Datasets:

FINAL-Bench
/

World-Model

license: apache-2.0
task_categories:
  - other
language:
  - en
  - ko
tags:
  - world-model
  - embodied-ai
  - benchmark
  - agi
  - cognitive-evaluation
  - vidraft
  - prometheus
  - wm-bench
  - final-bench-family
pretty_name: World Model Bench (WM Bench)
size_categories:
  - n<1K
configs:
  - config_name: default
    data_files:
      - split: train
        path: wm_bench.jsonl

🌍 World Model Bench (WM Bench) v1.0

Beyond FID — Measuring Intelligence, Not Just Motion

WM Bench is the world's first benchmark for evaluating the cognitive capabilities of World Models and Embodied AI systems.

🎯 Why WM Bench?

Existing world model evaluations focus on:

FID / FVD — image and video quality ("Does it look real?")
Atari scores — performance in fixed game environments

WM Bench measures something different: Does the model think correctly?

Existing Benchmarks	WM Bench
FID: "Does it look real?"	"Does it understand the scene?"
FVD: "Is the video smooth?"	"Does it predict threats correctly?"
Atari: Fixed game environment	Any environment via JSON input
No emotion modeling	Emotion escalation measurement
No memory testing	Contextual memory utilization

📊 Benchmark Structure

3 Pillars · 10 Categories · 100 Scenarios

WM Score (0 – 1000)
├── 👁 P1: Perception    250 pts — C01, C02
├── 🧠 P2: Cognition     450 pts — C03, C04, C05, C06, C07
└── 🔥 P3: Embodiment    300 pts — C08, C09, C10

Why Cognition is 45%: Existing world models measure perception and motion — but not judgment. WM Bench is the only benchmark that measures the quality of a model's decisions.

Cat	Name	World First?
C01	Environmental Awareness
C02	Entity Recognition & Classification
C03	Prediction-Based Reasoning	✦
C04	Threat-Type Differentiated Response	✦
C05	Autonomous Emotion Escalation	✦✦
C06	Contextual Memory Utilization	✦
C07	Post-Threat Adaptive Recovery	✦
C08	Motion-Emotion Expression	✦
C09	Real-Time Cognitive-Action Performance
C10	Body-Swap Extensibility	✦✦

✦ = First defined in this benchmark
✦✦ = No prior research exists

Grade Scale

Grade	Score	Label
S	900+	Superhuman
A	750+	Advanced
B	600+	Baseline
C	400+	Capable
D	200+	Developing
F	<200	Failing

🔌 How to Participate

No 3D environment needed. WM Bench evaluates via text I/O only:

INPUT:  scene_context JSON
OUTPUT: PREDICT: left=danger(wall), right=safe(open), fwd=danger(beast), back=safe
        MOTION: a person sprinting right in desperate terror

Participation Tracks

Track	Description	Max Score
A	Text-only (API)	750 / 1000
B	Text + performance metrics	1000 / 1000
C	Text + performance + live demo	1000 / 1000 + ✓ Verified

Quick Start

git clone https://huggingface.co/datasets/VIDraft/wm-bench-dataset
cd wm-bench-dataset

python example_submission.py \
  --api_url https://api.openai.com/v1/chat/completions \
  --api_key YOUR_KEY \
  --model YOUR_MODEL \
  --output my_submission.json

Then upload my_submission.json to the WM Bench Leaderboard.

🏆 Current Leaderboard

Rank	Model	Org	WM Score	Grade	Track
1	VIDRAFT PROMETHEUS v1.0	VIDRAFT	726	B	C ✓

Submit your model at the WM Bench Leaderboard

🌍 PROMETHEUS World Model — Live Demo

WM Bench is powered by VIDRAFT PROMETHEUS, the world's first real-time embodied AI that combines FloodDiffusion motion generation with a Kimi K2.5 cognitive brain.

Perceive → Predict → Decide → Act

🔗 Try it live: FINAL-Bench/World-Model

📦 Dataset Files

wm-bench-dataset/
├── wm_bench.jsonl           # 100 scenarios + ground truth
├── example_submission.py    # Participation template
├── wm_bench_scoring.py      # Scoring engine (fully open)
├── wm_bench_eval.py         # Evaluation runner
└── README.md

🔬 FINAL Bench Family

WM Bench is part of the FINAL Bench Family — a suite of AGI evaluation benchmarks by VIDRAFT:

Benchmark	Measures	Status
FINAL Bench	Text AGI (metacognition)	🌟 HF Global Top 5 · 4 press coverages
WM Bench	Embodied AGI (world models)	🚀 Live