Consolidation Without Weights: What the Complementary Learning Systems Analogy Licenses in LLM Agent Memory, and Why the Systems That Borrow Its Name Do Not Inherit Its Guarantee

Abstract

Memory systems for language-model agents almost all contain a step called consolidation, and almost all of them cite, or gesture at, the complementary learning systems account of hippocampus and neocortex when they name it. In that account consolidation is a specific operation: repeated replay from a fast, sparsely coded store into a slow learner whose shared parameters change, which is what produces generalisation to material never stored and which is also where interference lives. This paper separates what that theory commits its borrower to from what agent memory systems actually do. Four commitments are stated and used as an audit instrument. Against them, deployed agent memory divides into two families and neither instantiates the mechanism, for opposite reasons. The larger, textual family changes no parameters at all: its consolidation is iterated LLM-authored rewriting of an external store, and a 2026 controlled study reports that iterating it drives utility up and then down, in their setting below the no-memory baseline, while no replay result located here reports falling below its own no-replay control. A smaller parametric family, which appeared during 2026 and falsifies the common claim that agent memory never touches weights, does change parameters, but most of it buys stability through per-task adapter isolation or expandable blocks, and isolation withholds the shared representation that the source theory identifies as the common cause of interference and generalisation alike. The paper argues that the field's avoidance of online parametric transfer is well supported by evidence about what such transfer costs, and that what is not supported is retaining the vocabulary while declining the mechanism. It states the five measurements that would decide the question and identifies the single published configuration whose shape matches the theory.

The literature search, drafting and citation verification for this paper were carried out with AI assistance under the author's direction. Every citation was machine-verified against the arXiv API and Crossref before inclusion, and every quantitative claim was read back against the cited source's own abstract or, where a claim is drawn from a paper's body, against the located passage. The author is responsible for the final text and for all claims made in it.

About the author

Pranay Mahendrakar is an ai specialist and large language model engineer based in Bengaluru, India. He builds production artificial intelligence systems and publishes open-access research on how those systems fail. See all 66 papers by Pranay Mahendrakar, or his ORCID record.