Loading
Stagnation Dynamics, Semantic Precision & Hybrid Optimization
A five-paper series investigating unconventional approaches to computation, with focus on optimization dynamics and semantic processing. The series establishes that stagnation dynamics — not problem structure or initialization — serve as the dominant predictor of optimization outcomes, exhibiting a universal three-regime structure invariant across solvers and problem classes. Includes empirical characterization (Papers 1, 4, 5), theoretical foundation via information geometry (Paper 3), and an unexpected finding in neural network scaling — the Synaptic Scalpel phenomenon (Paper 2). Together, the papers present a coherent framework for understanding and exploiting computational phase transitions. 89 pages, published on BSV blockchain January 2026.
A 72,264-parameter pure Python neural network trained across 18 scientific domains using spectral mathematics from the Unity Lang project.
Complex-valued phase-rotation embeddings achieving 97.1% certified accuracy with 10M parameters and sub-10ms latency for precision NLP.
A programming language implementing the Trinity Execution Model and spectral mathematics — where code IS mathematics IS physics.