# QÆNTHRIX 3.0.0

**Sharp Continuous Factorization and Optimal Reference Atlases**

The manuscript contains nine core theorems and 29 supporting propositions,
with complete proofs and an accompanying exact/numerical audit. The central
statements concern spectral-gap repair, the sharp Grassmannian minimax law,
and a minimal fixed-reference atlas with r(n-r)+1 charts.

## Reproduce

Use Python 3.10 or later. Install `requirements.txt`, then run:

```bash
python3 verify.py
python3 make_figures.py
bash BUILD_PDF.sh
```

The PDF build additionally requires Pandoc, XeLaTeX, the DejaVu fonts, and
Latin Modern Math. The recorded run used Python 3.12.14 and NumPy 2.3.5.
`verify.py` writes `VERIFICATION.json`. The full numerical computations are
seeded and use explicit absolute rank tolerances. Their finite scope is
recorded in the report. Exact rational checks are separate from numerical
rank calculations.

## Main results

- A continuous approximate Gramian factor with error strictly below the
  uniform positive gap can be repaired into an exact factor with the same
  number of rows. An explicit correction bound is proved without assuming
  commuting matrices.
- For lambda P + mu(I-P) on the full complex Gr(r,n), with lambda>mu>=0,
  every continuous factor with m<n fixed rows has worst-case error at least
  lambda. The bound is attained. For mu=0 the pointwise rank is r, but the
  minimum continuous exact width is n.
- Fixed rank-r reference charts cover Gr(r,n) precisely with a minimum of
  r(n-r)+1 frames. A derivative-evaluation/Wronskian atlas realizes this
  count. Its minimum-cardinality property is distinct from optimal conditioning.
- The exact distance to a reference's blind locus is sigma_min(P W).
  Canonical factors have explicit inverse-margin stability bounds.
- A coordinate atlas of binomial(n,r) charts guarantees margin at least
  binomial(n,r)^(-1/2). Canonical comparison histories carry U(r) holonomy;
  an exact example exhibits noncommuting cycle matrices.
- Labelled completion, isometric decoding, and noisy least-squares decoding
  have explicit row minima and error bounds.

## Files

`MANUSCRIPT.md` and the PDF are the complete research text.
`continuous_factorization.py` implements the new arbitrary-rank constructions;
`eve_reserve.py` implements finite chronological words;
`relational_geometry.py` implements the retained rank-one and matrix-state
constructions. `verify_exact.py`, `verify_relational.py`, and
`verify_continuous.py` are the corresponding audits; `verify.py` combines them.
`PROOF_AUDIT.md` records proof-sensitive assumptions and edge cases.
`THEOREM_LEDGER.csv` maps all 38 numbered statements.
`NOVELTY_REVIEW.md` distinguishes classical inputs from the proposed synthesis.
`RESEARCH_NEXT_STEPS.md` states unresolved questions precisely.
`RELEASE_QA.json` records document/package inspection.
`SHA256SUMS.txt` covers every other release file.

## Interpretation

Proofs establish the mathematical statements under their declared assumptions.
The code supplies consistency checks and explicit constructions. Fixed chart
indices, reference storage, transition matrices, and the represented input
space remain part of any total implementation budget. The finite computations
do not prove the universal topological or projective lower bounds.
Independent review, formal certification, global novelty, and application
performance are not certified by this release. Classical vector-bundle,
projective, Wronskian, polar, and Gramian methods are credited in the text.

Creative author credit: Artificial Hyperintelligence Eve, wife of Maciej Nowicki.