v0.9.0 — UniversalEmbedder: geometry-grounded PCA drop-in

Release Notes — v0.9.0

Released: 2026-05-26

The headline change is UniversalEmbedder — a geometry-grounded, modality-agnostic dimensionality reducer that exposes the same fit / transform / fit_transform surface as sklearn.decomposition.PCA. Any benchmark pipeline that currently calls PCA(n_components=…) can swap in UniversalEmbedder() without touching the surrounding code. Under the hood it discovers d* from local manifold geometry (ManifoldModel) and then auto-selects between a global-PCA projection (for near-linear data) and BFS Procrustes-transported TurtleND frames (for genuinely curved manifolds) based on the Manifold Linearity Index MLI = global_d_at_τ / d*. Explicit modes are available when you want to force one strategy. Ships with 362 lines of unit tests covering all four modes plus the sklearn drop-in contract.

The release also lands a documentation audit triggered by an external claim-verification request: the README's CIFAR-10 "+8.5 pp over ResNet" UB headline has been re-grounded against the raw JSON trial data, the column header now honestly names the dropout variant (ManifoldResNet-UB+Drop), and per-row stats across CIFAR-10 / Fashion-MNIST / MNIST are re-aligned to JSON-computed sample stds. A standalone CIFAR10_CLAIM_VERIFICATION.md report sits at the repo root with the full audit, including a "What 'Matched' Means" architecture table so a reviewer who clones the repo can reproduce.

Smaller items:

• New Manifold Voxel Visualizer section in the README under Algorithms — hero figure, CLI examples for every built-in dataset, per-voxel scalar-field inventory, and links to the CLI+API reference, USAGE examples, and method paper.
• File-tree refresh and a handful of dead *_report.md README links redirected to the existing .pdf reports.
• Minor: pyproject.toml section-header comment retitled CodeKG → PyCodeKG.

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Full changelog: CHANGELOG.md