Prépublication
· 2026 arXiv
EN
Automated research with LLM agents has shown promising results in various domains. Through iterative refinement and scored verifiers, LLM agents are capable of discovering new ideas. There are different ways to scale automated research: let one agent iterate f...
Shangyin Tan, Ben Zhou et al.
#cs.AI
Prépublication
· 2026 arXiv
EN
3D object composition is pivotal to digital content creation and robot simulation, while also serving as a critical testbed for physical intelligence in foundation models. Previous methods either leverage large language models to directly predict object coordi...
Weichen Lyu, Chong Xia et al.
#cs.AI
Prépublication
· 2026 arXiv
EN
Understanding the social intelligence of large language models (LLMs) is increasingly important for applications involving human-AI interaction and multi-agent environments. Social intelligence comprises two aspects: cognitive intelligence, the ability to infe...
Ali Zamani, Soroush Esfahanian et al.
#cs.CL
Prépublication
· 2026 arXiv
EN
Conventional deep convection schemes remain a primary source of uncertainty in global climate models, yet replacing them with data-driven alternatives while maintaining physical consistency and numerical stability is an open challenge. We present a hybrid conf...
Xavier J. Levine, Griffin Mooers et al.
#physics.ao-ph
Prépublication
· 2026 arXiv
EN
Measurements of optical turbulence are essential in both ground-based astronomy and free-space optical communication systems contexts for multiple reasons including supporting and optimizing Adaptive Optics. Similarly, forecasting these parameters is highly va...
Camilo Weinberger, Elena Masciadri et al.
#astro-ph.IM
Prépublication
· 2026 arXiv
EN
High-resolution precipitation datasets are essential for weather forecasting, hydrological modeling, and climate risk assessment, yet observation-based gridded products often lack the spatial detail required for regional applications. Here we present PrecipCor...
Bipin Kumar, Rajib Chattopadhyay et al.
#physics.ao-ph
Prépublication
· 2026 arXiv
EN
Inferring climate fields at higher spatio-temporal resolutions from coarse representations is essential for accessing socioeconomic and environmental impacts. The downscaling of modelled climate fields faces a trade-off between data storage constraints, simula...
Yu Huang, Shangshang Yang et al.
#physics.ao-ph
Prépublication
· 2026 arXiv
EN
This work introduces a Process Additive Neural Network (PANN) framework to disentangle compensating biases in atmospheric models, where errors in different physical processes can offset each other and obscure their origins. Using a single-column model, each PA...
Yuiko Ichikawa, Edward Groot et al.
#physics.ao-ph
Prépublication
· 2026 arXiv
EN
Under anthropogenic greenhouse forcing, climate models project a decrease in year-to-year temperature variability in the mid-to-high latitudes and an increase in the tropics and subtropics. Here, we examine year-to-year temperature variability in the CESM Larg...
Rory Basinski-Ferris, Ian Eisenman
#physics.ao-ph
Prépublication
· 2026 arXiv
EN
We present the RemoTAP-Hybrid algorithm for global aerosol products retrieved from Multi-Angle Polarimeters (MAP), and apply it to one-year aerosol retrievals from the SPEXone MAP instrument. For the first time, Multiple Collaborated Neural Networks (MCNN) are...
Guangliang Fu, Sha Lu et al.
#physics.ao-ph
Prépublication
· 2026 arXiv
EN
Over 4 billion years, Earth's atmosphere has been transformed from a composition devoid of molecular oxygen (O$_2$) to one where O$_2$ is now 21% of our modern atmosphere by volume. With high quantities of O$_2$, an ozone (O$_3$) layer can form and partially s...
Akhil Kumar, Gregory J. Cooke
#astro-ph.EP
Prépublication
· 2026 arXiv
EN
Accurate forecasting of optical turbulence (OT) is useful to optimise telescope scheduling and maximising observing efficiency, as the performance of adaptive optics systems strongly depends on turbulence conditions. While various numerical weather prediction...
M. De Sepibus, E. Masciadri et al.
#astro-ph.IM