Prépublication
· 2026 arXiv
EN
Predicting how the world responds to an action requires more than what can be seen: robots must account for environmental factors that are neither directly observable nor explicitly specified, from physical properties such as friction and mass to human-defined...
Chenyuan Zhou, Yining Hong et al.
#cs.RO
Prépublication
· 2026 arXiv
EN
Mixture-of-experts (MoE) models have become an important approach for scaling model parameters. Modern MoE architectures increasingly combine two design choices: fine-grained expert segmentation and shared experts. The former increases expressiveness by enabli...
Bingcong Li, Hao Ma et al.
#cs.LG
Prépublication
· 2026 arXiv
EN
Existing agent benchmarks typically evaluate agents on well-specified tasks defined by issue reports, specifications, or task tickets. Real-world customer delivery is different: forward-deployed engineers (FDEs) must clarify ambiguous requirements, work in liv...
Dongxu Yang, Hongda Yan et al.
#cs.AI
Prépublication
· 2026 arXiv
EN
Recent 3D world models increasingly use features from geometry foundation models (GFMs) as predictive world states, benefiting from their rich geometric priors. However, the view dependence of these features entangles scene dynamics with camera-induced variati...
Hongchang Liu, Jian Liu et al.
#cs.CV
Prépublication
· 2026 arXiv
EN
How can robots autonomously collect the contact-rich experience needed to learn dexterous manipulation? We present DSP: Dexterity from supervised play, a framework in which a robot learns a play policy from minutes of human play through behavior cloning, then...
Botao He, Yiming Dou et al.
#cs.RO
Prépublication
· 2026 arXiv
EN
Continuous diffusion language models (CDLMs) offer an alternative approach to parallel language generation; however, most existing studies directly use transformer architectures from either causal large language models (LLMs) or Diffusion Transformers (DiTs) f...
Weijian Luo, Yujian Chen et al.
#cs.AI
Prépublication
· 2026 arXiv
EN
Image editing models produce impressive results for individual edits, but their quality often deteriorates over multiple editing turns. Each editing turn introduces small distribution shifts that accumulate over successive edits, creating a gap between the inp...
Sara Dorfman, Nir Goren et al.
#cs.CV
Prépublication
· 2026 arXiv
EN
Weather prediction is undergoing an important shift, with data-driven AI models outperforming leading numerical weather prediction systems on average at a fraction of the computational cost. Yet, whether this success extends to extreme events remains contested...
Nathan Chalumeau, Xavier Renard et al.
#cs.LG
Prépublication
· 2026 arXiv
EN
The unimolecular dynamics of Criegee intermediates depend strongly on their internal energy, which can differ substantially between nascent atmospheric species and laboratory-prepared reactants. Here, the decomposition dynamics of {\it anti-}CH$_3$CHOO are inv...
Mitradip Das, Cangtao Yin et al.
#physics.chem-ph
Prépublication
· 2026 arXiv
EN
In the recent AI Model Intercomparison Project (AIMIP) benchmark, the ACE2.1-ERA5 emulator was shown to underestimate the true near-surface warming trend both during the training period and the out-of-sample extrapolation period. In this work, we present M-ACE...
Alexey Yermakov, Troy Arcomano et al.
#physics.ao-ph
Prépublication
· 2026 arXiv
EN
Urban hydrodynamic flood models typically rely on a surveyed terrain model, an explicit pipe drainage network, and rain gauge measurements, none of which were available for the inundation following Typhoon Matmo in My Dinh and Tay Mo, Hanoi, on 7 October 2025....
Minh Hoa Nguyen, Mai Anh Tran
#physics.ao-ph
Prépublication
· 2026 arXiv
EN
Our understanding of complex organic chemistry during the formation of stars and planets is shaped by a handful of cherry-picked sources with bright molecular emission lines. This observational bias impedes our assessment of how universal chemical complexity i...
Maria N. Drozdovskaya, Timea Csengeri et al.
#astro-ph.IM