Datasets:

irodkin
/

recognition-memory-keyed

configs:
  - config_name: N1_L8_V16
    data_files:
      - split: train
        path: N1_L8_V16/train-*.parquet
      - split: validation
        path: N1_L8_V16/validation-*.parquet
      - split: test
        path: N1_L8_V16/test-*.parquet
  - config_name: N2_L8_V16
    data_files:
      - split: train
        path: N2_L8_V16/train-*.parquet
      - split: validation
        path: N2_L8_V16/validation-*.parquet
      - split: test
        path: N2_L8_V16/test-*.parquet
  - config_name: N4_L8_V16
    data_files:
      - split: train
        path: N4_L8_V16/train-*.parquet
      - split: validation
        path: N4_L8_V16/validation-*.parquet
      - split: test
        path: N4_L8_V16/test-*.parquet
  - config_name: N8_L8_V16
    data_files:
      - split: train
        path: N8_L8_V16/train-*.parquet
      - split: validation
        path: N8_L8_V16/validation-*.parquet
      - split: test
        path: N8_L8_V16/test-*.parquet
  - config_name: N16_L8_V16
    data_files:
      - split: train
        path: N16_L8_V16/train-*.parquet
      - split: validation
        path: N16_L8_V16/validation-*.parquet
      - split: test
        path: N16_L8_V16/test-*.parquet
  - config_name: N32_L8_V16
    data_files:
      - split: train
        path: N32_L8_V16/train-*.parquet
      - split: validation
        path: N32_L8_V16/validation-*.parquet
      - split: test
        path: N32_L8_V16/test-*.parquet
  - config_name: N64_L8_V16
    data_files:
      - split: train
        path: N64_L8_V16/train-*.parquet
      - split: validation
        path: N64_L8_V16/validation-*.parquet
      - split: test
        path: N64_L8_V16/test-*.parquet
  - config_name: N128_L8_V16
    data_files:
      - split: train
        path: N128_L8_V16/train-*.parquet
      - split: validation
        path: N128_L8_V16/validation-*.parquet
      - split: test
        path: N128_L8_V16/test-*.parquet
  - config_name: N256_L8_V16
    data_files:
      - split: train
        path: N256_L8_V16/train-*.parquet
      - split: validation
        path: N256_L8_V16/validation-*.parquet
      - split: test
        path: N256_L8_V16/test-*.parquet
  - config_name: N512_L8_V16
    data_files:
      - split: train
        path: N512_L8_V16/train-*.parquet
      - split: validation
        path: N512_L8_V16/validation-*.parquet
      - split: test
        path: N512_L8_V16/test-*.parquet
  - config_name: N1024_L8_V16
    data_files:
      - split: train
        path: N1024_L8_V16/train-*.parquet
      - split: validation
        path: N1024_L8_V16/validation-*.parquet
      - split: test
        path: N1024_L8_V16/test-*.parquet
  - config_name: N8_L1_V16
    data_files:
      - split: train
        path: N8_L1_V16/train-*.parquet
      - split: validation
        path: N8_L1_V16/validation-*.parquet
      - split: test
        path: N8_L1_V16/test-*.parquet
  - config_name: N8_L2_V16
    data_files:
      - split: train
        path: N8_L2_V16/train-*.parquet
      - split: validation
        path: N8_L2_V16/validation-*.parquet
      - split: test
        path: N8_L2_V16/test-*.parquet
  - config_name: N8_L4_V16
    data_files:
      - split: train
        path: N8_L4_V16/train-*.parquet
      - split: validation
        path: N8_L4_V16/validation-*.parquet
      - split: test
        path: N8_L4_V16/test-*.parquet
  - config_name: N8_L16_V16
    data_files:
      - split: train
        path: N8_L16_V16/train-*.parquet
      - split: validation
        path: N8_L16_V16/validation-*.parquet
      - split: test
        path: N8_L16_V16/test-*.parquet
  - config_name: N8_L32_V16
    data_files:
      - split: train
        path: N8_L32_V16/train-*.parquet
      - split: validation
        path: N8_L32_V16/validation-*.parquet
      - split: test
        path: N8_L32_V16/test-*.parquet
  - config_name: N8_L64_V16
    data_files:
      - split: train
        path: N8_L64_V16/train-*.parquet
      - split: validation
        path: N8_L64_V16/validation-*.parquet
      - split: test
        path: N8_L64_V16/test-*.parquet
  - config_name: N8_L128_V16
    data_files:
      - split: train
        path: N8_L128_V16/train-*.parquet
      - split: validation
        path: N8_L128_V16/validation-*.parquet
      - split: test
        path: N8_L128_V16/test-*.parquet
  - config_name: N8_L256_V16
    data_files:
      - split: train
        path: N8_L256_V16/train-*.parquet
      - split: validation
        path: N8_L256_V16/validation-*.parquet
      - split: test
        path: N8_L256_V16/test-*.parquet
  - config_name: N8_L512_V16
    data_files:
      - split: train
        path: N8_L512_V16/train-*.parquet
      - split: validation
        path: N8_L512_V16/validation-*.parquet
      - split: test
        path: N8_L512_V16/test-*.parquet
  - config_name: N8_L1024_V16
    data_files:
      - split: train
        path: N8_L1024_V16/train-*.parquet
      - split: validation
        path: N8_L1024_V16/validation-*.parquet
      - split: test
        path: N8_L1024_V16/test-*.parquet
license: mit
task_categories:
  - other
pretty_name: Keyed Recognition Memory
size_categories:
  - 1M<n<10M

Keyed Recognition Memory

A benchmark for comparing a memory that recalls with one that recognises, as the number and the length of stored items grow. Each sample stores N sequences of length L under N keys. A query names one key and gives a candidate: recall means producing that key's sequence; recognition means telling whether the candidate is it. It is the keyed version of irodkin/recognition-memory, which stores one sequence.

Subsets

Number of items: N1_L8_V16, N2_L8_V16, N4_L8_V16, N8_L8_V16, N16_L8_V16, N32_L8_V16, N64_L8_V16, N128_L8_V16, N256_L8_V16, N512_L8_V16, N1024_L8_V16: N sequences of length 8, N = 1, 2, 4, …, 1,024.
Length of items: N8_L1_V16, N8_L2_V16, N8_L4_V16, N8_L8_V16, N8_L16_V16, N8_L32_V16, N8_L64_V16, N8_L128_V16, N8_L256_V16, N8_L512_V16, N8_L1024_V16: 8 sequences of length L, L = 1, 2, 4, …, 1,024. N8_L8_V16 belongs to both families.

Tokens are over 16 values. Splits: train 100,000 / validation 5,000 / test 10,000 samples.

Fields

field	type	description
`keys`	`uint16[N]`	N distinct key ids, drawn without replacement from `0..2047`; their order is the storage order
`sequences`	`uint8[N][L]`	the sequence stored under each key; tokens i.i.d. uniform over `0..15`, the N sequences of a sample distinct
`query`	`uint16`	index (`0..N-1`) of the queried pair: the query key is `keys[query]`, the true candidate `sequences[query]`
`candidates`	`uint8[8][L]`	validation and test only: 4 small edits of the true sequence, then 4 distant negatives (below)
`num_changed`	`uint16[8]`	validation and test only: positions where each candidate differs from the true sequence

The train split has no candidates: draw training negatives with the same process (below), fresh each time.

Negatives

Small edits (4 per query). One position chosen uniformly at random is always changed, and each of the other L−1 positions with probability 1/(L−1), so about 2 tokens change (exactly 1 at L = 1). A changed token gets a uniformly random different value.
Other keys (min(4, N−1) per query). The sequences stored under other keys of the same sample, chosen at random. They test whether the memory binds a sequence to its key; a memory that only knows which sequences it stored can't reject them.
Fresh (4 − min(4, N−1) per query, so only for N < 5). Uniformly random sequences that the sample does not store (distinctness is checked for L ≤ 15; beyond that a repeat is vanishingly rare). With N = 1 all 4 distant negatives are fresh, with N = 2 three are, with N = 4 one is.

Evaluation protocol

Give the memory the N (key, sequence) pairs, then the query key.
Score the true sequence and each of its 8 candidates.
Report on the test split:
- acc9: the share of samples where the true sequence scores strictly highest among the 9. Ties for the top score count as 1/(number tied), so a constant score gives 1/9.
- pairwise, separately for edits, other keys and fresh sequences: the share of (true, negative) pairs ranked correctly, ties counting 1/2.
- For recall: the share of samples where greedy decoding reproduces the whole sequence.

Reproduction

Generated with generate_keyed.py (base seed 20261108) and checked with test_keyed.py; every chunk of 10k samples is seeded from (seed, N, length, vocabulary, key pool, split, chunk) and can be regenerated on its own.