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arXiv Machine Learning By Zeyu Jia (School of Biomedical Engineering,Technology, Tianjin Medical University, Medical School, Tianjin University)

Beyond Diagonal State Space Models: Exact Non-Abelian Group Tracking, Solvability Barriers, and Geometric Physical Manifolds

Read the original on arXiv Machine Learning →

The paper introduces a new class of selective state space models (SSMs) that move beyond traditional diagonal constraints by leveraging exact non‑Abelian group tracking and solvable affine transformation groups. It reports significant performance gains, including a 0.04‑degree dead‑reckoning error, high accuracy on Dyck‑2 and deep AST scope tracking tasks, and demonstrates that strict isometry is essential for lossless long‑range associative memory.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Machine Learning.

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