Context-Gated Associative Retrieval: From Theory to Transformers
Hopfield networks and their generalizations have established deep connections among biological associative memories, statistical physics, and transformers. Yet most models treat retrieval as a fixed query-to-memory mapping, ignoring the role of external context in recall. In this work, we propose a two-stage associative memory architecture, wherein a context-gate subcircuit reshapes the retrieval energy landscape before and during recall. We show theoretically that context gating increases inter-memory separation while inducing sparsity, translating into exponential improvements in retrieval. Crucially, we prove that the system admits a unique self-consistent fixed point, revealing that t...
| Authors | Moulik Choraria, Argyrios Gerogiannis, Vidhata Jayaraman et al. |
|---|---|
| Primary category | cond-mat.dis-nn |
| Published | 8 May 2026 |
| Last updated at source | 8 May 2026 |
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