CMU researchers are presenting 194 papers at the Fourteenth International Conference on Learning Representations (ICLR 2026), held from April 23rd-April 27th at the Riocentro Convention and Event Center in Rio de Janeiro, Brazil. Here is a quick overview of the areas our researchers are working on: Here are our most frequent collaborator institutions: Table of Contents Oral Papers Poster Papers Applications Computer Vision Deep Learning General Machine Learning Optimization Reinforcement Learning Social Aspects Theory Uncategorized Oral Papers EditBench: Evaluating LLM Abilities to Perform Real-World Instructed Code Edits Authors: Wayne Chi (CMU), Valerie Chen (Carnegie Mellon University), Ryan Shar (Apple), Aditya Mittal (CMU, Carnegie Mellon University), Jenny Liang (School of Computer Science, Carnegie Mellon University), Wei-Lin Chiang (UC Berkeley / LMSYS), Anastasios Angelopoulos (University of California Berkeley), Ion Stoica (), Graham Neubig (Carnegie Mellon University), Ameet Talwalkar (University of California-Los Angeles), Chris Donahue (CMU / Google DeepMind) This work introduces EditBench, a new benchmark for testing how well AI models can edit existing code based on user instructions. Unlike prior benchmarks, it uses real-world coding tasks and contexts, including things like the surrounding code and cursor position. The benchmark includes 545 diverse problems, and results show that most models struggle—only a […]