world2vec learns mechanistic world models from observations and actions
Latent dynamics use at least ten times fewer parameters than DreamerV3, TD-MPC2 and LeWM, without reward.
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- Mentions · 7d0
- Mentions · 30d1
- All mentions5
- First seenSep 24, 2026
- Last mentioned13d ago
Timeline
5 articles · newest firstLeWAM: A JEPA World Action Model with Diffusion-Steering-Based MPC
LeWAM is a decoder-free JEPA world action model that predicts dynamics and policies while planning in policy-head noise space.
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ATLAS: Aligned Transport of Latent Structure for Reliable World Model Planning
ATLAS preserves relational latent geometry so world-model planners keep novelty structure for reliable goal reaching.
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Adaptive Latent Capacity for World Models
ALeWM learns compact latent prefixes for JEPA world models and beats fixed-width LeWM.
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AD-WM: Action-Discriminative World Models for Counterfactual Model Predictive Control
AD-WM improves counterfactual planning by preserving action information, raising hard-start success from 3.7% to 52%.
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