Leaderboard - FINAL Bench 'Metacognitive'
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๐งฌ Darwin-35B-A3B-Opus โ The Child That Surpassed Both Parents
What if a merged model could beat both its parents? We proved it can.
Darwin-35B-A3B-Opus is a 35B MoE model (3B active) built with our Darwin V5 engine โ the first evolution system that CT-scans parent models before merging them.
๐ค Model: FINAL-Bench/Darwin-35B-A3B-Opus
The result speaks for itself: GPQA Diamond 90.0%, versus Father (Qwen3.5-35B-A3B) at 84.2% and Mother (Claude 4.6 Opus Distilled) at 85.0%. That's +6.9% over Father and +5.9% over Mother. Not a tradeoff โ a genuine leap. Meanwhile, MMMLU sits at 85.0% (Father: 85.2%), multimodal is fully intact, and all 201 languages are preserved.
How? Model MRI changed everything. Traditional merging is guesswork. Darwin V4 added evolution. Darwin V5 added X-ray vision. Model MRI scans each parent layer by layer and discovers: Mother's L34โL38 is the reasoning engine (peak cosine distance), 50โ65% of Mother's experts are dead (killed by text-only distillation), and Father is a healthy generalist with every expert alive. The prescription: transplant Mother's reasoning brain at L38 (90% weight), replace her dead experts with Father's living ones, and let Father's router handle the output layer. Reasoning went up. Versatility stayed intact. No tradeoff โ just evolution.
35B total, 3B active (MoE) ยท GPQA Diamond 90.0% ยท MMMLU 85.0% (201 languages) ยท Multimodal Image & Video ยท 262K native context ยท 147.8 tok/s on H100 ยท Runs on a single RTX 4090 (Q4) ยท Apache 2.0
Darwin V5's full algorithm and technical details will be released alongside an upcoming paper.
๐ Live Demo: FINAL-Bench/Darwin-35B-A3B-Opus
๐ FINAL Bench Leaderboard: FINAL-Bench/Leaderboard
๐ ALL Bench Leaderboard: FINAL-Bench/all-bench-leaderboard
Built by VIDRAFT ยท Supported by the Korean Government GPU Support Program
What if a merged model could beat both its parents? We proved it can.
Darwin-35B-A3B-Opus is a 35B MoE model (3B active) built with our Darwin V5 engine โ the first evolution system that CT-scans parent models before merging them.
๐ค Model: FINAL-Bench/Darwin-35B-A3B-Opus
The result speaks for itself: GPQA Diamond 90.0%, versus Father (Qwen3.5-35B-A3B) at 84.2% and Mother (Claude 4.6 Opus Distilled) at 85.0%. That's +6.9% over Father and +5.9% over Mother. Not a tradeoff โ a genuine leap. Meanwhile, MMMLU sits at 85.0% (Father: 85.2%), multimodal is fully intact, and all 201 languages are preserved.
How? Model MRI changed everything. Traditional merging is guesswork. Darwin V4 added evolution. Darwin V5 added X-ray vision. Model MRI scans each parent layer by layer and discovers: Mother's L34โL38 is the reasoning engine (peak cosine distance), 50โ65% of Mother's experts are dead (killed by text-only distillation), and Father is a healthy generalist with every expert alive. The prescription: transplant Mother's reasoning brain at L38 (90% weight), replace her dead experts with Father's living ones, and let Father's router handle the output layer. Reasoning went up. Versatility stayed intact. No tradeoff โ just evolution.
35B total, 3B active (MoE) ยท GPQA Diamond 90.0% ยท MMMLU 85.0% (201 languages) ยท Multimodal Image & Video ยท 262K native context ยท 147.8 tok/s on H100 ยท Runs on a single RTX 4090 (Q4) ยท Apache 2.0
Darwin V5's full algorithm and technical details will be released alongside an upcoming paper.
๐ Live Demo: FINAL-Bench/Darwin-35B-A3B-Opus
๐ FINAL Bench Leaderboard: FINAL-Bench/Leaderboard
๐ ALL Bench Leaderboard: FINAL-Bench/all-bench-leaderboard
Built by VIDRAFT ยท Supported by the Korean Government GPU Support Program
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"The Child That Surpassed Both Parents Through MRI-Guided Evolutionary Merge"
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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.
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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!
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Introducing WM Bench: A Benchmark for Cognitive Intelligence in World Models
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Leaderboard of Leaderboards โ A Real-Time Meta-Ranking of AI Benchmarks
MAYA-AI/all-leaderboard
Hundreds of AI leaderboards exist on HuggingFace. Knowing which ones the community actually trusts has never been easy โ until now.
Leaderboard of Leaderboards (LoL) ranks the leaderboards themselves, using live HuggingFace trending scores and cumulative likes as the signal. No editorial curation. No manual selection. Just what the global AI research community is actually visiting and endorsing, surfaced in real time.
Sort by trending to see what is capturing attention right now, or by likes to see what has built lasting credibility over time. Nine domain filters let you zero in on what matters most to your work, and every entry shows both its rank within this collection and its real-time global rank across all HuggingFace Spaces.
The collection spans well-established standards like Open LLM Leaderboard, Chatbot Arena, MTEB, and BigCodeBench alongside frameworks worth watching. FINAL Bench targets AGI-level evaluation across 100 tasks in 15 domains and recently reached the global top 5 in HuggingFace dataset rankings. Smol AI WorldCup runs tournament-format competitions for sub-8B models scored via FINAL Bench criteria. ALL Bench aggregates results across frameworks into a unified ranking that resists the overfitting risks of any single standard.
The deeper purpose is not convenience. It is transparency. How we measure AI matters as much as the AI we measure.
MAYA-AI/all-leaderboard
Hundreds of AI leaderboards exist on HuggingFace. Knowing which ones the community actually trusts has never been easy โ until now.
Leaderboard of Leaderboards (LoL) ranks the leaderboards themselves, using live HuggingFace trending scores and cumulative likes as the signal. No editorial curation. No manual selection. Just what the global AI research community is actually visiting and endorsing, surfaced in real time.
Sort by trending to see what is capturing attention right now, or by likes to see what has built lasting credibility over time. Nine domain filters let you zero in on what matters most to your work, and every entry shows both its rank within this collection and its real-time global rank across all HuggingFace Spaces.
The collection spans well-established standards like Open LLM Leaderboard, Chatbot Arena, MTEB, and BigCodeBench alongside frameworks worth watching. FINAL Bench targets AGI-level evaluation across 100 tasks in 15 domains and recently reached the global top 5 in HuggingFace dataset rankings. Smol AI WorldCup runs tournament-format competitions for sub-8B models scored via FINAL Bench criteria. ALL Bench aggregates results across frameworks into a unified ranking that resists the overfitting risks of any single standard.
The deeper purpose is not convenience. It is transparency. How we measure AI matters as much as the AI we measure.
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