.. _getting-started-introduction: Introduction ============================== Ngeodesic provides safety guardrails for AI with enhanced performance. Our Micro-LM (micro language model) sidecars act as a **Layer 2 for AI**: improving efficiency and reliability, enforcing determinism, auditability, and compliance — all while letting large language models remain fluent and flexible. For GitHub repos, you can visit: * `NGF Research `_ * `micro-LM `_ Warp->Detect->Denoise (WDD) ------------------------------- Warp → Detect → Denoise (WDD) is a patented pipeline for making noisy latent spaces deterministic and auditable. It first warps embeddings into a stable geometry, then detects true signals with calibrated filters, and finally denoises phantom attractors. The result: reproducible PASS/ABSTAIN decisions with clear traces — turning stochastic LLM (large language model) outputs into predictable, safe, enterprise-grade behavior. .. image:: /_static/ngf_warped_geodesic_contour.gif :alt: NGF Warped vs Flat Paths :align: center :scale: 70% Micro-LMs ------------------------------- Micro-LMs are lightweight, domain-specific companions to LLMs that provide determinism, safety, and auditability. They focus on a narrow set of primitives (e.g., DeFi actions, ARC puzzles), mapping prompts into PASS or ABSTAIN with verifiers and traces. The LLM handles fluency, while the micro-LM enforces guardrails and compliance — the best of both worlds. Micro-LM Sidecar ------------------------------- A **Micro-LM Sidecar** can be thought of as a **Layer 2 for AI**: it doesn’t replace the large language model, but it sits alongside it to improve performance, add safety guardrails, determinism, and auditability. In AI, the LLM is like Layer 1: broad, powerful, but stochastic and sometimes unsafe. A Micro-LM Sidecar is like Layer 2: lightweight, domain-specific, enforcing determinism and safety guardrails, but still “anchored” to the LLM’s fluency and reasoning power. **Request Cycle** .. code-block:: bash User → LLM → /decide {prompt, context, policy} ↓ SBERT → PCA → map → θ → guards ↓ ← {approve|reject|abstain, reason, plan, trace_id} - LLM explains/asks confirm - User confirms - LLM → /execute {trace_id, plan} - Sidecar re-checks guards → tools (if OK) → finalize trace **Workstream Breakdown** .. note:: Status icons legend — ✅ Built • 🟡 Partial • 🔴 Planning +-------------------------+-------------+---------+----------+---------+---------------------------------------------------+ | Workstream | Component | Status | % Effort | % Done | Description | +=========================+=============+=========+==========+=========+===================================================+ | WDD R&D | WDD | ✅ | 30% | 30% | Research, math, validation of Warp → Detect → | | | | | | | Denoise (ngf-alpha, DeFi/ARC, proofs). | +-------------------------+-------------+---------+----------+---------+---------------------------------------------------+ | Core pipeline (R&D) | Micro-LM | ✅ | 15% | 13% | SBERT → PCA prototyping, thresholds, datasets. | +-------------------------+-------------+---------+----------+---------+---------------------------------------------------+ | Core pipeline | Micro-LM | ✅ | 20% | 17% | Engineering impl. of SBERT → PCA/θ, integration | | (Engineering) | | | | | with WDD-lite. | +-------------------------+-------------+---------+----------+---------+---------------------------------------------------+ | Domain guards | Micro-LM | ✅ | 10% | 8% | HF / LTV / oracle freshness verifiers. | +-------------------------+-------------+---------+----------+---------+---------------------------------------------------+ | Plans & policies | Micro-LM | ✅ | 5% | 4% | Primitive specs, per-class thresholds, | | | | | | | policy defaults. | +-------------------------+-------------+---------+----------+---------+---------------------------------------------------+ | Quickstart harness | Micro-LM | ✅ | 5% | 4% | In-process run + JSON outputs. | +-------------------------+-------------+---------+----------+---------+---------------------------------------------------+ | Tests & canaries | Micro-LM | 🟡 | 5% | 3% | Smoke cases, threshold checks. | +-------------------------+-------------+---------+----------+---------+---------------------------------------------------+ | API layer | Sidecar | 🔴 | 4% | 0% | ``/decide``, ``/execute``, schemas. | +-------------------------+-------------+---------+----------+---------+---------------------------------------------------+ | Orchestrator glue | Sidecar | 🔴 | 2% | 0% | Routing, retries, caches. | +-------------------------+-------------+---------+----------+---------+---------------------------------------------------+ | Observability | Sidecar | 🔴 | 2% | 0% | Logs, metrics, ``/healthz`` & ``/version``. | +-------------------------+-------------+---------+----------+---------+---------------------------------------------------+ | Audit trail | Sidecar | 🟡 | 1% | 0.3% | Structured traces, IDs. | +-------------------------+-------------+---------+----------+---------+---------------------------------------------------+ | Ops safety | Sidecar | 🟡 | 1% | 0.2% | Warmup, kill-switch, double-gate at execute. | +-------------------------+-------------+---------+----------+---------+---------------------------------------------------+ **Total % Done ≈ 85% (±5%).** Our first pilots are: - **ARC Micro-LM** → reasoning stress test (Abstraction & Reasoning Corpus). - **DeFi Micro-LM** → finance primitives with verifiers for safety and risk control. If you are a researcher, developer, or just curious about deterministic AI reasoning, this guide will help you get started quickly. .. toctree:: :maxdepth: 1 :hidden: :caption: Getting Started Home quickstart roadmap licencing .. toctree:: :maxdepth: 3 :hidden: :caption: Concepts concepts/micro_lms concepts/ai_reasoning .. toctree:: :maxdepth: 3 :hidden: :caption: Research research/ngf research/benchmarks research/publications_patents .. toctree:: :maxdepth: 3 :hidden: :caption: Engineering engineering/interface_arc engineering/interface_defi engineering/repository_structure