Every result below was produced by the same unmodified codebase in a single first-pass execution. No domain-specific engineering. No human feedback loop between configuration and output.
Projected 60 MLB hitters for the 2025 season using only pre-season data. Projections were locked before Opening Day — no 2025 data was used in training. Each player received an independent pipeline run with ~3,090 observations across 72 metrics.
HR/PA RMSE of 0.0201 — competitive with projection systems (ZiPS, Steamer, Marcels) that have been iteratively refined over 20+ years. 55% of players graded Elite or Strong. 339 equations tested per player on average. Aaron Judge: 54 HR projected vs. 53 actual.
Ran Elijah on diabetes hospital readmission data covering 376 observations across 126 clinical metrics. Zero domain expertise provided. Zero guidance on which metrics matter.
603 equations tested, 603 passed. 0% failure rate. 99.48% backtest accuracy. 991 feedback loops discovered. The system independently identified high glucose prevalence adjusted by demographic factors as the dominant readmission driver — a finding that aligns with published clinical literature.
760 observations, 23 metrics. 103/103 equations passed. 99.72% accuracy. R² = 1.0 on wealth capacity and migration pull proxies.
584 observations, 31 metrics. 132/132 equations passed. 99.72% accuracy. R² = 1.0 on feeder household income and luxury buyer pressure.
4,867 observations, 31 MSAs, 84 metrics with equations. 408/408 passed. 35,974 feedback loops. 68,337 evolution multipliers.
Daily POS data. 98 metrics. 99.39% accuracy. 93 feedback loops. 3-day early warning signals for store-wide revenue shifts.
Two independent runs: ATI corridor reliability (14 entities) and PEMS congestion (25 entities). 0% failure rate on both.
Industry-standard Volve field benchmark. 60 feedback loops, 343 evolution multipliers. Interpretable equations no other benchmark provides.
Global water stress. 10 countries, 1,800 observations. Discovered conflict events and precipitation as the strongest signal — direct policy relevance.
Mathematical relationships independently discovered across separate entities and confirmed to hold structurally. These are not statistical averages — they are structurally identical symbolic equations discovered independently and then validated across the population.
When universal equations are fed back into individual analyses as seed equations, they dramatically accelerate convergence and improve projection quality — particularly for entities with limited historical data. Every new domain strengthens these laws for everyone.
The strongest statement we can make is the simplest: bring us data from any domain, and we will show you the equations governing it.
michael.lazzarotti@diginetics.co