CANTGBoost & CANTGit

Abstract

CANTGBoost v19.0 — Wave-Geometric AI Architecture Not a conventional neural network. Not a pretrained model. CANTGBoost is a CPU-first, wave-geometric applied-research architecture that rethinks learning as signal transport across curved mathematical spaces. Instead of relying only on weight fitting and classical backpropagation, it combines: Riemannian and differential geometry on spherical manifolds; wave and phase dynamics, FFT-based binding, and Kuramoto coherence; agent-based transport of gradients, residuals, and other learning signals; conformal uncertainty, EVT/OOD detection, and ALLOW / QUARANTINE / ABSTAIN decisions; provenance, rollback, co-discovery, and Git-like versioning of knowledge. ......... What it can do CANTGBoost can: learn from structured data from scratch; consume signals and representations produced by neural networks, trees, embeddings, and external models; operate as a calibration, safety, memory, provenance, and recovery layer around existing pipelines; run locally on CPUs without requiring a GPU cluster; missing or corrupted attributes are not imputed, but geometrically processed as natural projections of the agent's phase state onto lower-dimensional manifolds ($\mathbb{S}^{n-k}$). ......... The learning process is treated as geometric and wave-like transport rather than only as parameter fitting. Classical backpropagation can be viewed as a special case in which the signal is clean and the delivery path is effectively straight. Mathematical core The architecture explores: nested spherical parameterizations; Riemannian metrics and geometric drift; local Fourier and phase analysis; Lie-group and rotor representations; Krylov–Sobolev flows; joint diagonalization and commutator-based consistency; complex embeddings, RotatE-style phases, and FFT HRR bind/unbind; Lyapunov-style stabilization and conformal risk control. ......... Multimodality The architecture is designed for multimodal and variable-dimensional spaces rather than one fixed output format. Actual support depends on the available modality adapter. The current real benchmark covers tabular and text data (initial). Image, video, audio, code, 3D, medical, financial, and generative adapters remain explicitly deferred. This means the architecture is intended to extend beyond fixed image, video, or tensor dimensions, but unlimited output size or production-grade support for every modality is not yet claimed. Practical profile CPU-first Can learn from scratch or consume representations from existing models Can surround neural networks, trees, and other learners rather than replace them in every use case Supports uncertainty-aware decisions Preserves provenance and rollback paths Produces reproducible benchmark artifacts Designed for local, inspectable execution Current evidence Real five-witness experiments on public datasets Controlled synthetic validation of specific mechanisms 309/309 tests passed from source 309/309 tests passed from the installed wheel Up to 68.5% measured peak-RSS reduction in preserved validation protocols These results demonstrate working implementations and reproducible behavior. They are not an external SOTA claim. CANTGit CANTGit is the companion prototype for Git-like, content-addressed history of .antcolony knowledge maps. It supports immutable commits, branches, tags, three-way merge, quarantine, bundles, integrity checks, rollback, and provenance-aware history. Semantic merge creates an auditable proposal—not an automatic declaration of truth. Research boundary CANTGBoost is an applied-research architecture and prior-art package. It is not: a pretrained foundation model; a proven universal semantic truth engine; a production-grade implementation for every modality; an externally validated SOTA system. Research record Prior-art preprint: https://doi.org/10.5281/zenodo.21699806 SVE Meta-License: https://doi.org/10.5281/zenodo.19373736 HuggingFace 🤗: https://huggingface.co/skovnats/CANTGBoost S.V.E. Meta-License v5.0 «Free for Academia, Humanity & NOGO — Proprietary Constrained to Transparency-Audit & Symmetry Conditions»