arXiv Machine Learning August 4, 2026 By Weilun Wang, Wantong Li

Gram-Space: Structure-Preserving Codebook Compression for Memory-Efficient Neuro-Symbolic AI

Read the original on arXiv Machine Learning →

arXiv:2608. 01528v1 Announce Type: new Abstract: Vector symbolic architectures (VSA) are widely used for reasoning in neuro-symbolic (NeSy) AI, yet high-dimensional codebooks often create severe memory bottlenecks that limit scalability and deployment.

Summary generated by The Flow from the publisher's feed. The full article lives at arXiv Machine Learning.

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