4-Tensor Attention Model for Semantic Physical Reality

arXiv:2610.11716 · cs.LG, cs.CL · Submitted 2026-10-08 · Read on arXiv

cs.LG, cs.CL

Submitted: 2026-10-08

Updated: 2026-10-08

Comments: 36 pages, 4 figures

Code: https://github.com/indigowave511/semantic-world

License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/

Terminology

• Transformer — 11× in this paper · explained in 2 episodes
• Attention Mechanism — 7× in this paper · explained in 2 episodes
• Accuracy — 4× in this paper · explained in 1 episode
• World Model — 4× in this paper · explained in 7 episodes
• Consistency — 3× in this paper · explained in 3 episodes
• Embeddings — 3× in this paper · explained in 2 episodes
• Alignment — 2× in this paper · explained in 1 episode
• Structural Constraints — 2× in this paper · explained in 2 episodes
• Ablation — 1× in this paper · explained in 1 episode
• Abstraction — 1× in this paper · explained in 1 episode
• Activation Function — 1× in this paper · explained in 1 episode
• Computational Tractability — 1× in this paper · explained in 2 episodes
• Decode — 1× in this paper · explained in 2 episodes
• Decoupling — 1× in this paper · explained in 2 episodes
• Diffusion Model — 1× in this paper · explained in 8 episodes
• Diffusion Models — 1× in this paper · explained in 6 episodes
• Loss Function — 1× in this paper · explained in 2 episodes
• Perplexity — 1× in this paper · explained in 3 episodes
• Temporal consistency — 1× in this paper · explained in 2 episodes
• Tokenization — 1× in this paper · explained in 2 episodes

Sources

• Mastering Diverse Domains through World Models
• Revisiting Feature Prediction for Learning Visual Representations from Video
• VideoPoet: A Large Language Model for Zero-Shot Video Generation
• Imagen Video: High Definition Video Generation with Diffusion Models

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