# CCT Selected Public Replication Release V1.1

This release presents a compact executable view of the Continuum Computation
Thesis (CCT): a finite observer/controller search frame for finding physical
regimes in which measurement, timing, coordination, architecture, and control
organization change what a fixed system can resolve or execute.

It combines four kinds of traction:

1. **Formal constraints** that make easy expressivity, measurement scope,
   command attribution, resource fronts, and passive-boundary response
   mathematically inspectable.
2. **Prospective simulation chains** in which the question, comparator, or
   opportunity score was frozen before the scored evaluation.
3. **Physical exposure cards** that carry the surviving questions toward
   measurement, field/basin, and retained-state benches without publishing
   implementation-specific recipes.
4. **Prospective experiment selection** in which a structural representation
   changes which policy is probed next under matched observations, action
   access, acquisition logic, and probe budgets.

This is the selected public release. The working research repository contains
additional theorem history, branch tests, route fixtures, reviews, and internal
execution material.

## What To Inspect First

| Question | Start here |
|---|---|
| What has been earned, and by which artifact? | `CLAIMS_EVIDENCE_MAP.md` |
| What exactly is included? | `RELEASE_MANIFEST.md` and `SOURCE_SET.json` |
| How do I verify the release? | `RUNBOOK.md` and `verify_release.py` |
| Which formal results define the proof spine? | `open-theorem-working-notes/` |
| Which prospective simulation chains show traction? | `gu/docs/current/cct-marquee-simulations/README.md` |
| Which physical questions follow? | `exposure-cards/` |

## Selected Results

### Structured physical information can improve a process frontier

In the fixed-wave confirmatory model, structured space-time excitation reduced
coherent crosstalk by **63.6%** across eight shared functions at equal incident
energy. Phase scrambling, time shuffling, and wrong-model inversion removed the
successful execution.

### Coordination order can be an operational design variable

A frozen coordination-order challenge predicted **5/5** useful-handle
decisions, minimum coordination orders, destructive ablations, and relabeling
cases. The selected temporal order then transferred to a FitzHugh-Nagumo model,
beating its exact reverse by **0.0835** and a marginal-matched destructured
program by **0.0524**, with the ordering surviving **9/9** nuisance cases.

### A common opportunity quantity can map where structured control helps

Across fresh FitzHugh-Nagumo and Landau-Lifshitz-Gilbert model banks, a frozen
task-aligned intervention contrast classified all **18/18 decisive held-out
cases** correctly. The confirmation recorded **11 positive**, **6 null or
uncertain**, and **7 negative** cases, with AUC **1.0**, Pearson correlation
**0.9975**, and Spearman correlation **0.9974**.

### Structural information can reduce experimental search burden

On a matched 32-policy, 12-case active-search pilot, the CCT representation
reduced mean regret-curve area by **0.01656** relative to a tuned raw-schedule
Gaussian-process search. Its paired 95% interval was **-0.03429 to -0.00175**.
At four probes, the CCT workflow had found the held-out best policy in **7/12**
cases versus **3/12**, showing that the representation changed prospective
experiment selection rather than merely describing a completed policy map.

### Architecture can expose otherwise missing physical handles

The retained-geometry exhibit produced **115/128** exact target geometries and
failed under zone permutation and uniform-dose replacement. The wave/structure
co-design exhibit achieved **29/32** observed S-bend successes (**27/32** by the
frozen threshold), reduced median error by **81.2%** against optimized two-end
loading, and lost all held-out successes when the third independent channel was
removed.

## Quick Verification

Use Python 3.11 or newer:

```bash
python -m pip install -r requirements.txt
python verify_release.py
```

The verifier checks the allowlist, SHA-256 inventory, JSON and Python syntax,
then runs the selected formal and simulation verifiers against the packaged
artifacts.

## Reading The Evidence

The formal notes own analytical scope. Their executable companions exercise
declared assumptions, counterexamples, and route behavior.

The simulation reports own model definitions, frozen comparisons, numerical
results, and interpretation. The four core chains are prospective
model-evidence for regime mapping and search; the two architecture exhibits
show narrower physical-control possibilities.

`SOURCE_SET.json` records the upstream hash of every selected source. Four
fixed-wave files also carry a declared text-only lineage transformation that
keeps predecessor-campaign commentary separate from the current evidence map;
the transformation changes no executable logic or numerical artifact.

The exposure cards own the next public-safe physical questions. Build-specific
materials, geometries, operating windows, calibration values, and search maps
remain in the controlled execution layer described by
`PUBLIC_RELEASE_BOUNDARY.md`.
