iclr

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.

A PhD student at ICLR seeks practical tactics to overcome social anxiety and break into existing conversation groups without generic confidence advice.

Two ICLR 2026 papers show how small RL-trained agents outperform frontier models on machine-learning engineering tasks and how MLE-Smith automatically scales MLE workloads.

AutoFigure is an open-source system for generating and refining editable, publication-ready scientific diagrams, accepted to ICLR 2026.