PROMETHEUS v1.0 โ World Model Interactive Demo
๐ฅ
23
World-first embodied AI world model
chohyunkyu
chohyun
AI & ML interests
None yet
Recent Activity
liked a Space 1 day ago
FINAL-Bench/World-Model liked a dataset 1 day ago
FINAL-Bench/World-Model liked a Space 1 day ago
FINAL-Bench/worldmodel-benchOrganizations
None yet
liked a
Space 1 day ago
liked a
dataset 1 day ago
liked a
Space 1 day ago
upvoted an article 1 day ago
Article
Introducing WM Bench: A Benchmark for Cognitive Intelligence in World Models
โข
13
reactedto SeaWolf-AI's post with ๐ฅ 1 day ago
Post
4451
๐ World Model Bench โ 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 โ 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 โ 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 ยท 10 Categories ยท 100 Scenarios ยท 1,000-point scale
- ๐ P1 Perception (25%) โ Can it read the scene?
- ๐ง P2 Cognition (45%) โ Does it predict threats, escalate emotions, utilize memory?
- ๐ฅ P3 Embodiment (30%) โ Does the body respond with the right motion?
All evaluation is via simple JSON I/O โ no 3D engine, no special hardware. Any model with an API can participate.
We also built PROMETHEUS as a live reference implementation โ runs in your browser on a T4, no install needed. Combines FloodDiffusion motion generation with a LLM cognitive brain (Perceive โ Predict โ Decide โ Act). Scored 726/1000 (Grade B) on Track C โ the only directly verified model so far. Submissions from other teams very welcome.
---
๐ Dataset โ FINAL-Bench/World-Model
๐ Demo โ FINAL-Bench/World-Model
๐ Leaderboard โ FINAL-Bench/worldmodel-bench
๐ Article โ https://huggingface.co/blog/FINAL-Bench/world-model
Part of the FINAL Bench Family โ alongside FINAL Bench (Feb 2026). Feedback on rubrics and missing models always welcome!
FID measures realism. FVD measures smoothness. But neither tells you whether the model understood the scene.
We just released WM Bench โ 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 โ 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 ยท 10 Categories ยท 100 Scenarios ยท 1,000-point scale
- ๐ P1 Perception (25%) โ Can it read the scene?
- ๐ง P2 Cognition (45%) โ Does it predict threats, escalate emotions, utilize memory?
- ๐ฅ P3 Embodiment (30%) โ Does the body respond with the right motion?
All evaluation is via simple JSON I/O โ no 3D engine, no special hardware. Any model with an API can participate.
We also built PROMETHEUS as a live reference implementation โ runs in your browser on a T4, no install needed. Combines FloodDiffusion motion generation with a LLM cognitive brain (Perceive โ Predict โ Decide โ Act). Scored 726/1000 (Grade B) on Track C โ the only directly verified model so far. Submissions from other teams very welcome.
---
๐ Dataset โ FINAL-Bench/World-Model
๐ Demo โ FINAL-Bench/World-Model
๐ Leaderboard โ FINAL-Bench/worldmodel-bench
๐ Article โ https://huggingface.co/blog/FINAL-Bench/world-model
Part of the FINAL Bench Family โ alongside FINAL Bench (Feb 2026). Feedback on rubrics and missing models always welcome!
liked a
Space 15 days ago
liked a
dataset 20 days ago
upvoted an article 20 days ago
Article
๐๏ธ Smol AI WorldCup: A 5-Axis Benchmark That Reveals What Small Language Models Can Really Do
โข
38
reactedto SeaWolf-AI's post with โค๏ธ 20 days ago
Post
11104
๐๏ธ Smol AI WorldCup: A 4B Model Just Beat 8B โ Here's the Data
We evaluated 18 small language models from 12 makers on 125 questions across 7 languages. The results challenge the assumption that bigger is always better.
Community Article: https://huggingface.co/blog/FINAL-Bench/smol-worldcup
Live Leaderboard: ginigen-ai/smol-worldcup
Dataset: ginigen-ai/smol-worldcup
What we found:
โ Gemma-3n-E4B (4B, 2GB RAM) outscores Qwen3-8B (8B, 5.5GB). Doubling parameters gained only 0.4 points. RAM cost: 2.75x more.
โ GPT-OSS-20B fits in 1.5GB yet matches Champions-league dense models requiring 8.5GB. MoE architecture is the edge AI game-changer.
โ Thinking models hurt structured output. DeepSeek-R1-7B scores 8.7 points below same-size Qwen3-8B and runs 2.7x slower.
โ A 1.3B model fabricates confident fake content 80% of the time when prompted with nonexistent entities. Qwen3 family hits 100% trap detection across all sizes.
โ Qwen3-1.7B (1.2GB) outscores Mistral-7B, Llama-3.1-8B, and DeepSeek-R1-14B. Latest architecture at 1.7B beats older architecture at 14B.
What makes this benchmark different?
Most benchmarks ask "how smart?" โ we measure five axes simultaneously: Size, Honesty, Intelligence, Fast, Thrift (SHIFT). Our ranking metric WCS = sqrt(SHIFT x PIR_norm) rewards models that are both high-quality AND efficient. Smart but massive? Low rank. Tiny but poor? Also low.
Top 5 by WCS:
1. GPT-OSS-20B โ WCS 82.6 โ 1.5GB โ Raspberry Pi tier
2. Gemma-3n-E4B โ WCS 81.8 โ 2.0GB โ Smartphone tier
3. Llama-4-Scout โ WCS 79.3 โ 240 tok/s โ Fastest model
4. Qwen3-4B โ WCS 76.6 โ 2.8GB โ Smartphone tier
5. Qwen3-1.7B โ WCS 76.1 โ 1.2GB โ IoT tier
Built in collaboration with the FINAL Bench research team. Interoperable with ALL Bench Leaderboard for full small-to-large model comparison.
Dataset is open under Apache 2.0 (125 questions, 7 languages). We welcome new model submissions.
We evaluated 18 small language models from 12 makers on 125 questions across 7 languages. The results challenge the assumption that bigger is always better.
Community Article: https://huggingface.co/blog/FINAL-Bench/smol-worldcup
Live Leaderboard: ginigen-ai/smol-worldcup
Dataset: ginigen-ai/smol-worldcup
What we found:
โ Gemma-3n-E4B (4B, 2GB RAM) outscores Qwen3-8B (8B, 5.5GB). Doubling parameters gained only 0.4 points. RAM cost: 2.75x more.
โ GPT-OSS-20B fits in 1.5GB yet matches Champions-league dense models requiring 8.5GB. MoE architecture is the edge AI game-changer.
โ Thinking models hurt structured output. DeepSeek-R1-7B scores 8.7 points below same-size Qwen3-8B and runs 2.7x slower.
โ A 1.3B model fabricates confident fake content 80% of the time when prompted with nonexistent entities. Qwen3 family hits 100% trap detection across all sizes.
โ Qwen3-1.7B (1.2GB) outscores Mistral-7B, Llama-3.1-8B, and DeepSeek-R1-14B. Latest architecture at 1.7B beats older architecture at 14B.
What makes this benchmark different?
Most benchmarks ask "how smart?" โ we measure five axes simultaneously: Size, Honesty, Intelligence, Fast, Thrift (SHIFT). Our ranking metric WCS = sqrt(SHIFT x PIR_norm) rewards models that are both high-quality AND efficient. Smart but massive? Low rank. Tiny but poor? Also low.
Top 5 by WCS:
1. GPT-OSS-20B โ WCS 82.6 โ 1.5GB โ Raspberry Pi tier
2. Gemma-3n-E4B โ WCS 81.8 โ 2.0GB โ Smartphone tier
3. Llama-4-Scout โ WCS 79.3 โ 240 tok/s โ Fastest model
4. Qwen3-4B โ WCS 76.6 โ 2.8GB โ Smartphone tier
5. Qwen3-1.7B โ WCS 76.1 โ 1.2GB โ IoT tier
Built in collaboration with the FINAL Bench research team. Interoperable with ALL Bench Leaderboard for full small-to-large model comparison.
Dataset is open under Apache 2.0 (125 questions, 7 languages). We welcome new model submissions.
- 1 reply
liked a
Space 20 days ago
liked a
Space about 1 month ago
liked 5
Spaces 9 months ago
Unfiltered AI NSFW Image generation
โก
359
Generate high-quality custom images from text prompts
VEO3 Directors
๐
93
Wan2.1-T2V-14B + Fast 4-step with NAG + Automatic Audio
VEO3 Free
๐
457
Wan2.1-T2V-14B + Fast 4-step with NAG + Automatic Audio
Ghibli Meme Studio
๐ผ
25
Create avatars and profile images, turning your memes
ThinkFlow
๐จ
21
thought that reasoning into LLMs without modification
liked 3
models 9 months ago
liked a
Space 9 months ago