The paper introduces SIHA, a method for jointly recovering hidden-agent trajectories and their interaction structures in multi-agent dynamics. It tackles the circular dependency between trajectory reconstruction and structural inference by combining a structure‑agnostic initialization with iterative, structure‑guided refinement using Neural Relational Inference and multi‑strength structural attention. Experiments on benchmark dynamical systems and motion‑capture data show that SIHA improves both visible‑to‑visible structural inference and hidden‑state reconstruction, leading to better future predictions.
arXiv:2608. 06994v1 Announce Type: cross Abstract: World Action Models (WAMs) aim to construct a unified architecture capable of understanding world state evolution and guiding to generative motion planning.
arXiv:2607. 15880v1 Announce Type: cross Abstract: Imitation Learning aims to learn skills from extensive observations and demonstrations for robots, so it suffers from data scarcity and environment generalization.
The paper studies when joint-embedding predictive architectures (JEPAs) can recover underlying causal states from high‑dimensional observations. It introduces a latent variable model where observations arise from causal states with action‑conditioned dynamics, and proposes an information‑theoretic objective that maximizes conditional likelihood while preserving state entropy. The authors prove identifiability conditions—particularly sufficient action‑induced variation—and instantiate the objective as an action‑modulated Gaussian additive‑noise model (A‑JEPA), demonstrating theoretical and empirical success in synthetic and visual benchmarks.
arXiv:2609.15189v1 Announce Type: new Abstract: Latent action models (LAMs) learn action representations from unlabeled videos by inferring latent actions from visual transitions and reconstructing f...
arXiv:2603. 22281v2 Announce Type: replace-cross Abstract: Recent progress in latent world models (e.
The paper introduces a Prior‑Guided Residual Flow Matching framework for 3D multi‑person motion prediction. It uses a Deterministic Coarse Prior to anchor kinematics and a Dynamic Cross‑Interaction mechanism to synchronize inter‑agent message passing during integration, thereby improving structural consistency and social context extraction. A decoupled joint‑motion architecture with bidirectional fusion further preserves fine‑grained kinematic coherence, achieving state‑of‑the‑art accuracy on several datasets.
arXiv:2609.00161v1 Announce Type: new Abstract: World models have made remarkable progress in action-conditioned future prediction for embodied agents, yet still struggle to model physically plausibl...
arXiv:2610.01742v1 Announce Type: cross Abstract: Equipping artificial agents with spatial intelligence requires a comprehensive generative prior over the dynamic 3D world. We propose World Motion Mo...
SplitJEPA is a new joint embedding predictive architecture that learns a latent state and its invariant and variant components directly in representation space, without reconstructing observations. The method proves that, under stationary Gaussian predictive dynamics and a full‑rank variation condition, it can identify the invariant and variant subspaces up to independent block‑wise isometries. Experiments on synthetic nonlinear systems and robotic manipulation tasks confirm the theoretical guarantees and demonstrate practical benefits for robustness and efficiency.
arXiv:2608. 20065v1 Announce Type: new Abstract: World models construct latent states that support prediction, planning, and reasoning about an underlying system.
The paper introduces BRAID, a hierarchical latent-variable model that generates multi-person human motion by explicitly modeling both group-level interaction dynamics and individual behavior conditioned on evolving group context. It treats social motion generation as a meta-transfer learning problem, learning shared interaction priors across datasets and adapting them to arbitrary context sets of observed people and joints. BRAID supports coherent generation under full, sparse, or partial observations and produces compact social-state vectors useful for downstream embodied-agent systems, with evaluations on social forecasting, tracking, in-filling, and response generation.
Contrastive World Models propose a new method for learning latent dynamics without pixel reconstruction. By replacing observation reconstruction with a Deep InfoMax-like objective that maximizes mutual information between state-action sequences and local patch features of future observations, the approach encourages state representations to retain predictive information while ignoring visually irrelevant details. Experiments show that this method matches existing baselines in simple settings and significantly outperforms them when distractors or natural video backgrounds are present, while also training more efficiently by eliminating the pixel decoder.