GeoUnify_Paper_PIPU_2026

Photonic computing platforms—including Photonic Intelligence Processing Units (PIPUs)—offer unprecedented speed for matrix multiplication and AI inference. However, speed alonedoes not guarantee predictive accuracy: when the input signal is dominated by market-makercompensation (i.e., manipulation), any model—photonic or digital—will process noise. Wehave designed a deterministic geometric model that simulates and predicts the behaviorof a photonic intelligence platform under real market conditions. This model incorporatesthe Market Equilibrium Compensation Factor (MECF), an original contribution ofthe first author, which detects when the market is being actively compensated and abstainsfrom predicting. We validate the MECF on a 10-year walk-forward backtest across sixasset classes. Results show a statistically significant improvement in BTC (57.79% accuracy,+6.32%, p=0.0135) and a 4.4× increase in throughput. We argue that the MECF providesa preventive and profitable investment methodology: it does not predict every day, but onlywhen the geometric structure of the market is stable