Search is not available for this dataset
sequence list | corrupted list | num_changed list |
|---|---|---|
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End of preview. Expand in Data Studio
Recognition Memory
A benchmark for memories that recognise rather than recall: the model sees a sequence and must decide whether it is consistent with what it has stored, for example by using low surprise as the familiarity signal.
Subsets
N4_V16, N8_V16, N16_V16, N32_V16, N64_V16, N128_V16, N256_V16: sequences of length N over a vocabulary of 16 tokens.
Splits: train 900,000 / validation 50,000 / test 100,000 samples.
Fields
| field | type | description |
|---|---|---|
sequence |
uint8[N] |
primary sequence, tokens i.i.d. uniform over 0..15 |
corrupted |
uint8[8][N] |
8 corrupted copies of sequence |
num_changed |
uint16[8] |
number of tokens changed in each corrupted copy |
Corruption process
For each corrupted copy, one position chosen uniformly at random is always changed, and each
of the other N-1 positions is changed independently with probability 1/(N-1). The number of
changed tokens is therefore 1 + Binomial(N-1, 1/(N-1)), with expectation exactly 2. A changed
token is replaced by a uniformly random different token, so every corrupted copy differs from
the primary sequence. The 8 copies are drawn independently, so two copies of the
same sample can coincide (in about 4.4% of samples for N=4, 2.7% for N=8, 1.5% for N=16, and
roughly halving with each doubling of N after that).
Primary sequences are drawn i.i.d. and are not deduplicated. For N=4 there are only 16^4 = 65,536 possible sequences, so nearly all of them appear in train and every validation/test sequence also appears in train. From N=16 up, overlap between splits is essentially zero.
Evaluation protocol
- Give the memory the primary
sequence(for example, in context before a separator). - Score
sequenceand each of its 8corruptedcopies with the memory's familiarity signal (for example, minus the summed surprise of the candidate, or a learned score). - Report, over the test split:
- acc9: the share of samples where
sequencescores strictly highest among the 9 candidates. A tie for the top score counts as 1/(number tied), so a constant score gives 1/9. - pairwise: the share of (
sequence, corrupted copy) pairs wheresequencescores higher, with ties counting as 1/2. Chance is 0.5.
- acc9: the share of samples where
Reference points: a memory that keeps only the sum of the tokens mod 16 (4 bits) gets acc9 about 0.85 at every N, and one that also keeps a position-weighted sum (8 bits) about 0.97, because the corruptions are small edits.
Usage
from datasets import load_dataset
ds = load_dataset("irodkin/recognition-memory", "N64_V16")
Reproduction
Generated with generate.py (base seed 20261030); every chunk of 50k samples is seeded from
(seed, N, V, split, chunk) and can be regenerated on its own.
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