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configs:
- config_name: N4_V16
data_files:
- split: train
path: N4_V16/train-*.parquet
- split: validation
path: N4_V16/validation-*.parquet
- split: test
path: N4_V16/test-*.parquet
- config_name: N8_V16
data_files:
- split: train
path: N8_V16/train-*.parquet
- split: validation
path: N8_V16/validation-*.parquet
- split: test
path: N8_V16/test-*.parquet
- config_name: N16_V16
data_files:
- split: train
path: N16_V16/train-*.parquet
- split: validation
path: N16_V16/validation-*.parquet
- split: test
path: N16_V16/test-*.parquet
- config_name: N32_V16
data_files:
- split: train
path: N32_V16/train-*.parquet
- split: validation
path: N32_V16/validation-*.parquet
- split: test
path: N32_V16/test-*.parquet
- config_name: N64_V16
data_files:
- split: train
path: N64_V16/train-*.parquet
- split: validation
path: N64_V16/validation-*.parquet
- split: test
path: N64_V16/test-*.parquet
- config_name: N128_V16
data_files:
- split: train
path: N128_V16/train-*.parquet
- split: validation
path: N128_V16/validation-*.parquet
- split: test
path: N128_V16/test-*.parquet
- config_name: N256_V16
data_files:
- split: train
path: N256_V16/train-*.parquet
- split: validation
path: N256_V16/validation-*.parquet
- split: test
path: N256_V16/test-*.parquet
- config_name: N512_V16
data_files:
- split: train
path: N512_V16/train-*.parquet
- split: validation
path: N512_V16/validation-*.parquet
- split: test
path: N512_V16/test-*.parquet
- config_name: N1024_V16
data_files:
- split: train
path: N1024_V16/train-*.parquet
- split: validation
path: N1024_V16/validation-*.parquet
- split: test
path: N1024_V16/test-*.parquet
license: mit
task_categories:
- other
pretty_name: Recognition Memory
size_categories:
- 1M<n<10M
---
# 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`, `N512_V16`, `N1024_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
1. Give the memory the primary `sequence` (for example, in context before a separator).
2. Score `sequence` and each of its 8 `corrupted` copies with the memory's familiarity signal (for example,
minus the summed surprise of the candidate, or a learned score).
3. Report, over the test split:
- **acc9**: the share of samples where `sequence` scores 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 where `sequence` scores higher, with ties counting
as 1/2. Chance is 0.5.
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
```python
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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