arXiv Machine Learning
OnlineWM: Causality-Aware Active Online Learning for Effective World Modeling
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arXiv AI
Sep 10
Earth System World Model for What-If Simulations: A Case Study for Terrestrial Ecosystems
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.
arXiv Machine Learning
Aug 19
TabCausal: Pretraining Across Causal Environments for Tabular Causal Discovery
TabCausal is a causal discovery foundation model that learns to map datasets directly to causal graphs by pretraining across diverse causal environments. It uses a dynamic task construction strategy to expose the model to varied graph priors, mechanisms, noise models, dimensions, sample sizes, and intervention regimes, improving transferability from observational and mixed‑interventional data. On large synthetic benchmarks and a new protocol‑guided semantic benchmark, TabCausal outperforms many classical baselines and shows robust structure recovery, especially when interventional evidence is available.
arXiv Machine Learning
Jul 31
TAPO: Transition-Aware Policy Optimization for LLM Agents
arXiv:2607. 27973v1 Announce Type: new Abstract: Recently, Reinforcement Learning (RL) has emerged as a crucial paradigm for the post-training of Large Language Model (LLM) agents.
arXiv Machine Learning
Jun 25
Beyond One-Size-Fits-All: Diagnosis-Driven Online Reinforcement Learning with Offline Priors
arXiv:2606. 25527v1 Announce Type: new Abstract: Online reinforcement learning (RL) agents increasingly depend on knowledge acquired offline to achieve practical efficiency.
arXiv AI
Jul 22
DWM: Separating World Effects from Actions in Latent World Models
arXiv:2607. 18715v1 Announce Type: new Abstract: Latent world models underpin much of modern model-based control, yet current action-conditioned formulations supervise the next-latent transition with a single, undifferentiated target, forcing a monolithic learning signal to absorb every source of state change.
arXiv AI
Sep 1
CAER: Causal Action Effect Reweighting for World Model Training
arXiv:2608.30897v1 Announce Type: new Abstract: World models are becoming core infrastructure for embodied intelligence, with action-conditioned video generation providing controllable predictions of...