arXiv Computer Vision
Reverberation: Learning the Latencies Before Forecasting Trajectories
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arXiv AI
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Self-Supervised Latent Action-Conditioned World Modeling for Time Series
The paper introduces ACTS-WM, a self‑supervised framework that models latent actions underlying time‑series transitions. It combines a Joint‑Embedding Predictive Architecture encoder with an action‑conditioned world model to predict future representations conditioned on inferred latent actions and task information. Experiments on 22 datasets across four tasks show that ACTS‑WM improves downstream performance compared to pretrained JEPA‑based encoders.
arXiv Machine Learning
Oct 3
Variational Streaming Flow: Probabilistic Forecasting in Physical Time
Variational Streaming Flow (VSF) extends the efficient Streaming Flow (SF) framework by learning a latent distribution conditioned on system dynamics, enabling probabilistic forecasting in physical time. Unlike SF’s deterministic velocity field, VSF produces multiple plausible future trajectories, improving predictive accuracy and distributional fidelity across deterministic and stochastic dynamical systems. The method supports long‑horizon rollouts over 1,000 steps, handles bifurcating dynamics, and can be integrated as a plug‑and‑play predictor into Joint‑Embedding Predictive Architecture (JEPA) world models to enhance navigation, motion planning, and manipulation tasks.
arXiv AI
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Learning When to Listen: Gated Affect Fusion for Human Motion Prediction
arXiv:2607. 00296v1 Announce Type: cross Abstract: Human motion forecasting in unconstrained real-world videos remains challenging due to the ambiguity of future behaviors and the presence of noisy multimodal observations.
arXiv Machine Learning
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TimeWorld: Towards Action-Conditioned Multimodal World Models for Time Series Forecasting
TimeWorld introduces a plug‑in framework for action‑conditioned multimodal world models in time‑series forecasting. It builds on pretrained time‑series foundation models (TSFMs) and adds a Prototype‑Guided Latent Modulation module to embed textual actions into temporal dynamics, along with an Action Contrastive Learning component to sharpen action‑specific response modeling. The authors also present TSWM‑Bench, a cross‑domain benchmark of seven real‑world datasets with textual interventions, showing that simple text‑prefix adaptations outperform conventional multimodal forecasters and that pretrained TSFMs already encode useful world knowledge for action‑conditioned predictions.
arXiv Machine Learning
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WorldTS: World Modeling for Multimodal Covariate-aware Time Series Forecasting
WorldTS is a new forecasting framework that models latent dynamics conditioned on multimodal covariates to improve time‑series prediction. It uses a two‑stage training process: first learning latent state dynamics from historical data and covariates, then training a decoder to map predicted latent states back to future observations. Experiments on 21 real‑world datasets demonstrate the effectiveness of this approach.
arXiv Machine Learning
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Autoregressive One-Step Generative Modeling for Dynamical System Forecasting
arXiv:2605. 05540v2 Announce Type: replace Abstract: Fast surrogate modeling for high-dimensional physical dynamics requires more than low short-term error: useful models must roll out efficiently while preserving the statistical structure of long trajectories.