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[2601.01297] ARGUS: Adaptive Rotation-Invariant Geometric Unsupervised System
Summary
The paper introduces ARGUS, a novel framework for detecting distributional drift in high-dimensional data streams, emphasizing geometric invariance and computational efficiency.
Why It Matters
As data streams become increasingly complex, effective drift detection is crucial for maintaining the integrity of machine learning models. ARGUS addresses key challenges in existing methods, offering a scalable and robust solution that preserves geometric structure, which is vital for applications in various fields, including robotics and AI safety.
Key Takeaways
- ARGUS utilizes Voronoi tessellations for invariant drift metrics.
- Achieves O(N) complexity per snapshot, enhancing computational efficiency.
- Introduces a graph-theoretic approach to distinguish coherent shifts from noise.
- Scales effectively to high-dimensional data using product quantization.
- Experimental validation shows improved accuracy over existing methods.
Computer Science > Machine Learning arXiv:2601.01297 (cs) This paper has been withdrawn by Anantha Sharma [Submitted on 3 Jan 2026 (v1), last revised 17 Feb 2026 (this version, v2)] Title:ARGUS: Adaptive Rotation-Invariant Geometric Unsupervised System Authors:Anantha Sharma View a PDF of the paper titled ARGUS: Adaptive Rotation-Invariant Geometric Unsupervised System, by Anantha Sharma No PDF available, click to view other formats Abstract:Detecting distributional drift in high-dimensional data streams presents fundamental challenges: global comparison methods scale poorly, projection-based approaches lose geometric structure, and re-clustering methods suffer from identity instability. This paper introduces Argus, A framework that reconceptualizes drift detection as tracking local statistics over a fixed spatial partition of the data manifold. The key contributions are fourfold. First, it is proved that Voronoi tessellations over canonical orthonormal frames yield drift metrics that are invariant to orthogonal transformations. The rotations and reflections that preserve Euclidean geometry. Second, it is established that this framework achieves O(N) complexity per snapshot while providing cell-level spatial localization of distributional change. Third, a graph-theoretic characterization of drift propagation is developed that distinguishes coherent distributional shifts from isolated perturbations. Fourth, product quantization tessellation is introduced for scaling to very...
