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README.md

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 ---
+dataset_info:
+  - config_name: brak
+    features:
+      - name: prompt
+        dtype: string
+      - name: target
+        dtype: string
+    splits:
+      - name: train
+        num_examples: varies_by_bucket
+      - name: test
+        num_examples: varies_by_bucket
+  - config_name: stroph7
+    features:
+      - name: prompt
+        dtype: string
+      - name: target
+        dtype: string
+    splits:
+      - name: train
+        num_examples: 1000
+      - name: test
+        num_examples: 500
+configs:
+  - config_name: brak
+    data_files:
+      - split: train
+        path: "brak/brak_b*_train.json"
+      - split: test
+        path: "brak/brak_b*_test.json"
+  - config_name: stroph7
+    data_files:
+      - split: train
+        path: "stroph-7/stroph7_train.json"
+      - split: test
+        path: "stroph-7/stroph7_test.json"
 license: cc-by-sa-4.0
+task_categories:
+  - text-generation
+  - text-classification
+language:
+  - en
+tags:
+  - benchmark
+  - adaptability
+  - plasticity
+  - fine-tuning
+  - synthetic
+  - rule-following
 ---
+
+# BEAP Datasets — BRAK & Stroph-7
+
+This repository contains the datasets for the **Benchmark for Empirical Adaptability and Plasticity (BEAP)**. BEAP measures two distinct capabilities in language models:
+
+- **Adaptability** (via the LES sub-benchmark): how quickly and efficiently a model can acquire new structured knowledge using LoRA fine-tuning across ranks 0–128.
+- **Plasticity** (via the PHS sub-benchmark): how well a model retains prior learning while integrating new knowledge through full fine-tuning across six progressive difficulty buckets.
+
+Both datasets exist in two evaluation modes: **Generation (G)**, where the model must produce valid outputs, and **Recognition (R)**, where it must identify the valid option in a multiple-choice setting.
+
+---
+
+## Datasets
+
+### BRAK
+
+BRAK is a synthetic programming language with a fixed grammar: Subject–Object–Verb sentence order, mandatory verb suffixes, and an asymmetric bracket system (`)(`, `][`, `}{`) that encodes certainty levels through a rock-paper-scissors-style logic. Tasks are organised into six difficulty buckets:
+
+| Bucket | Focus |
+|--------|-------|
+| 1 | SOV statements, basic verb forms |
+| 2 | Verb suffixes (`ok`, `em`, `ith`, `vu`, `al`) |
+| 3 | Variable assignment (`>>`), conditional branching (`iff`) |
+| 4 | Function definitions (`func`) |
+| 5 | Pipelines (`~~`), anonymous functions (`lam`) |
+| 6 | Tentative/imperative bracket semantics |
+
+Each bucket includes `train`, `test`, and `cf_probe` splits. The CF (catastrophic forgetting) probes are used in PHS to measure retention of earlier buckets after training on later ones. Recognition mode files follow the same naming convention with an `_r_` infix (e.g. `brak_r_b3_train.json`).
+
+**File sizes per bucket:**
+
+| Bucket | Train | Test | CF probe |
+|--------|-------|------|----------|
+| 1–2 | 500 | 100 | 50 |
+| 3–4 | 750 | 150 | 50 |
+| 5–6 | 1000 | 200 | 50 |
+
+---
+
+### Stroph-7
+
+Stroph-7 is a synthetic constrained poetry format. Each poem consists of three strophes of four lines each, subject to multiple simultaneous constraints: line word counts (7 / 5 / 7 / 5), an ordered digit sequence across the six long lines (1–6), per-strophe anchor words at line 4, strophe-initial alliteration, and the presence of at least one nature-lexicon word per strophe.
+
+The dataset contains 1,000 training examples and 500 test examples in Generation mode. Recognition mode provides multiple-choice items where exactly one of four poems satisfies all constraints, with the three distractors each containing a different injected violation (`word_count`, `digit_missing`, `digit_order`, `anchor_wrong`, `alliteration`, or `nature_absent`).
+
+---
+
+## Data Format
+
+**Generation mode** items:
+```json
+{
+  "prompt": "Write a Stroph-7 poem.\n\nRules:\n...",
+  "target": "<valid poem or BRAK program>",
+  "params": { ... }
+}
+```
+
+**Recognition mode** items:
+```json
+{
+  "prompt": "Below are four poems / programs ... Which is fully valid?",
+  "correct_label": "B",
+  "options": {
+    "A": { "text": "...", "violation": "word_count" },
+    "B": { "text": "...", "violation": null },
+    ...
+  }
+}
+```
+
+---
+
+## Intended Use
+
+These datasets are intended for use with the BEAP benchmark runner (`beap.py`). They are not natural-language corpora and are not suitable for pre-training or general language modelling. Their value is specifically in measuring structured rule acquisition and retention under fine-tuning.
+
+---
+
+## Generation
+
+All data is fully synthetic and deterministically reproducible. Fixed seeds are used throughout:
+
+| Split | Seed |
+|-------|------|
+| Stroph-7 train | 0 |
+| Stroph-7 test | 10000 |
+| BRAK train | 42 |
+| BRAK test | 99000 |
+| BRAK CF probe | 55000 |
+
+To regenerate:
+```bash
+python beap.py --generate --data-dir ./beap_data
+```
+
+---
+
+## Citation
+
+If you use these datasets, please cite:
+```
+@misc{beap2026,
+  title  = {BEAP: Benchmark for Empirical Adaptability and Plasticity},
+  author = {Buisman, Michiel},
+  year   = {2026},
+  url    = {https://huggingface.co/datasets/MichielBuisman/beap-data}
+}
+```