arXiv:2605. 00972v2 Announce Type: replace-cross Abstract: Earth system science is producing increasingly large, high-dimensional datasets from both physics-based and AI-driven models.
By Nihanth W. Cherukuru, Matt Rehme, Kirsten J. Mayer, David John Gagne, John Schreck, John Clyne, Charlie Becker
arXiv:2606. 26535v1 Announce Type: cross Abstract: Current VLM evaluations often conflate language priors with genuine spatial reasoning.
By Zhixing Li, Yinan Yu
Current VLM evaluations often conflate language priors with genuine spatial reasoning. To address this, we introduce CRISP, a novel structural-diagnostic evaluation paradigm that assesses visual spatial intelligence through consistency, the alignment between implicit perception and explicit reasoning.
arXiv:2605.04515v2 Announce Type: replace
Abstract: Video Large Language Models (Video-LLMs) excel in general video understanding but often base physical judgments on event expectations rather than o...
By Zicheng Zhao, Chaofan Gan, Shijie Li, Weiyao Lin
HiPhy introduces a hierarchical reinforcement learning framework for video generation that enforces physical laws at both local and global levels. It addresses the challenge of multi-principle interactions—such as buoyancy and fluid dynamics occurring simultaneously—by ensuring each principle’s temporal dynamics and the overall scene’s coherence. The authors also provide a 50K-prompt dataset and the MultiPhyBench benchmark, demonstrating that HiPhy outperforms existing methods, especially in scenes with multiple concurrent physical principles.
By Tahira Kazimi, Shubhankar Borse, Munawar Hayat, Fatih Porikli, Pinar Yanardag
arXiv:2602. 06205v2 Announce Type: replace-cross Abstract: The Platonic Representation Hypothesis suggests that independently trained neural networks converge to increasingly similar latent spaces.
By Akshit Achara, Tatiana Gaintseva, Mateo Mahaut, Pritish Chakraborty, Viktor Stenby Johansson, Melih Barsbey, Emanuele Rodol\`a, Donato Crisostomi