Site: https://calebpruett927.github.io/GENERATIVE-COLLAPSE-DYNAMICS/
Calculator (try it now): https://calebpruett927.github.io/GENERATIVE-COLLAPSE-DYNAMICS/calculator
Repo: https://github.com/calebpruett927/GENERATIVE-COLLAPSE-DYNAMICS
PyPI: pip install umcp
I've been building an open-source validation framework called UMCP (Universal Measurement Contract Protocol) and wanted to share it. The core idea: take any set of measurable channels, run them through a fixed mathematical kernel, and get a three-valued verdict (CONFORMANT / NONCONFORMANT / NON_EVALUABLE — never boolean) about whether the system's integrity holds.
The interactive calculator runs entirely client-side in TypeScript — no server, no dependencies. You can plug in channel values, see the kernel invariants compute in real time, visualize regime phase diagrams, compose systems together, and explore 29 pre-loaded entities from Standard Model particles to astronomical objects.
What makes it interesting from an engineering perspective
Three-layer sandwich architecture:
C99 core (~1,900 lines) — the full validation protocol in portable C: frozen contracts, regime gates, trace management, integrity ledger, and the complete validation spine. No heap allocation in the hot path. Stable extern "C" ABI.
C++17 accelerator — links against the C core, exposes a pybind11 zero-copy NumPy bridge. 50–80× speedup on kernel computation and seam chains.
Python layer — 20 domain closures, CLI, REST API, all the high-level orchestration. The accel.py wrapper auto-detects the C++ extension at import; if it's not built, everything falls back transparently to NumPy. Same results to machine precision either way.
760 C/C++ test assertions verify the cross-language equivalence.
Contract-first validation:
Instead of "test that the output looks right," UMCP freezes a mathematical contract before any data is seen, then validates that every claim passes through a fixed pipeline:
CONTRACT → CANON → CLOSURES → INTEGRITY LEDGER → STANCE (freeze) (tell) (publish) (reconcile) (read) The verdict is derived from gates, never asserted. If the kernel invariants pass their thresholds, the system is Stable. If drift exceeds 0.30, it's in Collapse regime. There's no wiggle room — same data + same contract = same verdict, regardless of who runs it.
The kernel itself is small:
Six outputs from any input vector of channel values in [0,1]:
F (Fidelity) = weighted arithmetic mean — what survives
ω (Drift) = 1 − F — what's lost
IC (Integrity Composite) = weighted geometric mean — multiplicative coherence
κ (Log-integrity) = log of IC — sensitivity measure
S (Entropy) — field uncertainty
C (Curvature) — channel heterogeneity
Three identities hold for every input by construction: F + ω = 1, IC ≤ F, IC = exp(κ). These have been verified to machine precision across all 20 domains.
Scale:
14,182 tests (pytest)
20 scientific domains (particle physics, quantum mechanics, nuclear physics, materials science, finance, evolution, consciousness, semiotics, and more)
181 closure modules
252 proven theorems with subtests
44 structural identities verified to < 10⁻¹⁶
25 reproducible casepacks
SHA-256 integrity checking on 221 tracked files
Some of the more interesting domain results
Standard Model particles: 31 particles mapped through the kernel. Confinement (quarks → hadrons) shows up as a 98% cliff in the integrity composite — one dead channel (color charge going to zero) kills the geometric mean. No fitting parameters.
Periodic table: All 118 elements pass Tier-1 identities (10,162 subtests, 0 failures).
Double-slit experiment: The kernel-optimal state isn't full-wave or full-particle — it's partial measurement where all channels are alive. A >5× IC gap between the optimal state and the extremes.
Tech stack
Python 3.11+ · C99 · C++17 · pybind11 · NumPy · Astro 5 · TypeScript · Tailwind · GitHub Actions CI · PyPI MIT licensed. The web page has the full documentation, interactive tools, orientation proofs, and links straight to the repo.
Happy to answer questions about the architecture, the math, or the domain closures.