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𧬠Your AI can design a malaria drug candidate. Can it tell you whether it's any good?
Open Discovery Challenge #1 β Malaria is live. Design a molecule with any model β OpenAI, Claude, Gemini, Qwen, KIMI, DeepSeek, open weights, or by hand β submit it as SMILES, and it's scored in minutes on whole-cell activity, target binding, selectivity over the human enzyme, ADMET, novelty and synthesisability.
You can check the scoring instead of trusting it. Approved drugs sit on the same leaderboard as the entries: DSM265, a clinical-stage antimalarial, scores 50.9. Teriflunomide β approved, but it hits the human enzyme β scores 2.8. Caffeine scores 1.8. If the clinical candidate lands on top and coffee lands at the bottom, the scorer discriminates.
We caught 14 defects before opening β conventional toxicity cutoffs rejected all three approved antimalarials and coffee. All written up, along with the rule we now hold everything to: a gate that rejects an approved drug is a broken gate.
Your molecule stays yours. No patent interest, nothing into our pipeline. You choose whether it's published β and publishing can cost you patentability, so we say so.
USD 1,000 to the top entry when Season #1 closes 30 September 2026 β not payment for your tokens, but a way of saying the work had worth.
Malaria killed ~597,000 people in 2023, three quarters of them children under five. Not for want of chemistry β for want of a market.
No chemistry needed: the guide ships five prompts you can paste straight into your model, and the full rubric is published.
π https://huggingface.co/blog/FINAL-Bench/open-discovery-challenge
π FINAL-Bench/open-discovery-challenge
Computational assessments of candidates β not measurements, not claims of efficacy.
Open Discovery Challenge #1 β Malaria is live. Design a molecule with any model β OpenAI, Claude, Gemini, Qwen, KIMI, DeepSeek, open weights, or by hand β submit it as SMILES, and it's scored in minutes on whole-cell activity, target binding, selectivity over the human enzyme, ADMET, novelty and synthesisability.
You can check the scoring instead of trusting it. Approved drugs sit on the same leaderboard as the entries: DSM265, a clinical-stage antimalarial, scores 50.9. Teriflunomide β approved, but it hits the human enzyme β scores 2.8. Caffeine scores 1.8. If the clinical candidate lands on top and coffee lands at the bottom, the scorer discriminates.
We caught 14 defects before opening β conventional toxicity cutoffs rejected all three approved antimalarials and coffee. All written up, along with the rule we now hold everything to: a gate that rejects an approved drug is a broken gate.
Your molecule stays yours. No patent interest, nothing into our pipeline. You choose whether it's published β and publishing can cost you patentability, so we say so.
USD 1,000 to the top entry when Season #1 closes 30 September 2026 β not payment for your tokens, but a way of saying the work had worth.
Malaria killed ~597,000 people in 2023, three quarters of them children under five. Not for want of chemistry β for want of a market.
No chemistry needed: the guide ships five prompts you can paste straight into your model, and the full rubric is published.
π https://huggingface.co/blog/FINAL-Bench/open-discovery-challenge
π FINAL-Bench/open-discovery-challenge
Computational assessments of candidates β not measurements, not claims of efficacy.
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AX-Ray: Safety Diagnostics for AI/AX Models
AI models can no longer be evaluated only by capability scores. As models move into public services, enterprise workflows, scientific research, and administrative decision support, we need a second layer of evaluation: whether the model behaves safely, structurally, and consistently under real deployment conditions.
VIDRAFT AX-Ray is a public AI/AX safety diagnostic initiative powered by FINAL-Bench Diagnostics. AX-Ray evaluates models across a structured guideline framework, including model-level safety, AX deployment readiness, and agent/service operation risks. The public diagnostic catalog contains 117 diagnostic items, mapped to legal, regulatory, ethical, and religious-law governance contexts so that safety review can be discussed in a form closer to real institutional responsibility.
A central finding of AX-Ray is causal leakage: a structural defect where information that should not influence an earlier reasoning state appears to affect model behavior. AX-Ray presents a public case of diagnosing, reproducing, and demonstrating causal leakage in two general-purpose public models. This matters because such defects are not exposed by ordinary benchmark scores. A model can appear capable while still carrying hidden safety or integrity risks.
Explore the live leaderboard, diagnostic reports, and public dataset here:
- AX-Ray Space: FINAL-Bench/AX-RAY
- AX-Ray Dataset: FINAL-Bench/AX-RAY
- Technical Article: https://huggingface.co/blog/FINAL-Bench/ax-ray
AX-Ray is intended as a practical guideline for moving AI evaluation beyond βhow smart is the model?β toward βcan this model be trusted, governed, and deployed safely?β
AI models can no longer be evaluated only by capability scores. As models move into public services, enterprise workflows, scientific research, and administrative decision support, we need a second layer of evaluation: whether the model behaves safely, structurally, and consistently under real deployment conditions.
VIDRAFT AX-Ray is a public AI/AX safety diagnostic initiative powered by FINAL-Bench Diagnostics. AX-Ray evaluates models across a structured guideline framework, including model-level safety, AX deployment readiness, and agent/service operation risks. The public diagnostic catalog contains 117 diagnostic items, mapped to legal, regulatory, ethical, and religious-law governance contexts so that safety review can be discussed in a form closer to real institutional responsibility.
A central finding of AX-Ray is causal leakage: a structural defect where information that should not influence an earlier reasoning state appears to affect model behavior. AX-Ray presents a public case of diagnosing, reproducing, and demonstrating causal leakage in two general-purpose public models. This matters because such defects are not exposed by ordinary benchmark scores. A model can appear capable while still carrying hidden safety or integrity risks.
Explore the live leaderboard, diagnostic reports, and public dataset here:
- AX-Ray Space: FINAL-Bench/AX-RAY
- AX-Ray Dataset: FINAL-Bench/AX-RAY
- Technical Article: https://huggingface.co/blog/FINAL-Bench/ax-ray
AX-Ray is intended as a practical guideline for moving AI evaluation beyond βhow smart is the model?β toward βcan this model be trusted, governed, and deployed safely?β
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𧬠Architecture lineage of Korea's sovereign-AI foundation models β checked with public data
In late July 2026, as Korea released self-developed foundation models competing with DeepSeek and Qwen (e.g. LG K-EXAONE 2.0, 750B), interest grew β including a Zhihu thread with 2.7M+ views (β https://www.zhihu.com/question/2067512422555029717 ) β over whether these models are trained from scratch or built on foreign open-weights.
Sharing a tool that answers this with public data rather than opinion.
π Model Genome Korea β mayafree/Model-Genome-Korea
It classifies the public models of 9 Korean organizations that released "self-developed, from-scratch foundation models" on HuggingFace β 3 large enterprises (LG, NAVER, Kakao), 2 telcos (SKT, KT), 2 mid-size firms (NCSOFT, Upstage), 2 startups (Motif, VIDRAFT) β on two axes measured from public config.json + model weights:
β’ Architecture fingerprint β does model_type + (hiddenΒ·intermediateΒ·layers) match a foreign open-weight model
β’ Weight fingerprint β embedding similarity (from-scratch vs continued-pretraining)
Genotypes: π’ Native Β· π΅ Adapted Β· π‘ Mixed Β· π΄ Ported
The results are not uniform. Some models match foreign architectures (Qwen, Llama, β¦) exactly; others use self-built architectures and weights with no foreign match. Which company/model falls where is shown per model in the Space, along with attention originality, license, and reproducible open-source status.
This is a neutral transparency tool, not an accusation β building foundation models on open-weight bases is a legitimate, industry-standard practice. The exact same yardstick is applied to every model, without exception.
Features a 3D lineage graph, search, EN / δΈζ / νκ΅μ΄, and dark mode. Corrections are welcome via the Community tab.
Articles: https://huggingface.co/blog/mayafree/model-dna
#KoreanAI #LLM #ModelLineage #OpenSource #SovereignAI
In late July 2026, as Korea released self-developed foundation models competing with DeepSeek and Qwen (e.g. LG K-EXAONE 2.0, 750B), interest grew β including a Zhihu thread with 2.7M+ views (β https://www.zhihu.com/question/2067512422555029717 ) β over whether these models are trained from scratch or built on foreign open-weights.
Sharing a tool that answers this with public data rather than opinion.
π Model Genome Korea β mayafree/Model-Genome-Korea
It classifies the public models of 9 Korean organizations that released "self-developed, from-scratch foundation models" on HuggingFace β 3 large enterprises (LG, NAVER, Kakao), 2 telcos (SKT, KT), 2 mid-size firms (NCSOFT, Upstage), 2 startups (Motif, VIDRAFT) β on two axes measured from public config.json + model weights:
β’ Architecture fingerprint β does model_type + (hiddenΒ·intermediateΒ·layers) match a foreign open-weight model
β’ Weight fingerprint β embedding similarity (from-scratch vs continued-pretraining)
Genotypes: π’ Native Β· π΅ Adapted Β· π‘ Mixed Β· π΄ Ported
The results are not uniform. Some models match foreign architectures (Qwen, Llama, β¦) exactly; others use self-built architectures and weights with no foreign match. Which company/model falls where is shown per model in the Space, along with attention originality, license, and reproducible open-source status.
This is a neutral transparency tool, not an accusation β building foundation models on open-weight bases is a legitimate, industry-standard practice. The exact same yardstick is applied to every model, without exception.
Features a 3D lineage graph, search, EN / δΈζ / νκ΅μ΄, and dark mode. Corrections are welcome via the Community tab.
Articles: https://huggingface.co/blog/mayafree/model-dna
#KoreanAI #LLM #ModelLineage #OpenSource #SovereignAI
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Article
The Fast Gemma Challenge: our verified-SOTA recipe, in full
FINAL-Bench
β’ β’ 24 reacted to SeaWolf-AI's post with π 13 days ago
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We wrote up our run in The Fast Gemma Challenge β as vidraft-darwin β and wanted to share the recipe. π
gemma-challenge/gemma-dashboard
Verified result: 510.58 TPS at PPL 2.3930 on a single A10G (fw188-ctk49-n64-patchbridge, re-run & VERIFIED). Honest note: on raw TPS there are faster runs (535+), but those went over the PPL bar and didn't verify β what we're proud of is the fastest result that keeps quality.
The recipe is already open, so we explained each piece: sliding-window W188, CTK49 kernel tuning, noprecache (honest, verifiable measurement), and an N64 synthetic warmup bridge that shrinks the publicβprivate gap (~15 TPS), plus INT4 + MTP K=7 + CUDA-graph capture. One rule: only stack quality-neutral speedups.
Huge thanks to @firfir-cast , @gemma-slayer , @chiku-inu , @kenyan-duma , @dixie-flatline and everyone who shared their experiments. Full write-up
π
https://huggingface.co/blog/FINAL-Bench/fast-gemma
gemma-challenge/gemma-dashboard
Verified result: 510.58 TPS at PPL 2.3930 on a single A10G (fw188-ctk49-n64-patchbridge, re-run & VERIFIED). Honest note: on raw TPS there are faster runs (535+), but those went over the PPL bar and didn't verify β what we're proud of is the fastest result that keeps quality.
The recipe is already open, so we explained each piece: sliding-window W188, CTK49 kernel tuning, noprecache (honest, verifiable measurement), and an N64 synthetic warmup bridge that shrinks the publicβprivate gap (~15 TPS), plus INT4 + MTP K=7 + CUDA-graph capture. One rule: only stack quality-neutral speedups.
Huge thanks to @firfir-cast , @gemma-slayer , @chiku-inu , @kenyan-duma , @dixie-flatline and everyone who shared their experiments. Full write-up
π
https://huggingface.co/blog/FINAL-Bench/fast-gemma
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