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

arXiv: https://arxiv.org/abs/2610.00232 · HTML: https://mlsanity.com/paper/2610.00232/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 11 of its 16 references that are in the map.
- First posted on arXiv 23 Sep 2026.

## Builds on (most cited first, 11 of 11)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Efficient Streaming Language Models with Attention Sinks](https://mlsanity.com/paper/2309.17453/) (2023)
- [Transformers are RNNs: Fast Autoregressive Transformers with Linear Attention](https://mlsanity.com/paper/2006.16236/) (2020)
- [Gated Delta Networks: Improving Mamba2 with Delta Rule](https://mlsanity.com/paper/2412.06464/) (2024)
- [Linear Transformers Are Secretly Fast Weight Programmers](https://mlsanity.com/paper/2102.11174/) (2021)
- [Parallelizing Linear Transformers with the Delta Rule over Sequence Length](https://mlsanity.com/paper/2406.06484/) (2024)
- [Zoology: Measuring and Improving Recall in Efficient Language Models](https://mlsanity.com/paper/2312.04927/) (2023)
- [Using Fast Weights to Attend to the Recent Past](https://mlsanity.com/paper/1610.06258/) (2016)
- [Understanding Factual Recall in Transformers via Associative Memories](https://mlsanity.com/paper/2412.06538/) (2024)
- [Gated DeltaNet-2: Decoupling Erase and Write in Linear Attention](https://mlsanity.com/paper/2605.22791/) (2026)
- [Revisiting associative recall in modern recurrent models](https://mlsanity.com/paper/2508.19029/) (2025)

## Abstract (arXiv)

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.