arXiv — NLP / Computation & Language · · 3 min read

Towards Version-aware Operations and Transaction Memories for Multi-layer MeMo

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Computer Science > Computation and Language

arXiv:2606.24040 (cs)
[Submitted on 23 Jun 2026]

Title:Towards Version-aware Operations and Transaction Memories for Multi-layer MeMo

Authors:Peiran Li
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Abstract:MeMo proposes language models with explicit multi-layer correlation matrix memories (CMMs), where memorization, retrieval, and forgetting are architectural operations. This paper asks how such memories can reduce the need for retraining when knowledge changes. For changes expressible as MeMo memory associations, the model's accessible knowledge can be updated by editing explicit memories rather than retraining the whole model. We propose a version-aware operation layer in which high-level operations such as replace, obsolete, keep-history, rollback, and trace are compiled into MeMo-native primitive calls over sequences and tokens. The key observation is that a version-aware operation is rarely a single MeMo association. It is an ordered transaction of primitive edits, for example forgetting one sequence-token chain, memorizing another, preserving a historical chain, and recording an inverse program. The framework introduces two auxiliary CMMs: a Version CMM (V-CMM) for mapping version transitions to transaction handles, and a Transaction CMM (T-CMM) for storing reusable change contents and inverse programs. It supports both direct sequence-level edits and structured diff-level inputs, and outlines an evaluation route for update success, rollback, traceability, locality, and transaction reuse.
Comments: Accepted by MeMo Workshop on Mechanistic Interpretability & Neuro-symbolic Approaches by-design, Rome (Italy), 24/6/2026
Subjects: Computation and Language (cs.CL); Artificial Intelligence (cs.AI); Symbolic Computation (cs.SC)
Cite as: arXiv:2606.24040 [cs.CL]
  (or arXiv:2606.24040v1 [cs.CL] for this version)
  https://doi.org/10.48550/arXiv.2606.24040
arXiv-issued DOI via DataCite (pending registration)

Submission history

From: Peiran Li [view email]
[v1] Tue, 23 Jun 2026 00:53:14 UTC (28 KB)
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