Zain Ali

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loci

Does where a memory is filed decide what it can recall, and what it can imagine?

Pre-registered, two rounds · port of Vector-HaSH (Nature 2025)

0.009mean error

A theory with zero fitted parameters predicted recall on 156 held-out runs it was never developed on. Every run within 0.05.

The question

Vector-HaSH stores memories the way the hippocampus is thought to: a fixed grid-cell scaffold of 3,600 addresses. The paper leaves one choice open, where each memory goes. loci measures what that choice decides.

What I built

A theory of noisy recall with nothing fitted, frozen and hashed before it was tested; a filing rule derived from its error law (store where your recall points, then replay); and a test of whether the memory can construct combinations it never experienced.

What I found

The theory predicted 156 held-out runs to a mean error of 0.009. The filing rule beats k-means by 0.05–0.15. But filing for recall does not find the structure imagination needs: it constructs 0.24–0.31 of the unseen combinations, against 0.95–1.00 when filed by the true factors.

What it does not show

A study of how scaffold memories work and fail, not a better memory: kNN over the stored patterns recalls everything. The rule needs more state than the memory holds, and 2 of 13 pre-registered criteria failed.

0.009
mean error over 156 held-out runs, zero fitted parameters
11 of 13
pre-registered criteria passed as registered
0.24–0.31
unseen combinations constructed when filed for recall

The result

mean |error| of the frozen theory, by what each condition changed · 156 conditions
mean |error| of the frozen theory, by what each condition changed · 156 conditions
half the place cells0.004
masked cues, 50% unknown0.004
sentence embeddings0.007
a fourth grid module0.007
factor counts no module matches0.008
other module periods0.009
loads between the developed ones0.007
cue dimension 2,0000.010
the paper's own write rule0.029

How it works

  1. noisy cue → cue map · ridge → place cells → grid modules 3 · 4 · 5 → address → read-outcoefficients · snap
  2. error law → filing rule → placementstore where recall points
  3. replay → placementexact descent
  4. read-out → filing through the read-out

Every number, table and caveat: the full technical write-up