Add dataset card

README.md

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+---
+license: mit
+task_categories:
+- text-generation
+tags:
+- arc-agi
+- re-arc
+- test-time-training
+- synthetic-data
+pretty_name: ARC Tiny Transformer corpus and evaluation candidates
+---
+
+# ARC Tiny Transformer data
+
+Data and vote-level artifacts for [N8python/arc-tiny-transformer](https://github.com/N8python/arc-tiny-transformer).
+
+## Corpus
+
+`corpus/documents_3k_per_task.jsonl.zst` is the exact pretraining corpus:
+
+- 1,200,000 JSONL documents;
+- 3,000 documents for each of 400 RE-ARC generator families;
+- 3,401,127,124 ARC tokens;
+- 10,413,648,837 uncompressed bytes;
+- uncompressed SHA-256 `5d1336f8d6f45358a377af2e3fd05d43c4bfe91621b0c6a44c53dad0e34ca430`.
+
+Each record contains token IDs and metadata for task ID, sampled difficulty band, demonstration count, rejection statistics, and generation seed. The fixed vocabulary and sampler are in the GitHub repository. `manifests/` contains the complete task-level corpus provenance.
+
+## Evaluation artifacts
+
+`artifacts/` contains the full 128-candidate records for:
+
+- the frozen 50M model;
+- TTT replicas seeded 57, 58, and 59;
+- the 7M TTT run.
+
+These files are sufficient to recompute rank-1, raw-frequency top-2, historical hierarchical top-2, oracle accuracy, candidate-scaling curves, and multi-replica ensembles without rerunning model inference.
+
+`viewer/data/` contains the compact per-query data backing the browser-based 384-candidate explorer.
+
+## Licensing note
+
+Release code and metadata are MIT licensed. The corpus is procedurally generated with the pinned [RE-ARC](https://github.com/michaelhodel/re-arc) generators/verifiers; ARC-AGI task files are not duplicated here. Users should also review the upstream RE-ARC and ARC-AGI licenses for their use case.
+