OpenCoven/fable-forge-10k

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

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:

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 bridged via MythosBridge — general deep reasoning
• Stage 2: FableForge — narrative-specific reasoning with loop-depth annotations

Citation

@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

Links

• GitHub: OpenCoven/open-fable
• Architecture: OpenFable README
• Dataset card: datasets/README.md