arXiv:2606. 19302v1 Announce Type: cross Abstract: As deep learning for physical systems continues to grow in popularity, efforts to improve generalizability have primarily focused on designing architectures that embed physical constraints.
As deep learning for physical systems continues to grow in popularity, efforts to improve generalizability have primarily focused on designing architectures that embed physical constraints. However, for machine-learning surrogate climate models (emulators), we show that the low structural diversity in existing scenarios commonly used to generate training data places a ceiling on predictive skill.
arXiv:2609.23753v1 Announce Type: cross Abstract: Generative world models aim to predict future states conditioned on actions, where action controllability is fundamental for reliable dynamics modeli...
arXiv:2605.16929v2 Announce Type: replace Abstract: Global climate models are essential tools to simulate past and potential future pathways of climate change, as well as associated climate impacts....
The paper investigates how to collect data for recalibrating a misspecified simulator that models a partially observed real-world system as a fully observed Markovian process. It compares four data‑collection strategies: never recalibrating, passive on‑policy recalibration, continuous exploration across all units, and randomized experimentation on a sample of units. Theoretical analysis and a vending‑machine simulation show that experimentation can outperform passive adaptation when drift is invisible to on‑policy data or when recalibration lags behind ongoing drift, while exploration is preferable when trying worse actions is costly and dynamics may not have changed.
The paper introduces an action‑conditioned world‑modeling framework that turns Earth‑system simulator trajectories into training data for controllable state‑transition learning. By pretraining on naturally observed state changes as implicit action supervision and using masked response learning, the model can infer unobserved variables and learn coupled system dependencies. Experiments on ecosystem dynamics across six global regions demonstrate that the model maintains long‑horizon emulation accuracy while enabling structural interventions and coherent responses in coupled ecosystem‑cycle variables.
The paper demonstrates that learned simulators can fail in two distinct ways when conditions change: long‑horizon drift due to accumulated errors and incorrect responses to interventions on physical parameters. By adding a symplectic integrator to preserve conservative dynamics, rollouts remain stable for up to 100× the training horizon, while encoding physical coupling via explicit linear factorization allows the model to generalize to unseen signs of that coupling. The study shows that stability and counterfactual generalization arise from separate structural choices, enabling designers to impose each property independently.
arXiv:2608. 05702v1 Announce Type: new Abstract: Scientific machine learning commonly validates models at the level of a subdomain, a benchmark split, or an explanation for one prediction.
arXiv:2610.31869v1 Announce Type: new Abstract: Covariate-aware time series foundation models (TSFMs) accept a future path for a control variable and return a forecast, inviting what-if queries. The...
arXiv:2610.09770v1 Announce Type: cross Abstract: Deep learning has made rapid advances in weather forecasting: autoregressive models trained on atmospheric reanalyses now rival dynamical models acro...
The paper introduces a method for analyzing how interventions in a prompt affect a language model’s reasoning by using behavioral counterfactuals. It trains a module to edit the residual stream so that the next-token distributions for a fixed response match between a source and a counterfactual prompt, outperforming standard activation reconstruction. The resulting attribution maps and visualizations help identify causally important locations for counterfactual recovery and guide hypothesis testing with activation patching, as demonstrated in a case study on Gemma-4-E4B-it and DeepSeek-R1-0528-Qwen3-8B.
arXiv:2610.34921v1 Announce Type: new Abstract: Physical reasoning underpins how embodied systems understand and act in the physical world, yet vision-language models (VLMs) remain unreliable on ques...