README.md

---
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}
}
```