4-Tensor Attention Model for Semantic Physical Reality

Abstract:We describe a 4-tensor attention model that predicts the next semantic state of a scene, for video generation and robot planning. A window of states has positions (x, t) and two fibers, a semantic fiber and a temporal-context fiber, and one softmax normalizes attention jointly over the window. Frames and an agent's situation are written as those states; the encoder, the renderer, and the planner remain outside the update. To test the update on its own, we train on ROCStories, where each window poses the same next-sentence task at the semantic layer. On the validation split, with one seed per setting, the last-sentence cross-entropy on the three matched settings is lower for the 4-tensor model than for a free-running one-dimensional transformer by 5.3% at H=2, L=2, by 2.6% at H=4, L=2, and by 2.4% at H=4, L=3. At H=4, L=2 the parameter counts are nearly the same, 172.5M and 175.9M. On the same two GPUs that 4-tensor run finished in 2.4 hours and the baseline run in 45.2 hours; the baseline is trained by free-running decoding, one sequential forward pass per target token.

Machine Learning (cs.LG); Computation and Language (cs.CL)

I.2.6; I.2.7; I.2.10

arXiv:2610.11716 [cs.LG]

(or arXiv:2610.11716v1 [cs.LG] for this version)

https://doi.org/10.48550/arXiv.2610.11716 Focus to learn more arXiv-issued DOI via DataCite (pending registration)

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