Roadmap

Overview

The NGF + Micro‑LM ecosystem has completed Tier‑1 (deterministic SBERT mappers) and Tier‑2 (SBERT + WDD auditors). The next phase extends into richer latents, new domains, and open theoretical questions.

Tier-0: Baseline Deterministic Rails (✔ Secured)

  • Stock matched filter + parser pipeline.

  • Supports core DeFi primitives with deterministic abstain paths.

  • Sandbox verified and benchmarked with stable execution.

Status: ✅ Complete — foundation secured.

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Tier-1: Micro-LM on SBERT Latents (✔ Secured)

  • Replace hashmap lookups with a trained micro-LM encoder.

  • Train against 2–5k SBERT latent prompts.

  • Audit results to return ABSTAIN / PASS with auditable trace.

  • Benchmark with full Stage-11 runner on DeFi suites (1% hallucination / 0.98 F1 Score across 8 primitives).

Status: ✅ Complete — MVP secured.

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Tier-2: Incorporate WDD with SBERT Latents (✔ Secured)

The current release implements Warp → Detect → Denoise (WDD) on SBERT embeddings.

Core Features - Deterministic mapper + verifier with abstain-first behavior. - Handles both DeFi prompts (financial primitives) and ARC prompts (cognitive/aptitude tasks). - Auditable traces: every PASS/ABSTAIN decision includes reasons + confidence. - Stress-tested on SBERT latents: validated signal separation + denoising.

Status: ✅ Complete — WDD secured. Purpose: Community Edition, deterministic & auditable safety (but scoped), SBERT + WDD — Apache 2.0.

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Tier-3: LLM Latents + WDD (🔮 Future / Enterprise)

The end-goal is to extend WDD beyond SBERT into large language model hidden states.

Planned Features - Swap SBERT latents for LLM internal latents. - Apply WDD rails to noisy LLM embeddings → restore determinism. - Package as a sidecar system: LLM provides fluency, micro-LM provides deterministic safety. - Designed for enterprise use: auditability, compliance, SLAs.

Status: 🔮 Planning stage — not required for MVP, proprietary development path. Purpose: Enterprise Edition: gold standard, LLM Latents + WDD — proprietary.

Future Domains

Micro‑LMs can extend beyond ARC and DeFi into other high‑stakes or structured domains:

  • Healthcare: clinical notes → orders with dosage verifiers.

  • Manufacturing/Robotics: operator prompts → deterministic motion plans with collision/torque checks.

  • Supply Chain: planning instructions → workflows with capacity/customs checks.

  • Energy/Grid: operator dispatch → safe load balancing actions.

  • Legal/Contracts: clause parsing → compliance checks, abstains on ambiguity.

These domains all benefit from NGF’s determinism, abstains, and auditability.

Open Research Questions

  1. Latent Generalization: how best to warp/detect/denoise noisy LLM latents?

  2. Audit/Mapper Dynamics: can we formalize auditor vs mapper as a general pattern for sidecar AI systems?

  3. Abstain Philosophy: what are the limits of abstain‑first reasoning, and how can abstains be integrated into human‑AI workflows?

  4. Scalability: how do NGF rails scale as domain complexity grows (more primitives, larger context windows)?

  5. Cross‑Domain Bridges: can one Micro‑LM share priors or rails with another, or must each be trained/audited independently?

Summary

The roadmap is ambitious but clear: extend NGF rails to LLM latents, expand Micro‑LM applications to critical domains, and refine abstain‑centric reasoning into a universal safety doctrine for AI.