We study the structure of intelligent systems.
Iterative systems with the right structure exhibit properties that challenge fundamental assumptions about computation. We find these structures, measure their properties, and publish the observations. The mechanisms remain proprietary.
Inverse Cost Scaling in Iterative Convergence Systems
We demonstrate that iterative solvers with dimensional compression exhibit a counterintuitive property: computational cost decreases with iteration depth. Across three domains (radiosity rendering, financial equilibrium, differential equations), we observe 32x compute reduction on deep iterations through rank-adaptive compression and polar quantization. The hardest problems — those requiring the deepest convergence — become the cheapest to solve.
Structural Pattern Recognition in Financial Order Flow
We demonstrate that iterative refinement of byte-level financial data produces sigma profiles that correlate with structural novelty in order flow. Backtested across 14 months of multi-exchange data, the approach identifies regime transitions 2-4 hours before traditional volatility indicators.
Automated Compliance Verification in Home-Based Care Settings
Electronic visit verification and camera-based presence detection achieve 99.2% compliance accuracy across 1,400+ weekly visits.
Structural Anomaly Detection in High-Dimensional Research Data
We present a retrieval system that identifies structural patterns in research datasets that standard statistical pipelines classify as noise. Evaluated on three domains (genomics, materials science, and clinical trials), the system surfaces anomalies that correlate with subsequently validated findings at a rate 4.7x higher than baseline methods.
Thermodynamic Anomaly Detection in Network Byte Streams
Network traffic analyzed at the byte level exhibits structural signatures that distinguish zero-day exploits from benign anomalies. We show that iterative refinement of raw packet data produces stability profiles where security-relevant events cluster in geometrically distinct regions, achieving a 94% true positive rate with a 0.3% false positive rate on the CICIDS-2017 benchmark.
orthogonaLabs publishes empirical observations only. Proprietary mechanisms, architectures, and theoretical foundations are not disclosed in public research.