Article
Writing Down the Line Between Luck and Skill
FINAL-Bench
• • 13
TaeHyun Kim
Ctulu82
AI & ML interests
None yet
Recent Activity
upvoted an article 4 days ago
Writing Down the Line Between Luck and Skill reacted to SeaWolf-AI's post with 🤗 4 days ago
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 −1.0 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 — 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 −25%. Donchian breakout does the exact opposite — 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 — buy and hold, volatility targeting, random — 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.
https://huggingface.co/spaces/FINAL-Bench/finchal
https://huggingface.co/blog/FINAL-Bench/financial-forecast-challenge reacted to SeaWolf-AI's post with 😎 4 days ago
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 −1.0 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 — 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 −25%. Donchian breakout does the exact opposite — 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 — buy and hold, volatility targeting, random — 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.
https://huggingface.co/spaces/FINAL-Bench/finchal
https://huggingface.co/blog/FINAL-Bench/financial-forecast-challengeOrganizations
upvoted an article 4 days ago
reacted to SeaWolf-AI's post with 🤗😎❤️👀👍🔥 4 days ago
Post
2875
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 −1.0 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 — 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 −25%. Donchian breakout does the exact opposite — 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 — buy and hold, volatility targeting, random — 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 −1.0 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 — 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 −25%. Donchian breakout does the exact opposite — 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 — buy and hold, volatility targeting, random — 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
reacted to SeaWolf-AI's post with ❤️🔥🚀😎 6 days ago
Post
3717
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
- 6 replies
liked a
Space 6 days ago
reacted to SeaWolf-AI's post with ❤️🚀🔥😎👍 14 days ago
Post
3301
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 “how smart is the model?” toward “can this model be trusted, governed, and deployed safely?”
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 “how smart is the model?” toward “can this model be trusted, governed, and deployed safely?”
- 2 replies
upvoted an article 14 days ago
Article
AX-Ray, Finding Causal-Leakage Defects in Two General-Purpose Public Models
FINAL-Bench
• • 13 liked a
dataset 14 days ago
liked a
Space 14 days ago