The paper introduces a schema‑adaptive action‑conditioned Joint‑Embedding Predictive Architecture (SAAC‑JEPA) for cross‑machine CNC transfer when only a subset of sensors overlap between source and target machines. Experiments show that pretraining does not improve source‑only forecasting, but a carefully selected action‑conditioned JEPA model achieves a zero‑shot RMSE of 0.546 on the target, outperforming persistence but falling short of certain baseline models. Ablation studies reveal that adding RevIN improves RMSE but harms calibration, and limited post‑lock adaptation can further reduce error.
By Ayoub Louaye Bouaziz, Matthieu Ostertag, Anton Demasles
arXiv:2608. 14456v1 Announce Type: new Abstract: Short-horizon forecasting of fine particulate matter (PM2.
By Shahab Band, Hamed Mohammadi
arXiv:2607. 27017v1 Announce Type: new Abstract: A central premise of latent world models is that predicting the future forces a representation to internalize the physics of its environment.
By Kaizhen Tan (New York University, Carnegie Mellon University), Xin Xu (Carnegie Mellon University), Siru Tao (Carnegie Mellon University), Hanzhe Hong (Carnegie Mellon University), Yang Feng (Columbia University), Heqing Du (Columbia University)
arXiv:2512.03400v2 Announce Type: replace-cross
Abstract: We study how explicit world-modeling objectives affect the internal representations and downstream capability of Transformers, using Rubik's...
By Prakhar Gupta, Henry Conklin, Sarah-Jane Leslie, Andrew Lee
arXiv:2609.13840v1 Announce Type: new
Abstract: A contract-logistics spare-parts operator is paid on order-level service: an order counts only if every requested line is fulfilled, yet forecasters ar...
By Joo Ern Chin, Shih-Fen Cheng, Aldy Gunawan
arXiv:2511. 02748v2 Announce Type: replace-cross Abstract: We argue that sixth-generation (6G) intelligence is not fluent token prediction but the capacity to imagine and choose -- to simulate future scenarios, weigh trade-offs, and act with calibrated uncertainty.
By Farhad Rezazadeh, Amir Ashtari Gargari, Hatim Chergui, Sandra Lagen, Merouane Debbah, Houbing Song, Lingjia Liu
arXiv:2607. 04978v1 Announce Type: new Abstract: Joint-Embedding Predictive Architectures (JEPAs) underpin a growing family of latent world models for control from raw pixels, but every existing JEPA world model commits at training time to a single inference paradigm: either trajectory optimisation in a learned dynamics model, or direct behaviour cloning.
By Ruslan Rakhimov, George Bredis, Yuriy Maksyuta, Daniil Gavrilov
JEPA-x is a cross‑predictive physics grounding method that aligns visual latent dynamics with privileged physical trajectories. By treating visual observations and physical states as two views of the same action‑conditioned trajectory and sharing a predictor, it forces the model to learn a common transition rule for both modalities. The physical branch is only used during training, so deployment incurs no extra cost, and the approach significantly reduces rollout drift and boosts control success across a multi‑task suite.
By Kehan Wen, Ziming Li, Siyuan Luo, Fan Shi
arXiv:2606. 16076v1 Announce Type: cross Abstract: Multivariate forecasting in physical systems requires models that predict coupled temporal variables while preserving meaningful state evolution.
By Weizhi Nie, Weichao Liu, Honglin Guo, Yuting Su
Joint-Embedding Predictive Architectures (JEPAs) underpin a growing family of latent world models for control from raw pixels, but every existing JEPA world model commits at training time to a single inference paradigm: either trajectory optimisation in a learned dynamics model, or direct behaviour cloning. A single checkpoint that serves both would defer this choice to inference, when deployment constraints (rollout cost, observation accessibility) determine which path wins.
arXiv:2609.07299v1 Announce Type: new
Abstract: Learned world models typically assume that observations arrive synchronously, an abstraction inherited from simulators that return a complete state vec...
By Akash Anand, Abhay Anand, Yash Vishe
District heating energy hubs require reliable heat load forecasts for efficient operational scheduling. Conventional forecasting workflows train system-specific models on historical data, which can become burdensome when networks change through new consumers, retrofits, or changing operating regimes.