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Publications

NeuPortal Quant Lab

Recherches sur les agents IA, backtests et audits de prévisions de marché, et architecture de la plateforme.

Toutes les publications · 132

  1. Autre

    Can AI Destroy Humanity? What the Evidence Actually Shows in 2026

    Estimates of AI causing human extinction run from 0.38% to 16% depending on who you ask, and 63% of Americans think it could happen. Here are the three mechanisms, what the 2026 International AI Safety Report changed, and why the forecasters on both sides were no better than trend extrapolation on the questions that could actually be scored.

    9 min de lecture9 sections

  2. Pièges des modèles

    How Often Do AI Agents Actually Finish the Job? 66% Once, 47% Every Time

    Thinkingbox runs 507 stateful business workflows past AI agents 20 times each and reports pass^20, not just pass@1. The strongest model solves 66.50% on one attempt and 47.53% on all twenty; another posts 89.35% pass@20 and 7.50% pass^20. What that gap means for agents running unattended, why the attempts are not coin flips, and how to measure your own.

    11 min de lecture9 sections

  3. IA et entreprise

    ChatGPT for Financial Services: What a 69.9% Score Means at a Real Desk

    OpenAI put GPT-6 Astra in the junior banker seat on 10 September 2026 and published a score: 69.9% on a financial document benchmark. Here is what that rate does to a deck, what citations do and do not prove, and the checking step to run before it touches client work.

    8 min de lecture9 sections

  4. IA et entreprise

    TikTok From Zero to $100 a Day: The Complete Operating Manual

    Creator Rewards pays $0.40 to $1.00 per thousand qualified views. Here is the whole system built backwards from that number - account setup, how to find what is working, how to build your own version of it, and what to do every single day.

    11 min de lecture12 sections

  5. Pièges des modèles

    Your 30-Day Backtest Has 3,275 Windows and 110 Observations

    Slide a window across price history and you get thousands of samples. Almost none of them are independent. Here is the arithmetic, measured on real Binance history, and what it means for every confidence interval you have ever published.

    7 min de lecture7 sections

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