vanyacohen/MET-Bench-Minecraft

MET-Bench: Multimodal Entity Tracking for Evaluating the Limitations of Vision-Language and Reasoning Models

Vanya Cohen and Raymond Mooney · ICML 2026

Paper · Publication page · Load the dataset · Citation

Domains: Chess · Shell Game · Minecraft

MET-Bench evaluates entity state tracking across text and image modalities. This repository contains the Minecraft domain.

Minecraft

Minecraft is a state prediction task involving partial observations, dynamic environments, and visually complex scenes. Given an initial player state and an action instruction, the model selects the correct next state from four candidates.

Examples are drawn from recorded trajectories of scripted behaviors: collecting wood, exploring, and building structures. Actions describe player inputs such as walking, turning, attacking, using an item, climbing, and descending. One instruction can combine several inputs, such as “Walk forward 8 blocks, attack.”

Parallel text and image modalities

Each example contains two aligned representations of its initial state and four candidate next states:

Representation	Initial state	Candidate next states
Text	initial_state	candidate_states
Image	image_initial_state	image_candidate_states

Text states contain structured game telemetry, including player position, orientation, velocity, health, food, hotbar contents and held item when available, nearby blocks, and nearby entities. Image states are corresponding first-person screenshots.

The entries candidate_states[i] and image_candidate_states[i] describe the same candidate observation. Both representations share the same action and correct answer.

The paper evaluates text-state and image-state inputs with text actions. The dataset also includes image_action, a rendered diagram of the action instruction.

Dataset splits

Configuration	Train	Validation	Test
full (default)	5,000	500	1,500

Source trajectories are disjoint across training, validation, and test. Each example contains one initial state, one action instruction, and four candidate next states.

The paper reports Minecraft results on a 500-example subset of the test split. This release includes the complete splits.

Evaluation subset

The evaluation configuration contains a single 500-example test split for benchmark evaluation. Examples are the first 500 unique next-state tasks in the full test split, in source order. Uniqueness is determined by the initial state, action instruction, and ordered candidate states. The initial state, four candidates, images, and correct answer are preserved.

The evaluation_text_only configuration contains the same examples in the same order, with image fields omitted for text-only evaluation. Both configurations preserve the source example_id values and all text fields. The full configuration remains the default.

from datasets import load_dataset

test = load_dataset("vanyacohen/MET-Bench-Minecraft", "evaluation", split="test")
text_test = load_dataset("vanyacohen/MET-Bench-Minecraft", "evaluation_text_only", split="test")

Usage

import json
from datasets import load_dataset

dataset = load_dataset(
    "vanyacohen/MET-Bench-Minecraft",
    "full",
    streaming=True,
)
example = next(iter(dataset["test"]))

# Text states
initial_state = json.loads(example["initial_state"])
candidate_states = [
    json.loads(state) for state in example["candidate_states"]
]

# Corresponding images, decoded as PIL images
image_initial_state = example["image_initial_state"]
image_candidate_states = example["image_candidate_states"]

action = example["action"]
target_index = example["correct_choice"] - 1

target_state = candidate_states[target_index]
target_image = image_candidate_states[target_index]

correct_choice numbers the four candidates from 1 to 4. Subtracting one gives the corresponding Python list index.

Data fields

The dataset is stored in Parquet shards with text and embedded images together in each row.

Field	Type	Description
example_id	string	Stable identifier for the source split and row.
initial_state	string	Initial game telemetry encoded as JSON.
image_initial_state	Image	Corresponding 854×480 PNG screenshot.
action	string	Action instruction describing the transition.
image_action	Image	A 384×384 PNG diagram of the action instruction.
candidate_states	list of strings	Four candidate next states encoded as JSON.
image_candidate_states	list of Image	Four corresponding 854×480 PNG screenshots in matching order.
correct_choice	integer	Correct candidate number, from 1 to 4.
trajectory_id	string	Identifier of the source recording.
timestep	integer	Starting observation index in the processed source trajectory.
task	string	Task identifier: forward_prediction.

The correct candidate is the recorded state after the action. The other three candidates are drawn from other points in the same trajectory.

Citation

@inproceedings{cohen2026metbench,
  title={MET-Bench: Multimodal Entity Tracking for Evaluating the Limitations of Vision-Language and Reasoning Models},
  author={Cohen, Vanya and Mooney, Raymond},
  booktitle={International Conference on Machine Learning},
  year={2026},
  url={https://arxiv.org/abs/2502.10886}
}

License

MIT.