Ascension: Deterministic Software Evolution Through Autonomous Primitive Collision

We introduce Ascension™, a deterministic software evolution engine that identifies, scores, and hardens latent architectural capabilities in arbitrary source code — without invoking external artificial intelligence. The engine operates by colliding uploaded code against a fixed matrix of 40 computational primitives organized across four taxonomic categories (Organs, Layers, Engines, and Agents), scoring emergent combinations via the Crown Jewel Pipeline Index™ (CJPI), and exporting hardened artifacts as self-contained Sealed Runtimes™. We present empirical results from fifteen verified case studies spanning five programming languages and eight industry verticals, including critical findings in code from IBM, Rapid7, Hugging Face, OpenSSL, ArduPilot, QuantLib, Google, Meta, Anthropic — and a four-part self-referential audit where the substrate analyzed its own internal systems and discovered weak cryptographic randomness, unhandled async rejections, and missing error handling in production code. Our findings demonstrate that deterministic primitive collision reliably surfaces structural deficiencies invisible to conventional static analysis, linting, and AI-assisted code review. We propose Ascension™ as the foundation for a new discipline: post-authorship software evolution, where code improvement occurs structurally rather than generatively.