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Constant-Memory Recall: Learned Associations in a Fixed Matrix State

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Computer Science > Machine Learning

arXiv:2610.00232 (cs)
[Submitted on 23 Sep 2026]

Title:Constant-Memory Recall: Learned Associations in a Fixed Matrix State

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Abstract:Fixed-size recurrent memory limits storage growth during inference, but successful recall depends on the task and training. We study a small DeltaNet variant with fixed token-specific key biases, trained to remember 32 new key-value pairings per sequence. With 32 KiB of recurrent matrix state, it achieves 99.95% mean accuracy across three training seeds when choosing among the sequence's values. Recall remains near perfect when filler extends the pre-query context to 1,798 tokens without adding pairings. Zeroing the first memory block removes this recall. An exploratory 48-pair test remains near chance after one quarter of the primary training budget and does not locate a capacity limit. Parameter-matched vector and Transformer baselines remain near chance, including the Transformer after additional training searches. This unresolved baseline failure prevents a memory-efficiency comparison.
Comments: 8 pages, 3 figures
Subjects: Machine Learning (cs.LG)
Cite as: arXiv:2610.00232 [cs.LG]
  (or arXiv:2610.00232v1 [cs.LG] for this version)
  https://doi.org/10.48550/arXiv.2610.00232
arXiv-issued DOI via DataCite (pending registration)

Submission history

From: Samuel Larson [view email]
[v1] Wed, 23 Sep 2026 00:17:36 UTC (311 KB)
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