Atomic Vector Symbolic Architecture: Physics-Inspired Hyperdimensional Computing for Explainable AI
Year: 2026
Type: preprint
Abstract: This study introduces the Atomic Vector Symbolic Architecture (Atomic VSA), a deterministic framework grounded in Hyperdimensional Computing (HDC) for clinical triage and explainable AI. Key Results:• 92.5% F1 Score on 25-category ICD-11 clinical triage (Winner-Take-All, no tuning)• 91.9% label recall for multi-label comorbidity detection• 11.97ms median inference latency on commodity CPU (15W)• Algebraically-traceable decisions enabling full audit trails The system employs 10,048-dimensional bipolar vectors with holographic reduced representations, enabling semantic composition through binding and bundling operations. We document an 8% accuracy ceiling due to semantic clones with 100% symptom overlap—a fundamental symptom-encoding limit requiring lab values for disambiguation. Atomic VSA is positioned as a complementary paradigm to neural networks for scenarios prioritizing deterministic inference, on-premise deployment, and regulatory compliance.
Author: Arshad, Muhammad
Institution: Independent Research Association
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W7128949136