ICLR

A Model Zoo on Phase Transitions in Neural Networks

Konstantin Schürholt, Léo Meynent, Yefan Zhou, Haiquan Lu, Yaoqing Yang, Damian Borth

机构 * Department of Computer Science University of St. Gallen, Switzerland（斯德哥尔摩大学计算机科学系） ； Department of Computer Science Dartmouth College, USA（达特茅斯学院计算机科学系） ； Department of Electrical and Computer Engineering National University of Singapore, Singapore（新加坡国立大学电子与计算机工程系） 纠错

Comments Accepted at the ICLR Workshop on Neural Network Weights as a New Data Modality 2025; Accepted in the Journal of Data-centric Machine Learning Research (DMLR) 2025; Dataset and code accessible at https://phasetransitions.modelzoos.cc/

From An LLM Swarm To A PDDL-Empowered HIVE: Planning Self-Executed Instructions In A Multi-Modal Jungle

Kaustubh Vyas, Damien Graux, Yijun Yang, Sébastien Montella, Chenxin Diao, Wendi Zhou, Pavlos Vougiouklis, Ruofei Lai, Yang Ren, Keshuang Li, Jeff Z. Pan

机构 * Huawei Technologies Ltd., UK（华为技术有限公司，英国） ； University of Edinburgh, UK（爱丁堡大学，英国） 纠错

Comments Published as a conference paper at ICLR 2025

Rare-to-Frequent: Unlocking Compositional Generation Power of Diffusion Models on Rare Concepts with LLM Guidance

Dongmin Park, Sebin Kim, Taehong Moon, Minkyu Kim, Kangwook Lee, Jaewoong Cho

机构 * KRAFTON ； Seoul National University（首尔国立大学） ； University of Wisconsin-Madison（威斯康星大学麦迪逊分校） 纠错

Comments ICLR 2025 (spotlight)

Neural Context Flows for Meta-Learning of Dynamical Systems

Roussel Desmond Nzoyem, David A. W. Barton, Tom Deakin

机构 * School of Computer Science University of Bristol（计算机科学学院英国布里斯托尔大学） ； School of Engineering Mathematics and Technology University of Bristol（工程数学与技术学院英国布里斯托尔大学） 纠错

Comments Accepted as a conference paper at ICLR 2025

AnveshanaAI: A Multimodal Platform for Adaptive AI/ML Education through Automated Question Generation and Interactive Assessment

Rakesh Thakur, Diksha Khandelwal, Shreya Tiwari

Comments 11 pages, 12 figures. Under review as a conference paper at ICLR 2026. Preprint version posted on arXiv

Unsupervised Conformal Inference: Bootstrapping and Alignment to Control LLM Uncertainty

Lingyou Pang, Lei Huang, Jianyu Lin, Tianyu Wang, Akira Horiguchi, Alexander Aue, Carey E. Priebe

机构 * Department of Statistics, University of California, Davis（加州大学戴维斯分校统计系） ； Department of Applied Mathematics and Statistics, Johns Hopkins University（约翰霍普金斯大学应用数学与统计学系） 纠错

Comments 26 pages including appendix; 3 figures and 5 tables. Under review for ICLR 2026