From Raw Point Clouds to Causal $H^2/H^3$ DAGs

Combining Blum and Sakana.ai $\text{CTM}^2$ agent falsification with slow-cycle $H^2/H^3$ discrete conformal mapping. Transforming raw, unstructured point clouds into structured Directed Acyclic Graphs (DAGs) without metric distortion.

INTERACTIVE MANIFOLD VISUALIZER

Raw Point-Cloud Clutter $\rightarrow$ Conformal $H^2/H^3$ Directed Graph

Fisher Information Metric: $g_{ij}(\theta)$

DUAL-CYCLE ALGORITHMIC ENGINE SPECIFICATION

Fast-Cycle MoE Falsification & Slow-Cycle Fisher Metric Discovery

FAST-CYCLE Blum + Sakana.ai $\text{CTM}^2$ (Signal $\rightarrow$ Semantic)

Integrates Nathaniel Thurston’s proprietary Blum Conscious Turing Machine ($\text{CTM}$) with open-sourced Sakana.ai agent frameworks. Operates as a Mixture of Experts (MoE) multi-agent peer review loop, running real-time hypotheses and falsification tests ($c \lt 10\text{ms}$) to convert raw multi-spectral signals into verified semantic primitives.

SLOW-CYCLE G-GAM Core (Semantics $\rightarrow$ Geometry)

G-GAM discovers the intrinsic Fisher Information Metric tensor $g_{ij}(\theta)$, topological shape, and manifold structure directly from data into continuous non-Euclidean $H^2/H^3$ hyperbolic geometries with zero angle compression ($\Delta_{\text{CD}} \lt 1.8\%$).