Add dataset card

README.md

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+---
+license: mit
+task_categories:
+- text-generation
+- text2text-generation
+language:
+- en
+tags:
+- narrative-reasoning
+- character-consistency
+- recurrent-depth-transformer
+- story-generation
+- synthetic
+- opencoven
+size_categories:
+- 10K<n<100K
+---
+
+# FableForge — Narrative Reasoning Dataset with Recurrence-Depth Annotations
+
+**The first narrative dataset designed around recurrence depth requirements.**
+
+Every example carries a `suggested_n_loops` field with a theoretically grounded basis —
+derived from the structural complexity of the task, not a heuristic label or emergent property.
+
+## Background
+
+Standard narrative datasets treat reasoning depth as an emergent property. FableForge is
+different: it annotates *how much computation* each task structurally requires, making it
+suitable for training Recurrent-Depth Transformers (RDTs) where loop count is a controllable
+hyperparameter at inference time.
+
+## Task Types
+
+### `character_trace`
+Track a named character's state (location, emotion, companions) across N scene transitions.
+
+**Recurrence requirement:** `loops = f(n_characters × n_scenes)`
+
+More characters over more scenes → more recurrence needed to maintain entity state without drift.
+
+### `coherence_challenge`
+Detect and correct a single planted narrative inconsistency.
+
+**Recurrence requirement:** `loops = f(inconsistency_type)`
+
+Six inconsistency types, ordered by cognitive depth:
+
+| Type | Loops | Why |
+|---|---|---|
+| `name_drift` | 4 | Surface pattern match |
+| `location_contradiction` | 8 | Spatial reasoning |
+| `object_continuity` | 8 | Object state tracking |
+| `timeline_error` | 16 | Temporal ordering |
+| `relationship_error` | 16 | Social graph recall |
+| `trait_reversal` | 32 | Character psychology |
+
+### `narrative_completion`
+Generate a story continuation that satisfies N explicit constraints simultaneously.
+
+**Recurrence requirement:** `loops = f(n_characters × n_constraints)`
+
+## Dataset Fields
+
+| Field | Type | Description |
+|---|---|---|
+| `id` | string | Unique stable identifier |
+| `task_type` | string | `character_trace` / `coherence_challenge` / `narrative_completion` |
+| `genre` | string | `fantasy` or `contemporary` |
+| `complexity_score` | float | 0–1, derived from task structure |
+| `suggested_n_loops` | int | 4 / 8 / 16 / 32 — recurrence target |
+| `narrative_mode` | string | `action` / `dialogue` / `exposition` |
+| `fable_memory_required` | bool | Whether FableMemory injection is needed |
+| `coherence_probe_targets` | list[str] | Character names to monitor |
+| `messages` | list | `[{"role": "user", "content": ...}, {"role": "assistant", "content": ...}]` |
+| `source` | string | `fable_forge_v1` |
+| `constraint_count` | int | Number of simultaneous constraints |
+| `n_characters` | int | Characters in the task |
+| `n_scenes` | int | Scenes spanned |
+
+## Distribution (10k sample)
+
+- ~40% character_trace, ~30% coherence_challenge, ~30% narrative_completion
+- ~83% require FableMemory injection
+- Loop distribution: ~12% dialogue (8), ~37% exposition (16), ~51% deep (32)
+- Genres: ~50% fantasy, ~50% contemporary
+- Fully deterministic: `seed=42`
+
+## Usage
+
+```python
+from datasets import load_dataset
+
+ds = load_dataset("OpenCoven/fable-forge-10k", split="train")
+print(ds[0].keys())
+# dict_keys(['id', 'task_type', 'genre', 'complexity_score', 'suggested_n_loops',
+#            'narrative_mode', 'fable_memory_required', 'coherence_probe_targets',
+#            'messages', 'source', 'constraint_count', 'n_characters', 'n_scenes'])
+
+# Filter by depth tier
+hard = ds.filter(lambda x: x["suggested_n_loops"] == 32)
+print(f"{len(hard):,} examples require maximum recurrence depth")
+
+# Use with OpenFable NarrativeDepthController
+from open_fable.depth import NarrativeDepthController
+ndc = NarrativeDepthController()
+for ex in ds.select(range(5)):
+    loops = ndc.get_n_loops(ex["narrative_mode"])
+    print(f"{ex['task_type']:25s} suggested={ex['suggested_n_loops']:2d}  ndc={loops}")
+```
+
+## Generating More Data
+
+FableForge is deterministic and open-source. Generate at any scale:
+
+```bash
+git clone https://github.com/OpenCoven/open-fable
+cd open-fable
+pip install -e .
+python -m open_fable.data.fable_forge --count 100000 --seed 99 --output data/fable_forge_100k.jsonl
+```
+
+## Two-Stage Training Pipeline
+
+This dataset is Stage 2 of OpenFable's training pipeline:
+
+- **Stage 1:** [WithinUsAI/claude_mythos_distilled_25k](https://huggingface.co/datasets/WithinUsAI/claude_mythos_distilled_25k) bridged via MythosBridge — general deep reasoning
+- **Stage 2:** FableForge — narrative-specific reasoning with loop-depth annotations
+
+## Citation
+
+```bibtex
+@misc{openfable2026fableforge,
+  title   = {{FableForge}: A Synthetic Narrative Dataset with Recurrence-Depth Annotations},
+  author  = {OpenCoven},
+  year    = {2026},
+  url     = {https://github.com/OpenCoven/open-fable},
+  note    = {First dataset designed around recurrence depth requirements for
+             narrative reasoning in Recurrent-Depth Transformer architectures.}
+}
+```
+
+## License
+
+MIT — see [LICENSE](https://github.com/OpenCoven/open-fable/blob/main/LICENSE)
+
+## Links
+
+- GitHub: [OpenCoven/open-fable](https://github.com/OpenCoven/open-fable)
+- Architecture: [OpenFable README](https://github.com/OpenCoven/open-fable#readme)
+- Dataset card: [datasets/README.md](https://github.com/OpenCoven/open-fable/blob/main/datasets/README.md)