AI Engram: In Search of Memory Traces in Artificial Intelligence
A new geometric framework identifies 'AI engrams'—identifiable memory traces in deep neural networks—by formalizing neuroscientific criteria into a constrained inverse problem. The closed-form estimator isolates individual memories from entangled parameters, enabling surgical composition or erasure via linear arithmetic without iterative optimization. Experiments from MLPs to LLMs demonstrate causal validity and scalability, bridging biological memory and representation learning.
[2606.14997] AI Engram: In Search of Memory Traces in Artificial Intelligence
[Submitted on 12 Jun 2026]
Title:AI Engram: In Search of Memory Traces in Artificial Intelligence
View a PDF of the paper titled AI Engram: In Search of Memory Traces in Artificial Intelligence, by Jea Kwon and 5 other authors
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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.
Comments: Accepted to ICML 2026 (Oral). Code is available at this https URL
Subjects:
Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
Cite as: arXiv:2606.14997 [cs.AI]
(or arXiv:2606.14997v1 [cs.AI] for this version)
https://doi.org/10.48550/arXiv.2606.14997
arXiv-issued DOI via DataCite (pending registration)
Submission history
From: Jea Kwon [view email] [v1] Fri, 12 Jun 2026 22:36:52 UTC (12,079 KB)
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