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.