Publish AutomataBench public dataset v1

README.md

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+---
+pretty_name: "AutomataBench"
+language:
+- en
+license: cc-by-4.0
+size_categories:
+- n<1K
+task_categories:
+- question-answering
+tags:
+- benchmark
+- reasoning
+- cellular-automata
+- reversible-computing
+- constraint-satisfaction
+- trace-completion
+- json
+configs:
+- config_name: default
+  default: true
+  data_files:
+  - split: public_dev
+    path: data/public_dev.jsonl
+  - split: public_eval
+    path: data/public_eval.jsonl
+  - split: sample
+    path: data/sample.jsonl
+---
+
+# AutomataBench
+
+AutomataBench evaluates whether a model can reconstruct the initial state of a
+reversible cellular automaton from revealed cells in its space-time evolution.
+
+This Hugging Face dataset card is structured like a benchmark dataset repo. It
+uses Hub metadata front matter and an explicit `configs` block so the data can be
+loaded with `datasets.load_dataset`.
+
+```python
+from datasets import load_dataset
+
+ds = load_dataset("AutomataBench/automata-bench", split="sample")
+print(ds[0].keys())
+```
+
+For local development before upload:
+
+```python
+from datasets import load_dataset
+
+ds = load_dataset("hf_dataset", split="sample")
+```
+
+Install the optional loader first if needed:
+
+```bash
+python3 -m pip install datasets
+```
+
+## Fields
+
+- `id`: stable row identifier.
+- `split`: `sample`, `public_dev`, or `public_eval`.
+- `difficulty`: `easy`, `medium`, or `hard`.
+- `task`: currently `initial_state_recovery`.
+- `grid`: `width`, `height`, and `boundary`.
+- `time_horizon`: number of reversible automaton steps.
+- `rule`: reversible 2x2 Margolus block rule, including binary alphabet,
+  bit order, partition type, and 16-entry permutation.
+- `observations`: revealed cells as `(t, x, y, value)` records.
+- `answer`: reference answer with `initial_state`.
+- `metadata`: full generator metadata.
+
+## Task
+
+Return only JSON:
+
+```json
+{"initial_state": [[0, 1], [1, 0]]}
+```
+
+with the actual instance dimensions. The answer is correct when simulating the
+provided reversible block cellular automaton from the returned initial state
+matches every observation.
+
+## Evaluation
+
+Public splits include gold answers for local scoring. Do not include `answer` in
+model prompts. Scores on these public splits are useful for debugging,
+reproducibility, and public comparison, but they are not trusted official
+leaderboard scores because the answers are public. Official leaderboard scores
+use a separate non-public evaluation set.
+
+The public verifier lives in the GitHub repo:
+
+```bash
+cd public_repo
+automata-bench-verify path/to/public_split.jsonl
+```
+
+## Dataset Creation
+
+Each accepted sample was generated by:
+
+1. sampling a reversible binary 2x2 Margolus block rule;
+2. rejecting degenerate rules;
+3. simulating a random initial state;
+4. revealing cells from the simulated trace;
+5. using a PySAT-backed SAT encoding to prove uniqueness by solving once,
+   blocking the recovered initial state, and proving the blocked formula UNSAT;
+6. rejecting instances solved by propagation alone or below the branch-count
+   threshold.
+
+All public instances have certified unique solutions. The SAT check finds the
+reference initial state, blocks that state, and proves the blocked formula UNSAT
+with Glucose4. Rows expose this as `metadata.unique_solution = true`.
+
+This is a static public snapshot. The held-out private evaluation set used for
+the initial organizer-run leaderboard is not included here and was checked on
+2026-06-24 to have zero `rule_id` overlap with `sample`, `public_dev`, and
+`public_eval`. The `public_dev` and `public_eval` splits were also checked to
+have zero exact-instance overlap and zero `rule_id` overlap.
+
+## Intended Use
+
+This dataset is intended for evaluation and benchmark development.
+
+## License
+
+The public AutomataBench dataset files and documentation are licensed under the
+Creative Commons Attribution 4.0 International License.
+
+The AutomataBench name, logo, website, official leaderboard, and non-public
+evaluation or data assets are not licensed under this public dataset license.
+
+For larger datasets, custom-generated evaluation suites, or commercial
+licensing, contact: data@automatabench.com.
+
+## Available Splits
+
+- `public_dev`: 300 rows, 100 easy, 100 medium, 100 hard.
+- `public_eval`: 300 rows, 75 easy, 100 medium, 125 hard.
+- `sample`: 60 rows, 20 easy, 20 medium, 20 hard.
+
+Observation density varies by difficulty in public v1: easy rows range from
+0.35 to 0.45, medium rows from 0.234375 to 0.25, and hard rows from 0.1875 to
+0.21875. Overall public v1 density ranges from 0.1875 to 0.45.
+
+All public splits include answers. `sample` is a balanced quick-inspection
+excerpt from public data. Official leaderboard scoring uses a separate
+non-public evaluation set, not these public splits.

metadata.json

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+{
+  "format_version": "srstc-v1",
+  "name": "AutomataBench",
+  "repo_id": "AutomataBench/automata-bench",
+  "row_schema": {
+    "answer": "reference object containing row-string initial_state",
+    "difficulty": "easy, medium, or hard",
+    "id": "stable row identifier",
+    "metadata": "generation and uniqueness-check metadata",
+    "split": "sample, public_dev, or public_eval"
+  },
+  "short_name": "AutomataBench",
+  "splits": [
+    {
+      "answers": "included",
+      "name": "public_dev",
+      "num_rows": 300,
+      "path": "data/public_dev.jsonl"
+    },
+    {
+      "answers": "included",
+      "name": "public_eval",
+      "num_rows": 300,
+      "path": "data/public_eval.jsonl"
+    },
+    {
+      "answers": "included",
+      "name": "sample",
+      "num_rows": 60,
+      "path": "data/sample.jsonl"
+    }
+  ]
+}