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π₯
Wan_VO- generate a video from an image with a text prompt
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reacted to SeaWolf-AI's post with π₯ about 8 hours ago
π World Model Bench β does your world model actually think?
FID measures realism. FVD measures smoothness. But neither tells you whether the model understood the scene.
We just released WM Bench β the first benchmark for cognitive intelligence in world models. The core question: when a beast charges from 3 meters away, does the model know to sprint β not walk? Does it respond differently to a human vs an animal? Does it remember the left corridor was blocked two steps ago?
Those are cognitive questions. No existing benchmark asks them. So we built one.
3 Pillars Β· 10 Categories Β· 100 Scenarios Β· 1,000-point scale
- π P1 Perception (25%) β Can it read the scene?
- π§ P2 Cognition (45%) β Does it predict threats, escalate emotions, utilize memory?
- π₯ P3 Embodiment (30%) β Does the body respond with the right motion?
All evaluation is via simple JSON I/O β no 3D engine, no special hardware. Any model with an API can participate.
We also built PROMETHEUS as a live reference implementation β runs in your browser on a T4, no install needed. Combines FloodDiffusion motion generation with a LLM cognitive brain (Perceive β Predict β Decide β Act). Scored 726/1000 (Grade B) on Track C β the only directly verified model so far. Submissions from other teams very welcome.
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π Dataset β https://huggingface.co/datasets/FINAL-Bench/World-Model
π Demo β https://huggingface.co/spaces/FINAL-Bench/World-Model
π Leaderboard β https://huggingface.co/spaces/FINAL-Bench/worldmodel-bench
π Article β https://huggingface.co/blog/FINAL-Bench/world-model
Part of the FINAL Bench Family β alongside FINAL Bench (Feb 2026). Feedback on rubrics and missing models always welcome! updated a Space about 10 hours ago
ginigen/siteagent-serverside published a Space about 10 hours ago
ginigen/siteagent-serversideOrganizations
