siaglass/hitori-hrm
Updated
grid list | solution list | solve_nodes int64 | cap1_nodes int64 | tier int64 | conn_critical int64 | n_black int64 | bin string |
|---|---|---|---|---|---|---|---|
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End of preview. Expand in Data Studio
Hitori 9×9: uniquely solvable, difficulty-graded
1,950 machine-generated 9×9 Hitori puzzles,
every one verified to have exactly one solution: 1,000 train + 200 test
(standard config) and a 750-puzzle stratified hard test set (hard config).
Built as the training/eval domain for PRISM: an experiment testing whether HRM-style recurrent reasoning models (27M params) generalize to a puzzle domain they have never been trained on. A 27M-parameter HRM trained on the 1,000 standard puzzles (with dihedral + digit-permutation augmentation) reaches 97.7% exact solutions on the hard set despite training only on the standard-difficulty set; efficient-tier LLMs (gpt-5.6-luna, gemini-3.5-flash) solve 0/25 of a frozen hard subset via direct or extended reasoning prompts.
Rules of Hitori
Shade a subset of cells so that:
- (a) no number repeats among unshaded cells in any row or column;
- (b) no two shaded cells are orthogonally adjacent;
- (c) all unshaded cells form one orthogonally connected region.
Rule (c) is a global constraint (unlike Sudoku's row/column/box locality),
which makes Hitori useful for probing whether a model can learn non-local
consistency. conn_critical (hard set) flags puzzles where connectivity is
load-bearing for uniqueness.
Fields
| field | type | in | meaning |
|---|---|---|---|
grid |
81 × int (1–9) | all | puzzle numbers, row-major |
solution |
81 × int (0/1) | all | 1 = shaded (black), 0 = unshaded |
n_black |
int | all | number of shaded cells |
solve_nodes |
int | all | search nodes the reference solver needed (difficulty proxy; 3–2,431) |
cap1_nodes |
int | hard | solver nodes with propagation capped (secondary difficulty signal) |
tier |
int | hard | deductive difficulty tier |
conn_critical |
0/1 | hard | 1 = uniqueness depends on the connectivity rule |
bin |
str | hard | stratum: raw, hard24, anneal150, anneal400, anneal400x2 (150 each) |
Generation & verification
- Black-cell sets sampled as maximal independent sets with connected complement (row-by-row DFS with invariants incl. one-row lookahead); Latin-square fill for white cells; black cells take a same-line white value to force shading.
- Uniqueness gate: count-to-2 backtracking solver that branches only on value-sharing cells; every puzzle re-verified after every transformation.
- Hard set: simulated annealing on the number assignment to maximize solver nodes (3 → 2,431), uniqueness re-verified at every step, stratified 5×150, deduplicated against the train set under dihedral + digit-relabel canonical form (train/test overlap: zero).
Intended use
Constraint-reasoning evals for LLMs, training data for small reasoning models (the 81-token grid drops into HRM/TRM Sudoku pipelines unchanged: seq_len 81, vocab 11), or just skipping the generator-engineering if you need verified-unique Hitori instances.
Links
- Write-up: https://anstall-mig.se/blog/prism
- Trained model: https://huggingface.co/siaglass/hitori-hrm
License
CC-BY-4.0. Generated data, no third-party content.
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