing submissions for ICLR, focusing on the rising volume of AI-generated papers and the growing difficulty of detecting AI-written text and 'slop' submissions in OpenReview.
该论文提出Station——一个多智能体开源环境，用于评估AI代理进行开放式科学发现的能力。通过添加Supervisor和Meta Reflection机制，Station在重现ICLR oral论文发现的基准上平均达到62.7%，显著超越Codex Multiagent-v2（15.4%）和AI Scientist-v2（14.4-20.6%）。
A new paper introducing a benchmark for intuitive visual reasoning ("humanity's sixth sense") to evaluate the perceptual intuition capabilities of multimodal AI models.
ICLR 2027 faces a de-anonymization incident where submissions were exposed to program committee members, raising privacy and security concerns for academic conferences.
The author expresses concerns about ICLR's review policy that requires individuals to serve as reviewers if they appear on 3 or more papers, regardless of their qualifications.
A Twitter user advises paper authors and conference organizers to cancel ICLR 2027 and implement policies to restore the prestige of top-tier publications.
The author shares personal reminders for conducting AI research, emphasizing curiosity over publication pressure, insights over benchmarks, and thorough investigation over rushed papers.
A tweet humorously comments on the over 60,000 paper submissions to ICLR this year, questioning the overall quality of the submissions.
A tweet criticizing an ICLR 2026 paper, pointing out that an open-sourced paper achieved excellent results but actually used test set labels to select parameters, questioning the rigor of top conference peer review.
ICLR 2027 has set its full paper deadline before NeurIPS 2026 decisions are announced, potentially disadvantaging papers that improved after NeurIPS rejection.
Fair Reinforcement Learning introduces Democratic Alignment to incorporate multiple competing value sets from different agents, overcoming traditional RLHF limitations, and achieves orders of magnitude faster optimization via a black-box policy wrapper.
This article presents a dataset and analysis pipeline for ICLR 2026 accepted papers, extracting institutional affiliations from PDF title blocks to create a clean dataset and publication-ready treemap visualizations.
MIT CSAIL researchers introduce RLCR, a method using Brier scores in reinforcement learning to train AI models to output calibrated confidence estimates, significantly reducing overconfidence without sacrificing accuracy.