Repository Structure

Overview

The NGF ecosystem is organized into three main repositories, each with a distinct role:

  1. ngf-alpha → research prototypes and staged experiments.

  2. ngeodesic → the production-ready Python package implementing NGF rails.

  3. micro-lm → domain-specific micro‑LM applications built on top of ngeodesic.

This layered structure ensures a clean separation between theory, framework, and product.

ngf-alpha (Research)

  • Repository: https://github.com/ngeodesic-ai/ngf-alpha

  • Purpose: experimental log of the Noetic Geodesic Framework (NGF).

  • Content: staged experiments from toy latent spaces to real embeddings.

  • Key contribution: crystallized the Warp → Detect → Denoise (WDD) doctrine.

  • Outcome: validated NGF as a viable method for deterministic reasoning.

ngeodesic (Science)

  • Repository: https://github.com/ngeodesic-ai/ngeodesic

  • Purpose: implements NGF rails in a reusable, domain‑agnostic package.

  • Key features: - Warp: PCA projection, funnel profiles, semantic well shaping. - Detect: matched filters, null/foil separation, margin thresholds. - Denoise: EMA+median smoothing, phantom guards, jitter averaging. - Parser API: stock parsers + Stage‑11 geodesic parser.

  • Provides the rails and utilities for downstream micro‑LMs.

micro-lm (Product Line)

  • Repository: https://github.com/ngeodesic-ai/micro-lm

  • Purpose: applies NGF rails to build deterministic, domain‑specific sidecars.

  • Domains: - DeFi Micro‑LM: maps finance prompts → primitives with LTV/HF/oracle verifiers. - ARC Micro‑LM: stress‑tests reasoning on grid transformations.

  • Architecture (Tier‑1) 【232†OVERVIEW.md】: - Adapters: normalize context (JSON → schema). - Mapper: SBERT/wordmap classifiers. - Verifier: domain policy checks. - Planner: expands intent into structured plans. - Harness: runs deterministically on Stage‑11 NGF rails.

  • Tier‑2 refactor 【231†TIER2.md】: introduces WDD auditors for latent separation.

Cross-Repo Flow

  1. ngf-alpha proves the theory (WDD rails, abstain logic).

  2. ngeodesic packages it as a stable Python library.

  3. micro-lm consumes ngeodesic to deliver applied products (ARC, DeFi).

This flow mirrors a traditional R&D pipeline: Research → Engineering → Product.