AI Engram: In Search of Memory Traces in Artificial Intelligence

Abstract:Memory formation is fundamental to intelligence, yet whether deep neural networks preserve identifiable memory traces analogous to biological memory units remains an open question. This work introduces a geometric framework to identify such "AI engrams" by formalizing the neuroscientific criteria of specificity, reactivation, sufficiency, and necessity into a constrained inverse problem. We derive a closed-form estimator that isolates individual memory traces from globally entangled parameters, and show that this biologically-derived solution corresponds to a natural gradient update on the parameter manifold. AI engrams enable surgical manipulation of learned knowledge: any subset of memories can be composed or erased through linear arithmetic, without iterative optimization. Experiments ranging from simple MLPs to LLMs demonstrate the causal validity and substantial scalability of AI engrams. Together, these results bridge theories of biological memory and artificial representation learning and offer geometric insight into how deep networks simultaneously support functional specificity within distributed storage.

Accepted to ICML 2026 (Oral). Code is available at this https URL

Artificial Intelligence (cs.AI); Machine Learning (cs.LG)

arXiv:2606.14997 [cs.AI]

(or arXiv:2606.14997v1 [cs.AI] for this version)

https://doi.org/10.48550/arXiv.2606.14997 Focus to learn more arXiv-issued DOI via DataCite (pending registration)

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