VRE-Bench: Visual Rule Exploration

100 hand-designed visual rule exploration puzzles that each have a hidden rule that an agent has to discover through experiments. Each puzzle consists of two phases:

1. In an exploration phase, the agent sees an exploration_image, has to submit action sequences, and gets back integer points per action as a reward.
2. In a test phase, it has to apply what it has learned to a test_image of the same puzzle.

VRE-Bench measures how efficiently vision-language models learn hidden rules, at which step they fail, and whether they know when to stop exploring.

A test answer is scored by min-max normalizing its total points with the puzzle's exact reward bounds. The action sets, reward logic, bounds and the evaluation harness live in vreBench.

Fields

Field	Type	Description
id	int	Puzzle id (1–100), the key into vreBench's puzzle implementations
exploration_image, test_image	image	The two puzzle states
exploration_image_description, test_image_description	string	Python-literal textual descriptions of the images (grids, lists, dicts), complete enough to solve the puzzle without the image
hidden_rule	string	Natural-language statement of the hidden rule. Is not shown to the model.
layout	string	graph (40) · geometric (26) · grid (19) · stateful (15)
control	string	avatar (55) · manipulation (30) · selection (15)
reward_source	string	collect (42) · condition (39) · differential (19)
hidden_info	list[string]	Rule parameters the image does not show: none, or any of values, pairings, remapping
theme	string	Depicted scenario (e.g. islands-bridges, mine), or abstract
mechanics	list[string]	Optional extra tags (e.g. rotation, irreversible)
human_difficulty	int	1–5 from human performance: the mean normalized test score over the five checkpoints, ranked and cut into five bins of 20 puzzles each
human_explorations	list	Per annotator: annotator_id and attempts, the 10 recorded exploration attempts (actions, rewards, infos). Currently one annotator, on all 100 puzzles

Usage

from datasets import load_dataset

ds = load_dataset("flowun/vre", split="test")

To run a model:

pip install git+https://github.com/flowun/vreBench.git
vre --model <model-name>   # any OpenAI-compatible endpoint

Citation

If you use VRE-Bench, please cite:

@thesis{TODO,
  title  = {TODO: paper title},
  author = {TODO},
  year   = {TODO},
  url    = {TODO}
}