ICML2026 论文速递

Yann LeCun (@ylecun) 转发了 Surya Ganguli (@SuryaGanguli) 的帖子： Check out all the amazing work from our @SimonsFdn Collaboration on the Physics of Learning and Neural Computation (https://t.co/TfOKlQxCrE) presented at the main meeting of @ICMLconf #ICML2026 Tuesday Efficient Learning of Compositional Targets with Hierarchical Spectral Methods,Hugo Tabanelli, Yatin Dandi, Luca Pesce, and Florent Krzakala https://t.co/wix8AkXVcl CompleteP for RL: Maintaining Feature Learning When Scaling Deep Reinforcement Learning M Ganesh Kumar, Adam Lee, Blake Bordelon , Cengiz Pehlevan https://t.co/Ox6AgFt5LU Universal One-third Time Scaling in Learning Peaked Distributions Yizhou Liu, Ziming Liu, Cengiz Pehlevan, Jeff Gore https://t.co/QzIQf5ANde Wednesday A Noise Sensitivity Exponent Controls Large Statistical-to-Computational Gaps in Single- and Multi-Index Models, Leonardo Defilippis, Florent Krzakala, Bruno Loureiro, Antoine Maillard https://t.co/x4zZVfTHP9 Single-Head Attention in High Dimensions: A Theory of Generalization, Weights Spectra, and Scaling Laws Fabrizio Boncoraglio, Vittorio Erba, Emanuele Troiani, Yizhou Xu, Florent Krzakala, Lenka Zdeborová https://t.co/CKWt5qGyOR A Solvable High-Dimensional Model Where Nonlinear Autoencoders Learn Structure Invisible to PCA While Test Loss Misaligns With Generalization Vicente Mendes, Lorenzo Bardone, Cédric Koller, Jorge Medina Moreira, Vittorio Erba ⋅ Emanuele Troiani, Lenka Zdeborova https://t.co/rgDY2ieGJR Deep networks learn to parse uniform-depth context-free languages from local statistics Jack T. Parley, Francesco Cagnetta, Matthieu Wyart https://t.co/yrqjxZTrAp Demystifying LLM-as-a-Judge: Analytically Tractable Model for Inference-Time Scaling Indranil Halder, Cengiz Pehlevan https://t.co/ERCgkiOyIt On the Existence of Consistent Adversarial Attacks in High-Dimensional Linear Classification Matteo Vilucchio, Lenka Zdeborova, Bruno Loureiro https://t.co/KDpelqRLla Robust Stochastic Gradient Posterior Sampling with Lattice Based Discretisation Zier Mensch, Lars Holdijk, Samuel Duffield, Maxwell Aifer, Patrick Coles, Max Welling, Miranda C. N. Cheng https://t.co/ikqMhKQOfJ Teaching Models to Teach Themselves: Reasoning at the Edge of Learnability Shobhita Sundaram, John Quan, Ariel Kwiatkowski, Kartik Ahuja, Yann Ollivier, Julia Kempe https://t.co/wjgSrAbJ25 Thursday Deriving Neural Scaling Laws from the Statistics of Natural Language Francesco Cagnetta ⋅ Allan Raventos ⋅ Surya Ganguli ⋅ Matthieu Wyart https://t.co/b6nKYqNulh Symmetry in language statistics shapes the geometry of model representations Dhruva Karkada, Daniel Korchinski, Andres Nava, Matthieu Wyart, Yasaman Bahri https://t.co/Y58fJt9qmk A Random Matrix Perspective on the Consistency of Diffusion Models Binxu Wang, Jacob A Zavatone-Veth, Cengiz Pehlevan https://t.co/snM0EAxv3Q Hyperparameter Transfer with Mixture-of-Expert Layers Tianze Jiang, Blake Bordelon, Cengiz Pehlevan, Boris Hanin https://t.co/dIe32NuQTS Analytic Bijections for Smooth and Interpretable Normalizing Flows Mathis Gerdes, Miranda C. N. Cheng https://t.co/kWCw1EHGn4 Efficient RL Training for LLMs with Experience Replay Charles Arnal, Vivien Cabannnes, Taco Cohen, Julia Kempe, Remi Munos https://t.co/EjmNErAFpC Embedding Trust: Semantic Isotropy Predicts Nonfactuality in Long-Form Text Generation Dhrupad Bhardwaj, Julia Kempe, Tim G. J. Rudner https://t.co/MknrTodSaX What Characterizes Effective Reasoning? Revisiting Length, Review, and Structure of CoT Yunzhen Feng, Julia Kempe, Cheng Zhang, Parag Jain, Anthony Hartshorn https://t.co/S18yTP5fy5 From Kepler to Newton: Inductive Biases Guide Learned World Models in Transformers Ziming Liu, Surya Ganguli, Andreas Tolias https://t.co/H7eFnYtYQC