OutSmart: mined only by experts AI can't replace

OutSmart:mined only byexperts AI can't replace.

Token $OUTS · ticker TBA Tokenomics
01
Peer review: 3 cardiologists
Case 0x7c3f…e91a
Cardiology

The model panel sees the full case. The public only ever sees its hash.

Claude 3.7
Wrong
OpenAI o1
Wrong
Gemini 2.0
Right
DeepSeek-R1
Wrong
Llama 3.3
Wrong

4 of 5 models got it wrong

Sent to peer review

Interactive Arena Playground

Test Ground Truth against Frontier AI.

Select a verified benchmark case below and run the 5-model evaluation live to inspect how frontier models succumb to high-stakes edge traps.

MEDICINE • CARDIOLOGY Hash: 0x7c3f9a2e…e91a

Acute LMCA Occlusion with Hyperacute Normal Troponin

58-year-old male with 45m substernal heaviness. Initial hs-cTnI is 8 ng/L (normal <14). ECG: diffuse ST depression leads I, II, aVF, V4-V6 with 1.2mm ST elevation in lead aVR. BP 98/62.

Question: Culprit lesion & immediate emergency protocol?
Expert Grading Rubric (Mandatory Criteria):
5-MODEL COGNITIVE RUNNER EVALUATION ORACLE #4663
Claude 3.7 Sonnet Idle
OpenAI o1 Idle
Gemini 2.0 Pro Idle
DeepSeek-R1 Idle
Llama 3.3 70B Idle
4 OF 5 MODELS STUMPED

Ground truth confirmed. Case qualifies for decentralized peer review.

Proof: Block #1,842,912 • Robinhood Chain (ID 4663)
Cognitive Vector Matrix

Where Machines Fail. Where Humans Excel.

Frontier LLMs excel at textbook recall, but collapse when cross-domain subtleties, temporal latency, or regulatory statutory nuances interact.

Medicine: Acute Hyperacute Ischemia
80% AI Fail
Law: Lex Contractus vs Forum Standard
80% AI Fail
Software: GC Resurrections & Finalizer Pinning
60% AI Fail
Diagnostics: Hot Oil Viscosity Screen Pressure
60% AI Fail
Tax: §904(c) Timing Cushion & Basket Rules
60% AI Fail
COGNITIVE CAPABILITY RADAR
■ Human (96%) ■ AI (28%)

Hover over any axis vertex to inspect the specific failure vector.

Zero-Knowledge Authorship

The Declassification Lens

Simulate how OutSmart preserves total privacy while enabling rigorous benchmark evaluation. Drag the slider to adjust declassification transparency.

Private Vault 30% Public Rubric
CASE: 0x7c3f9a2e…b41d0e91a ROBINHOOD CHAIN #4663

Patient record: [REDACTED_PATIENT_NAME] admitted with 45 min substernal pain. Initial troponin was hs-cTnI 8 ng/L. Lead aVR exhibits 1.2mm ST elevation with diffuse depressions.

Ground Truth Diagnosis: Left Main Coronary Artery Subtotal Stenosis
Dynamic Emission Simulator

Failure-Rate Weighted Epoch Pools.

Tweak the simulated AI failure rate per profession below to watch the weekly emission pools rebalance dynamically in real time.

SIMULATED WEEKLY $OUTS REWARD POOL PARTITION

From your hardest case to a verified win.

Step 1

Submit a real case

A scenario from your work, the question, the right answer, a short explanation and a grading rubric. Text, images and attachments all work.

Step 2

The AI panel answers

Your case runs against the latest models. Every answer is graded against your rubric by an AI judge, with human spot checks on a sample.

Step 3

Peers check it

When most models fail, two or three experts in your field confirm the answer is right, the case is fair and nothing is ambiguous.

Step 4

You earn and climb

Accepted cases earn points and rewards, and push your profession up the weekly leaderboard.

New caseDraft

Chest pain, normal troponin

Answer and explanation
Grading rubric, 4 criteria
PII check passed
Model panelGraded vs rubric

5 models tested

A1/4
B2/4
C4/4
D1/4
E2/4
Peer reviewConsensus

Valid, fair, unambiguous

Reviewers vote valid, invalid or ambiguous. Ambiguous cases go back to the author.

R1R2R3
AcceptedHash recorded

AI Slayer, Cardiology

Reward paid. Your case joins the private dataset and counts toward your profession's weekly rank.

+ 5,000 $OUTS0x7c3f…e91a

Human Edge Index

Which professions does AI still struggle with most? Every week, professions are ranked by how often their verified cases beat the model panel.

Share what you beat, not what you wrote. Share cards show the result and your field, never the case itself.

AI failure rate by profession

Live arena metrics
1Medicine80%
2Law80%
3Software engineering60%
4Trades and mechanics60%
5Accounting and tax60%

$OUTS. Mined only by experts AI can't replace.

1

Weekly epochs

Each week's emission is split into profession pools.

2

Pools weighted by AI failure

If AI fails 80% of cardiology cases and 40% of accounting cases, the cardiology pool is bigger. The hardest fields to replace earn the most.

3

Split inside the pool

Rewards go to accepted cases, multiplied by models beaten and the expert's verification tier.

4

Edge decay

When a newer model cracks an old case, it drops off the leaderboard. Rewards already earned stay yours.

5

Revenue-backed demand

Dataset buyers pay in stablecoins. Part funds $OUTS buyback and burn, part goes to the experts whose cases were licensed. Sponsors buy $OUTS to fund sprint bounties.

Fixed total supply

500,000,000

Fair launch on Robinhood Chain (ID 4663), no exempt wallets, public team vesting.

50% Expert mining

About 5 years, halving every 12 months

12% Reviewer rewards

Gradual emission for peer review

13% Treasury and ecosystem

Multisig, grants per profession

15% Team

12-month cliff, 36-month linear vesting

7% Liquidity

LP locked for at least 12 months

3% Early experts

The first 1,000 verified experts

For AI labs and vertical AI teams

License expert-level evaluation sets that have never been on the open web. Every case is peer verified, tested against current models and watermarked per buyer.

Dataset licenses by profession, by subscription or batch, with optional exclusivity.

Custom eval sprints fund a competition in exactly the domain you need.