arXiv Computer Vision
World-as-Graph: Relational World Modeling Through Latent Space Graphs
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arXiv Computer Vision
6d ago
WALT: Learning World-Model-Aligned Latent Trajectories for Autonomous Driving
WALT introduces a method to align latent trajectories with pretrained driving world models, creating a compact generative trajectory space that preserves action-relevant semantics without altering the original model. The approach uses a dual-branch autoencoder to map raw waypoints into this latent space and transfers visual world knowledge into trajectory representations. Experiments on NAVSIM benchmarks show modest performance gains and a 30.5% reduction in planner FLOPs, indicating that maintaining world representations while extracting action-relevant information can improve trajectory planning efficiency.
arXiv Computer Vision
Sep 22
Relationally Grounded Latent World Models for Autonomous Driving
Relationally Grounded Latent World Models for Autonomous Driving proposes using traffic scene graphs as privileged semantic supervision for latent world representations. The approach builds actor‑centric scene graphs from nuScenes 3D annotations, encodes their relational structure with a frozen text embedding model, and aligns visual latent representations to this semantic target during training. At inference the supervision branch is removed, requiring no scene graphs or 3D annotations and adding no extra computation, while achieving a 5.9% reduction in average trajectory L2 error and a 52.4% drop in collision rate compared to the LAW baseline.
arXiv AI
4d ago
Abductive World Modeling via Causal Representation Learning
arXiv:2609.36985v1 Announce Type: cross Abstract: The central challenge of world modeling is to learn representations that capture how the world evolves. However, existing world models predominantly...
arXiv Computer Vision
Sep 25
Representation World Model: Learning States, Transition and Executable Plans in Representation
The Representation World Model (RWM) learns states, transitions, and executable plans directly within a representation space, bypassing traditional explicit dynamics models and action-space search. It uses inverse-dynamics supervision along latent paths to shape the representation geometry, enabling direct planning by constructing a latent path between current and goal states and recovering actions via inverse dynamics. Experiments on continuous-control benchmarks and robotic manipulation tasks demonstrate RWM’s effectiveness and potential for complex embodied control.
arXiv Machine Learning
Jun 15
Temporal Straightening for Latent Planning
arXiv:2603. 12231v2 Announce Type: replace Abstract: Learning good representations is essential for latent planning with world models.
arXiv AI
Jul 1
Delta-JEPA: Learning Action-Sensitive World Models via Latent Difference Decoding
arXiv:2606. 31232v1 Announce Type: new Abstract: Learning visual world models for planning requires compact latent dynamics that remain sensitive to actions, yet reconstruction-free joint-embedding objectives can collapse to action-insensitive representations.