README.md

metadata

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

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.