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[2602.10195] Versor: A Geometric Sequence Architecture
Summary
The paper introduces Versor, a novel geometric sequence architecture that leverages Conformal Geometric Algebra for enhanced performance and interpretability in machine learning tasks.
Why It Matters
Versor represents a significant advancement in machine learning architecture by utilizing geometric transformations to improve efficiency and generalization. Its ability to outperform traditional models like Transformers in various benchmarks highlights its potential for future applications in AI and data science.
Key Takeaways
- Versor utilizes Conformal Geometric Algebra for structural generalization.
- It significantly reduces parameter count compared to Transformers, enhancing efficiency.
- The architecture maintains stable predictions in out-of-distribution tests.
- Features like Recursive Rotor Accumulator improve temporal complexity.
- Custom Clifford kernels achieve substantial speedups in processing.
Computer Science > Machine Learning arXiv:2602.10195 (cs) [Submitted on 10 Feb 2026 (v1), last revised 26 Feb 2026 (this version, v2)] Title:Versor: A Geometric Sequence Architecture Authors:Truong Minh Huy, Edward Hirst View a PDF of the paper titled Versor: A Geometric Sequence Architecture, by Truong Minh Huy and 1 other authors View PDF HTML (experimental) Abstract:A novel sequence architecture is introduced, Versor, which uses Conformal Geometric Algebra (CGA) in place of traditional linear operations to achieve structural generalization and significant performance improvements on a variety of tasks, while offering improved interpretability and efficiency. By embedding states in the $Cl_{4,1}$ manifold and evolving them via geometric transformations (rotors), Versor natively represents $SE(3)$-equivariant relationships without requiring explicit structural encoding. Versor is validated on chaotic N-body dynamics, topological reasoning, and standard multimodal benchmarks (CIFAR-10, WikiText-103), consistently outperforming Transformers, Graph Networks, and geometric baselines (GATr, EGNN). Key results include: orders-of-magnitude fewer parameters ($200\times$ vs. Transformers); interpretable attention decomposing into proximity and orientational components; zero-shot scale generalization (0.993 vs. 0.070 MCC for ViT); and featuring a Recursive Rotor Accumulator (RRA) for $O(L)$ linear temporal complexity in dynamical systems, and a Geometric Product Attention (GPA) mec...