HyperC sits between a research lab and a product company: working papers, a proposed data contract for the category, a defined falsification protocol — and a commercial product measured in customer-realized profit. This page keeps the evidence inspectable, with synthetic and live results clearly separated.
From the executed slower-market-waves notebook in the Computable Markets working paper (August 2026). The point is not that P34 wins — it's why conventional models lose.
A tuned XGBoost classifier scored 0.9266 ROC-AUC on the business-observed holdout and showed +$61.7k in naive observed-data evaluation — then lost $417.4k applied to the full 20-week future opportunity menu it had never been forced to refuse.
P34 selected 115 trades, predicted +$2,404, realized +$2,250. The calibrated XGBoost baseline selected 177 trades and realized −$8,789 on $18,821 deployed. Live system invocation on a synthetic market — not a live-market trial.
Partial observability, selection bias, regime change, optimistic backtest traps. In the easy stationary/growing benchmark P34 preserved upside: +$228.6k vs the baseline's +$227.1k.
Disciplined refusal is the central mechanism: no-trade is a rewarded output, and false-positive control is explicit in the model class.
Company-reported figures. Note: not audited by a human licensed auditor. Detailed per-market performance packs (evaluated counts, rejection rates, predicted-vs-realized economics, win/loss distributions) are published per supported market as part of the evidence engine.
The operator-relative theory of computable markets, the Menu-Sales-Description (MSD) data contract, the Computable-Market Thesis, application templates for 10+ verticals, and a falsification protocol. Includes the revised notebook results above.
PDF Download the working paperThe P34/PARML technical report: benchmark construction (partial observability, selection bias, regime change), baseline tuning, results and limitations — with notebooks.
GIT hyperc-ai/p34-technical-report — notebooks & resultsProvisional patent filed on the P34 architecture: synthetically-trained universe selection, Pareto-front alignment, and the confidence interface. Multiple defensive publications published.
How to try to break P34: holdout menu construction, baseline calibration rules, refusal accounting, and what a falsifying result would look like. Ask your own AI to audit it.
PDF Evidence pack — drop file to publishP34 is for computable markets; it does not yet automatically prove that a market is computable.
If the deployment-time menu is far from training context, an explicit refusal path is not yet exposed.
The business must have had more options historically than it chose — otherwise there is insufficient policy-selection signal.
Latency ranges from under a minute to hours; very high-frequency applications are outside current scope.
Generalization across all markets is not fully known; pilots proceed through shadow testing and controlled deployment.
Unknown unknowns remain — P34 is an early release in a new model class.