StarWM: Self-Supervised Trained Attention Routing for Robust World Models

arXiv:2609.30667 · cs.CV, cs.LG · Submitted 2026-09-25 · Read on arXiv

cs.CV, cs.LG

Submitted: 2026-09-25

Updated: 2026-09-25

Terminology

World Model — 36× in this paper · explained in 7 episodes
Accuracy — 32× in this paper · explained in 1 episode
Contrastive Learning — 25× in this paper · explained in 5 episodes
Reinforcement Learning — 15× in this paper · explained in 4 episodes
Ablation — 13× in this paper · explained in 1 episode
Robustness — 8× in this paper · explained in 5 episodes
Redundancy — 7× in this paper · explained in 2 episodes
Representation Learning — 7× in this paper · explained in 2 episodes
Grounding — 6× in this paper · explained in 3 episodes
Latent Dynamics — 6× in this paper · explained in 2 episodes
Latent Space — 6× in this paper · explained in 7 episodes
Action Space — 5× in this paper · explained in 1 episode
Benchmark — 5× in this paper · explained in 2 episodes
Generalization — 5× in this paper · explained in 13 episodes
Core — 4× in this paper · explained in 2 episodes
Dual-Stream Architecture — 4× in this paper · explained in 3 episodes
Consistency — 3× in this paper · explained in 3 episodes
Decode — 3× in this paper · explained in 2 episodes
Latent Representation — 3× in this paper · explained in 2 episodes
Ablation Studies — 2× in this paper · explained in 1 episode
Actor — 2× in this paper · explained in 3 episodes
Alignment — 2× in this paper · explained in 1 episode
Conference on Computer Vision and Pattern Recognition — 2× in this paper
Feature Extraction — 2× in this paper · explained in 2 episodes
Reconstruction Error — 2× in this paper · explained in 2 episodes
Regularization — 2× in this paper · explained in 3 episodes
Video Hard Random — 2× in this paper
Video Hard Sequential — 2× in this paper
Visual Grounding — 2× in this paper · explained in 2 episodes
alpha — 2× in this paper · explained in 1 episode
Ablation study — 1× in this paper · explained in 1 episode
Abstraction — 1× in this paper · explained in 1 episode
Adam — 1× in this paper · explained in 2 episodes
Aggregation — 1× in this paper · explained in 1 episode
Attention Mechanism — 1× in this paper · explained in 3 episodes
Benchmarking — 1× in this paper · explained in 3 episodes
Calibration — 1× in this paper · explained in 7 episodes
Data Augmentation — 1× in this paper · explained in 7 episodes
Deep Reinforcement Learning — 1× in this paper · explained in 2 episodes
Domain Shift — 1× in this paper · explained in 4 episodes
Failure Modes — 1× in this paper · explained in 3 episodes
Fidelity — 1× in this paper · explained in 2 episodes
Foundation Model — 1× in this paper · explained in 3 episodes
Foundation Models — 1× in this paper · explained in 7 episodes
Gradient Flow — 1× in this paper · explained in 2 episodes
Iterative Refinement — 1× in this paper · explained in 3 episodes
Latent Representations — 1× in this paper · explained in 3 episodes
Loss Function — 1× in this paper · explained in 2 episodes
Quantization — 1× in this paper · explained in 12 episodes
Recurrent State Space Model — 1× in this paper
Regret — 1× in this paper · explained in 3 episodes
Reward Function — 1× in this paper · explained in 2 episodes
Self-supervised Trained Attention Routing World Model — 1× in this paper
Temporal consistency — 1× in this paper · explained in 2 episodes
Transformer — 1× in this paper · explained in 2 episodes
Verification — 1× in this paper · explained in 2 episodes

StarWM: Self-Supervised Trained Attention Routing for Robust World Models

Terminology

Sources

Related papers