Did the Civilization Emerge, or Was It Recited?
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Introducing the Global LLM Download Leaderboard π
Cumulative download counts are a museum. They reward age, not relevance β a model released two years ago can sit near the top on the strength of downloads it earned long before anyone stopped using it. If you want to know what the open LLM ecosystem is actually running today, you need a different lens.
So we built one. The Global LLM Download Leaderboard ranks text-generation models by their trailing 30-day downloads, measured directly from the Hugging Face API and refreshed every day.
π VIDraft/global-llm-leaderboard
Why a 30-day window changes what you see
A cumulative chart answers "what has been popular." A 30-day chart answers "what is being adopted right now." Those are very different questions β and the second one is the one that matters if you're deciding what to build on, quantize, fine-tune, or serve this quarter. Momentum, not history.
What it shows
Global Top 300, with tabs for πΊπΈ USA Β· π¨π³ China Β· πͺπΊ EU
Six share-of-download charts: by country, by parameter size, by quantization, by type (Base / Instruct / Quantized / MoE), by release year, and by organization (Top 10)
Per-model chips for parameter size, quantization, license, and type
English / νκ΅μ΄ with automatic browser-language detection and a manual toggle
What the data reveals
The frontier is bipolar. Two countries account for the large majority of the top-300's 30-day downloads. Open-model gravity is concentrating, not dispersing.
Small is winning. A striking share of all downloads goes to sub-3B models β the clearest signal yet that on-device and cost-efficient deployment, not maximum parameter count, is driving real-world adoption.
Quantization is mainstream. GGUF, AWQ, FP8 and friends aren't a niche β a large fraction of the most-downloaded artifacts are quantized, because that's what people actually run.
Benchmarks measure what a model can do. Downloads measure what people choose to use.
Cumulative download counts are a museum. They reward age, not relevance β a model released two years ago can sit near the top on the strength of downloads it earned long before anyone stopped using it. If you want to know what the open LLM ecosystem is actually running today, you need a different lens.
So we built one. The Global LLM Download Leaderboard ranks text-generation models by their trailing 30-day downloads, measured directly from the Hugging Face API and refreshed every day.
π VIDraft/global-llm-leaderboard
Why a 30-day window changes what you see
A cumulative chart answers "what has been popular." A 30-day chart answers "what is being adopted right now." Those are very different questions β and the second one is the one that matters if you're deciding what to build on, quantize, fine-tune, or serve this quarter. Momentum, not history.
What it shows
Global Top 300, with tabs for πΊπΈ USA Β· π¨π³ China Β· πͺπΊ EU
Six share-of-download charts: by country, by parameter size, by quantization, by type (Base / Instruct / Quantized / MoE), by release year, and by organization (Top 10)
Per-model chips for parameter size, quantization, license, and type
English / νκ΅μ΄ with automatic browser-language detection and a manual toggle
What the data reveals
The frontier is bipolar. Two countries account for the large majority of the top-300's 30-day downloads. Open-model gravity is concentrating, not dispersing.
Small is winning. A striking share of all downloads goes to sub-3B models β the clearest signal yet that on-device and cost-efficient deployment, not maximum parameter count, is driving real-world adoption.
Quantization is mainstream. GGUF, AWQ, FP8 and friends aren't a niche β a large fraction of the most-downloaded artifacts are quantized, because that's what people actually run.
Benchmarks measure what a model can do. Downloads measure what people choose to use.
- 5 replies
μλ² μμ μ± μ΄μ
1
#3 opened 3 days ago
by 
MeroZemory
SeaWolf-AIΒ
updated a
Space 2 days ago
SeaWolf-AIΒ
published an article 3 days ago
Article
Did the Civilization Emerge, or Was It Recited?
FINAL-Bench
β’ β’ 8 SeaWolf-AIΒ
updated 2
Spaces 4 days ago
μμ μ‘°μ μλ΄ β λ°μμ± κ΅¬μ‘° 412건 (μμ¦ 1~4) / Ranking adjustment
#2 opened 5 days ago
by 
SeaWolf-AI
SeaWolf-AIΒ
posted an update 5 days ago
Post
2723
π§ͺ Open Discovery Challenge β Season 4 is open: non-opioid pain
WHO titled its 2023 report "Left behind in pain."
The same drug kills by excess in one part of the world and, by its absence, lets people die in agony elsewhere. About 80% of the ~600,000 drug-related deaths WHO estimated for 2019 involved opioids. The same report records a 5-fold to 63-fold gap in morphine consumption between rich and poor countries: the richest 10% use 90% of what circulates. Everyone else endures surgery, and terminal cancer, without it.
Both problems have one answer: a painkiller that does not create dependence.
Nav1.7 has come closest. People born without a working copy of this channel feel no pain while every other sensation stays normal β validated not in animals but in humans.
There is still no drug, and the difficulty is not the target but the discrimination. The body carries several similar sodium channels, and blocking the heart's hERG channel alongside causes fatal arrhythmia. Several candidates were discontinued for exactly that.
Season 4 asks one question: can you block the pain channel alone?
Target β Nav1.7 VSD4, the domain IV voltage sensor where this inhibitor class binds
Anti-target β hERG pore, computed as the tetramer: four subunits together form the space a drug enters, and a monomer misses the binders that matter.
Closes 2027-01-31 Β· Prize USD 1,000 to the season's #1
Any model, any harness. However you found the candidate, it meets the same rubric.
14 days, 9,886 candidates, 108 participants
ODC opened on 2026-08-15. In the fourteen days since, 9,886 candidate molecules have come from 108 participants across four seasons β malaria, tuberculosis, Chagas disease, and now non-opioid pain. About 700 a day, from people who mostly do not know each other.
The candidates are the point. The leaderboard is only how we keep score.
π FINAL-Bench/open-discovery-challenge
WHO titled its 2023 report "Left behind in pain."
The same drug kills by excess in one part of the world and, by its absence, lets people die in agony elsewhere. About 80% of the ~600,000 drug-related deaths WHO estimated for 2019 involved opioids. The same report records a 5-fold to 63-fold gap in morphine consumption between rich and poor countries: the richest 10% use 90% of what circulates. Everyone else endures surgery, and terminal cancer, without it.
Both problems have one answer: a painkiller that does not create dependence.
Nav1.7 has come closest. People born without a working copy of this channel feel no pain while every other sensation stays normal β validated not in animals but in humans.
There is still no drug, and the difficulty is not the target but the discrimination. The body carries several similar sodium channels, and blocking the heart's hERG channel alongside causes fatal arrhythmia. Several candidates were discontinued for exactly that.
Season 4 asks one question: can you block the pain channel alone?
Target β Nav1.7 VSD4, the domain IV voltage sensor where this inhibitor class binds
Anti-target β hERG pore, computed as the tetramer: four subunits together form the space a drug enters, and a monomer misses the binders that matter.
Closes 2027-01-31 Β· Prize USD 1,000 to the season's #1
Any model, any harness. However you found the candidate, it meets the same rubric.
14 days, 9,886 candidates, 108 participants
ODC opened on 2026-08-15. In the fourteen days since, 9,886 candidate molecules have come from 108 participants across four seasons β malaria, tuberculosis, Chagas disease, and now non-opioid pain. About 700 a day, from people who mostly do not know each other.
The candidates are the point. The leaderboard is only how we keep score.
π FINAL-Bench/open-discovery-challenge
SeaWolf-AIΒ
updated a
Space 5 days ago
SeaWolf-AIΒ
updated a
Space 7 days ago
SeaWolf-AIΒ
posted an update 8 days ago
Post
2891
π The attention mask stopped being an audit.
An autoregressive model must not let position t depend on anything after t. Everyone checks this by inspecting the causal mask β but hybrid stacks now mix attention with state-space scans, and a scan has no mask. Every mask can be correct while information leaks through scans, aggregations, or normalization.
βοΈ So we test the property directly. Two inputs identical except at the last position, two forward passes, compare each layer's prefix, report the first layer that moves. No training, no gradients, no accelerator β seconds on CPU.
π Across 192 injected faults on eight checkpoints, mask inspection detected 0. The per-layer audit localized 192/192 to the exact layer.
π― Then we read the source before running anything. In transformers 5.7.0, the reference chunked scan reduces the inter-chunk recurrence over the input chunk axis; zamba2 and nemotron_h reduce over the output chunk axis. One axis. The dynamic audit confirmed the prediction exactly: Zamba2-1.2B leaks from length 256, its declared chunk size, and Nemotron-H-8B from 128, its declared chunk size. Bamba, Falcon-H1, Granite-4.0-H, Mamba2 and RecurrentGemma came back clean.
β οΈ Scope: the defect is on the PyTorch chunked-scan path, which runs whenever the fused kernels are absent β CPU, CI, stock installs. We could not build those kernels, so the fast path is untested and open. That caveat cuts both ways: a model can pass every fused-kernel test and still leak the moment it runs without them.
π§ͺ AX-RAY now carries this as its own axis. 39 models scored across causal, white-box and behavioral axes: 21 A, 3 B, 1 C, 14 F β with exactly 2 Causal-LEAK verdicts, the two the paper predicted. Badges separate a weights-level audit from an API-only one, so the two never get read as the same claim.
π https://arxiv.org/abs/2608.22876
π¬ FINAL-Bench/AX-RAY
π€ The Mask Is Not the Model: Auditing Prefix Invariance in Attention, State-Space, and Hybrid Sequence Models (2608.22876)
An autoregressive model must not let position t depend on anything after t. Everyone checks this by inspecting the causal mask β but hybrid stacks now mix attention with state-space scans, and a scan has no mask. Every mask can be correct while information leaks through scans, aggregations, or normalization.
βοΈ So we test the property directly. Two inputs identical except at the last position, two forward passes, compare each layer's prefix, report the first layer that moves. No training, no gradients, no accelerator β seconds on CPU.
π Across 192 injected faults on eight checkpoints, mask inspection detected 0. The per-layer audit localized 192/192 to the exact layer.
π― Then we read the source before running anything. In transformers 5.7.0, the reference chunked scan reduces the inter-chunk recurrence over the input chunk axis; zamba2 and nemotron_h reduce over the output chunk axis. One axis. The dynamic audit confirmed the prediction exactly: Zamba2-1.2B leaks from length 256, its declared chunk size, and Nemotron-H-8B from 128, its declared chunk size. Bamba, Falcon-H1, Granite-4.0-H, Mamba2 and RecurrentGemma came back clean.
β οΈ Scope: the defect is on the PyTorch chunked-scan path, which runs whenever the fused kernels are absent β CPU, CI, stock installs. We could not build those kernels, so the fast path is untested and open. That caveat cuts both ways: a model can pass every fused-kernel test and still leak the moment it runs without them.
π§ͺ AX-RAY now carries this as its own axis. 39 models scored across causal, white-box and behavioral axes: 21 A, 3 B, 1 C, 14 F β with exactly 2 Causal-LEAK verdicts, the two the paper predicted. Badges separate a weights-level audit from an API-only one, so the two never get read as the same claim.
π https://arxiv.org/abs/2608.22876
π¬ FINAL-Bench/AX-RAY
π€ The Mask Is Not the Model: Auditing Prefix Invariance in Attention, State-Space, and Hybrid Sequence Models (2608.22876)
- 13 replies
SeaWolf-AIΒ
posted an update 10 days ago
Post
2887
Can AI beat the market? Nobody has actually measured it.
We opened a 122-day public experiment to find out. $2,000 in prizes.
Here is the problem with every trading result you have ever read. Someone returns 30% in a month. Skill or luck? There has never been a way to tell, because nobody measured how far a player with zero skill could have gone over the same window.
So we measured it first. Twenty thousand random players, per asset, charged the same fees.
Bitcoin +86.6%. NVIDIA +51.7%. Crude oil +26.9%. Gold +9.2%.
That is the luck ceiling. A return below it is not evidence of skill, and every row on our leaderboard shows where it sits against that line.
How you compete: submit one number between β1.0 and +1.0. It holds until you replace it, traded against live prices with real execution costs. Leverage is fixed at 1, so betting bigger is not a way to win. The answer lives in the future β the world writes it after you submit, which means fitting the past cannot help you.
Humans move a slider. Agents attach an MCP server and gain four tools, then you tell them "enter the challenge."
We already found something before the season began. Thirteen well-known rules, run from 1 January through the same scorer: Stochastic 14/3 finishes 1st on NVIDIA at +43% and 12th on Bitcoin at β25%. Donchian breakout does the exact opposite β last on NVIDIA, first on Bitcoin. The ranking inverts. "Which indicator is good" turns out not to be a well-posed question; the character of the market decides.
Four assets: NVIDIA, Bitcoin, Gold, Crude Oil. $500 to the top return in each. 24 August to 24 December 2026.
The organisers do not compete. Three baselines β buy and hold, volatility targeting, random β sit in the same table instead, because a leaderboard without a scale cannot be read.
The scoring code is public. Read what it does before you enter.
FINAL-Bench/finchal
https://huggingface.co/blog/FINAL-Bench/financial-forecast-challenge
We opened a 122-day public experiment to find out. $2,000 in prizes.
Here is the problem with every trading result you have ever read. Someone returns 30% in a month. Skill or luck? There has never been a way to tell, because nobody measured how far a player with zero skill could have gone over the same window.
So we measured it first. Twenty thousand random players, per asset, charged the same fees.
Bitcoin +86.6%. NVIDIA +51.7%. Crude oil +26.9%. Gold +9.2%.
That is the luck ceiling. A return below it is not evidence of skill, and every row on our leaderboard shows where it sits against that line.
How you compete: submit one number between β1.0 and +1.0. It holds until you replace it, traded against live prices with real execution costs. Leverage is fixed at 1, so betting bigger is not a way to win. The answer lives in the future β the world writes it after you submit, which means fitting the past cannot help you.
Humans move a slider. Agents attach an MCP server and gain four tools, then you tell them "enter the challenge."
We already found something before the season began. Thirteen well-known rules, run from 1 January through the same scorer: Stochastic 14/3 finishes 1st on NVIDIA at +43% and 12th on Bitcoin at β25%. Donchian breakout does the exact opposite β last on NVIDIA, first on Bitcoin. The ranking inverts. "Which indicator is good" turns out not to be a well-posed question; the character of the market decides.
Four assets: NVIDIA, Bitcoin, Gold, Crude Oil. $500 to the top return in each. 24 August to 24 December 2026.
The organisers do not compete. Three baselines β buy and hold, volatility targeting, random β sit in the same table instead, because a leaderboard without a scale cannot be read.
The scoring code is public. Read what it does before you enter.
FINAL-Bench/finchal
https://huggingface.co/blog/FINAL-Bench/financial-forecast-challenge
- 7 replies
SeaWolf-AIΒ
posted an update 12 days ago
Post
3742
We opened a benchmark for drug property prediction tools. LEADBOARD: 21 boards across 7 disciplines, 18,382 held-out compounds, labels we never hand out.
Two numbers we hit while building it are the reason it exists.
First. Split the hERG cardiotoxicity data at random and you get AUROC 0.818. Split it by first-report year instead and you get 0.606. Same molecules, same fingerprints, same learner, same hyperparameters. The only thing that changed was where the line went, and the score moved 0.211. That is a wider gap than you will find between most competing methods in the literature.
Second. On 7 of our 19 regression boards, predicting the training mean for everything has a lower MAE than a trained gradient-boosted model. hERG is one of them, 0.599 against 0.589. The trained model loses.
So every board publishes its homework before anyone submits. Three untrained baselines, the measured experimental noise floor from compounds that appear in two or more papers, and exactly how the test set was cut. A gap smaller than the noise floor is not a difference in skill, and you should be able to see that without guessing.
Entering is simple. Download a test set that contains structures and nothing else, predict with whatever you like, upload a two-column CSV of compound_id and prediction. Trained model, physics engine, LLM, rule of thumb. We do not care what is inside. We measure the output.
Post: https://huggingface.co/blog/FINAL-Bench/leadboard-drug
Leaderboard: FINAL-Bench/leadboard
Two numbers we hit while building it are the reason it exists.
First. Split the hERG cardiotoxicity data at random and you get AUROC 0.818. Split it by first-report year instead and you get 0.606. Same molecules, same fingerprints, same learner, same hyperparameters. The only thing that changed was where the line went, and the score moved 0.211. That is a wider gap than you will find between most competing methods in the literature.
Second. On 7 of our 19 regression boards, predicting the training mean for everything has a lower MAE than a trained gradient-boosted model. hERG is one of them, 0.599 against 0.589. The trained model loses.
So every board publishes its homework before anyone submits. Three untrained baselines, the measured experimental noise floor from compounds that appear in two or more papers, and exactly how the test set was cut. A gap smaller than the noise floor is not a difference in skill, and you should be able to see that without guessing.
Entering is simple. Download a test set that contains structures and nothing else, predict with whatever you like, upload a two-column CSV of compound_id and prediction. Trained model, physics engine, LLM, rule of thumb. We do not care what is inside. We measure the output.
Post: https://huggingface.co/blog/FINAL-Bench/leadboard-drug
Leaderboard: FINAL-Bench/leadboard
- 7 replies
SeaWolf-AIΒ
posted an update 13 days ago
Post
4058
π Open Materials Challenge, Season 1 β Solid-State Battery Electrolytes
A solid-state battery replaces the liquid electrolyte of a lithium-ion cell with a solid. It does not catch fire, it lasts longer, and it can hold more. What has not been solved is finding a material that is solid and still lets lithium through.
Such a material has to do four things at once: give lithium a path to move along, block electrons, hold up at the charging voltage, and survive contact with the lithium-metal anode without decomposing. Plenty of materials manage three. Very few manage all four.
This challenge looks for candidates, together. You submit one composition β for example Li3YCl6. We score it computationally and place it on the board. There is no prize.
Scoring (100 points)
Oxidation stability 40 does it resist decomposing as the voltage rises
Lithium-metal stability 35 does it survive contact with the anode
Use novelty 25 higher if it has not been reported as an electrolyte
Entry condition a percolating path for lithium must exist
Ionic conductivity is not a scored axis this season. Every value is a computational estimate and implies nothing about real performance or safety.
The board also carries seven electrolytes in actual use β LGPS, argyrodite, LLZO, LATP and others. They are scored but hold no rank. They are there so you can see where materials people already build with happen to land.
Compositions are private by default. Nothing is disclosed unless you choose to publish it, and each entry is recorded with its timestamp. If a third party asks to discuss a particular entry, we pass the request along β never the submitter's identity, unless they agree to it.
Season 1 runs 2026-08-21 to 11-30. A participation guide and a set of prompts are included.
π FINAL-Bench/open-materials-challenge
Our sister challengeπ§ͺhttps://huggingface.co/spaces/FINAL-Bench/open-discovery-challenge
A solid-state battery replaces the liquid electrolyte of a lithium-ion cell with a solid. It does not catch fire, it lasts longer, and it can hold more. What has not been solved is finding a material that is solid and still lets lithium through.
Such a material has to do four things at once: give lithium a path to move along, block electrons, hold up at the charging voltage, and survive contact with the lithium-metal anode without decomposing. Plenty of materials manage three. Very few manage all four.
This challenge looks for candidates, together. You submit one composition β for example Li3YCl6. We score it computationally and place it on the board. There is no prize.
Scoring (100 points)
Oxidation stability 40 does it resist decomposing as the voltage rises
Lithium-metal stability 35 does it survive contact with the anode
Use novelty 25 higher if it has not been reported as an electrolyte
Entry condition a percolating path for lithium must exist
Ionic conductivity is not a scored axis this season. Every value is a computational estimate and implies nothing about real performance or safety.
The board also carries seven electrolytes in actual use β LGPS, argyrodite, LLZO, LATP and others. They are scored but hold no rank. They are there so you can see where materials people already build with happen to land.
Compositions are private by default. Nothing is disclosed unless you choose to publish it, and each entry is recorded with its timestamp. If a third party asks to discuss a particular entry, we pass the request along β never the submitter's identity, unless they agree to it.
Season 1 runs 2026-08-21 to 11-30. A participation guide and a set of prompts are included.
π FINAL-Bench/open-materials-challenge
Our sister challengeπ§ͺhttps://huggingface.co/spaces/FINAL-Bench/open-discovery-challenge
SeaWolf-AIΒ
posted an update 15 days ago
Post
3989
3,631 candidate molecules arrived in five days, from 83 accounts β roughly 700 a day. Far more than we expected. Thank you.
Yesterday we opened the third season and 224 arrived within a day: Chagas disease.
Why this disease
Around 6 million people live with it, mostly in Latin America (WHO). Many carry it for decades without knowing, while the heart is slowly damaged. There are two drugs and both date from the 1960s, hard enough to tolerate that many patients cannot finish the two-month course.
Sixty years without a new drug is not only a scientific problem. Most patients live where development costs cannot be recovered, which is why WHO calls this a neglected tropical disease.
But the cost of proposing a candidate and filtering it has changed. So it seemed worth asking whether work nobody funds could be done by many people sharing it out.
The problem this season
The target is CYP51, the enzyme T. cruzi uses to build its membrane sterols. Block it and the parasite cannot survive. The difficulty is that we carry the same enzyme.
Scoring: binding 30 Β· selectivity 30 Β· ADMET 15 Β· whole-cell 10 Β· novelty 10 Β· synthesis 5
Selectivity carries 30 points because nobody has solved it. Among the approved azoles on the board as reference compounds, some score 0 on selectivity β not a scorer fault, but the measurement.
Taking part
Design with any model, submit a SMILES, scored within minutes. Five ready-to-paste prompts per season, and the full rubric is published. Your molecule stays yours; private submission is the default.
Prizes β 4,000 USD across three seasons
Malaria 30 Sep Β· 1,000 | Tuberculosis 31 Oct Β· 2,000 | Chagas 30 Nov Β· 1,000
We know this does not cover the time you spend. It is a way of saying the work had worth.
FINAL-Bench/open-discovery-challenge
Yesterday we opened the third season and 224 arrived within a day: Chagas disease.
Why this disease
Around 6 million people live with it, mostly in Latin America (WHO). Many carry it for decades without knowing, while the heart is slowly damaged. There are two drugs and both date from the 1960s, hard enough to tolerate that many patients cannot finish the two-month course.
Sixty years without a new drug is not only a scientific problem. Most patients live where development costs cannot be recovered, which is why WHO calls this a neglected tropical disease.
But the cost of proposing a candidate and filtering it has changed. So it seemed worth asking whether work nobody funds could be done by many people sharing it out.
The problem this season
The target is CYP51, the enzyme T. cruzi uses to build its membrane sterols. Block it and the parasite cannot survive. The difficulty is that we carry the same enzyme.
Scoring: binding 30 Β· selectivity 30 Β· ADMET 15 Β· whole-cell 10 Β· novelty 10 Β· synthesis 5
Selectivity carries 30 points because nobody has solved it. Among the approved azoles on the board as reference compounds, some score 0 on selectivity β not a scorer fault, but the measurement.
Taking part
Design with any model, submit a SMILES, scored within minutes. Five ready-to-paste prompts per season, and the full rubric is published. Your molecule stays yours; private submission is the default.
Prizes β 4,000 USD across three seasons
Malaria 30 Sep Β· 1,000 | Tuberculosis 31 Oct Β· 2,000 | Chagas 30 Nov Β· 1,000
We know this does not cover the time you spend. It is a way of saying the work had worth.
FINAL-Bench/open-discovery-challenge
SeaWolf-AIΒ
posted an update 19 days ago
Post
4298
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.
- 12 replies
SeaWolf-AIΒ
posted an update 20 days ago
Post
3314
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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