drwahl/afantasia

A-Fantasia

A-Fantasia measures an LLM's capacity for mental imagery (or ability to fake it). This dataset holds the questions and every model response behind the leaderboard, plus the raw Inspect logs.

• Code: github.com/danwahl/afantasia
• Leaderboard: danwahl.net/afantasia

Overview

A-Fantasia is an Inspect AI evaluation that measures LLM capacity for mental imagery through three complementary tasks: analyzing chess positions, tracking 3D cube rotations, and spelling words backwards from definitions. Models must respond immediately without chain-of-thought reasoning, testing their ability to manipulate information internally.

Dataset structure

samples (default)

One row per model and question.

Field	Type	Description
model	string	Inspect model id, e.g. openrouter/anthropic/claude-opus-4.6
name	string	Leaderboard name
provider	string	Provider slug, e.g. anthropic
task	string	chess, cube or spell
run	string	Path of the source log under logs/
created	string	When the run started (ISO 8601)
question_id	string	Question id, joining to questions
input	string	User message shown to the model
response	string	The model's final response; earlier attempts are in the log
answer	string	Answer parsed from the response; null if none was found
valid	bool	Whether the response gives an answer to score; false if it is empty or cut off mid-reasoning
correct	bool	Whether the answer is correct
attempts	int	Times the model was asked
inspect_ai	string	Inspect version that ran the eval
canary	string	Canary string

questions

Field	Type	Description
task	string	chess, cube or spell
question_id	string	Question id
question	string	Question text; the full user message is in samples.input
targets	list[string]	Correct answers; for chess, every legal move
metadata	string	JSON with the task's details: the FEN and move history, the cube's starting faces and rotations, or the word
canary	string	Canary string

logs/

The raw Inspect logs named in run, with full message histories and run settings.

Scoring

A task's error rate is the share of valid samples that are not correct. A model is ranked when each task has at least 80 valid samples, and its overall score is the mean of the three error rates. The leaderboard's 95% intervals come from a percentile bootstrap that resamples questions within each task.

Contamination protection

Each row carries the canary GUID. Please don't train on this dataset, and keep the canary strings if you redistribute it.

License

Released under the MIT License. The spell definitions come from WordNet, used under the WordNet License.

Citation

@misc{wahl2026afantasia,
  title = {{A-Fantasia: A benchmark for evaluating an LLM's capacity for mental imagery}},
  author = {Wahl, Daniel},
  year = {2026},
  doi = {10.5281/zenodo.23132560},
  url = {https://github.com/danwahl/afantasia}
}

@misc{wahl2026afantasiadata,
  title = {{A-Fantasia dataset}},
  author = {Wahl, Daniel},
  year = {2026},
  publisher = {Hugging Face},
  doi = {10.57967/hf/10746},
  url = {https://huggingface.co/datasets/drwahl/afantasia}
}