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Evidence record 5770 · automatically gathered

SRMU: Relevance-Gated Updates for Streaming Hyperdimensional Memories

Sequential associative memories (SAMs) are difficult to build and maintain in real-world streaming environments, where observations arrive incrementally over time, have imbalanced sampling, and non-stationary temporal dynamics. Vector Symbolic Architectures (VSAs) provide a biologically-inspired framework for building SAMs. Entities and attributes are encoded as quasi-orthogonal hyperdimensional vectors and processed with well defined algebraic operations. Despite this rich framework, most VSA s

Record details

Published: 16 April 2026
Source: arXiv
Category: Research
Topics: Environment · Biotech
Retrieved: 14 July 2026

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These records share source-supplied organisations, an exact publisher byline, automatic topics or regions. The reason is shown on every link; related does not mean supporting, agreeing with or verifying this record.

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How to cite this record

ethics.ai (16 April 2026), “SRMU: Relevance-Gated Updates for Streaming Hyperdimensional Memories,” evidence record 5770, https://ethics.ai/record/5770 (originally published by arXiv).

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