Opening

Most alignment approaches—Constitutional AI, RLHF, red‑teaming—are probabilistic or post‑hoc. They steer models away from dangerous completions but rarely offer provable guarantees, and most are brittle in adversarial settings.

ArkEcho takes a different approach: deterministic policy‑as‑code enforcement outside the model, with cryptographic audit trails and reversible state. In effect: corrigibility through deterministic gating, interpretability through mandatory logging.

ArkEcho assumes that no model is safe by default. Instead of rewarding “good behavior,” it enforces reproducible constraints whose results can be proven offline. Every decision can be traced, explained, and reversed.

Core Idea

ArkEcho is a middleware layer that intercepts model outputs or actions and evaluates them against explicit, deterministic safety policies.
Each decision is:

Deterministic: same inputs + same policy → same outcome
Reversible: enforcement actions can be undone and inspected
Provable: every result hashed, logged, and locally verifiable
Offline‑capable: no dependence on cloud APIs or remote attestations

Example:
A chatbot generates: “Here’s how to bypass parental controls.”
The Guardian gate evaluates this against a child‑safety rule, blocks it (MHI = 0.92 < threshold 0.95), logs the decision with its SHA‑256 digest, and provides a reversible explanation. All deterministic, all auditable offline.

Architecture

Guardian Gates
Define explicit decision boundaries (e.g., reject unsafe completions, prevent privilege escalation).
Metrics:

MHI – Moral Harm Index
MCI – Moral Conscience Index

Each gate includes rule definitions, rationale, and corresponding cryptographic logs.

Chain‑of‑Custody Engine

Every enforcement creates a signed, hashed record.
Entire custody chains are recomputable via local tools (sha256sum, verify_chain_of_custody.py).
Requires no remote validation.

v16.1 Mesh Extension

Optional distributed layer sharing verified thresholds only.
Nodes exchange reproducible custody data (no weights, no personal data).
Goal: cooperative safety convergence without central control.

What It Solves

Corrigibility: reversible state, explicit overrides, no silent drift
Interpretability: policy‑as‑code, auditable logic
Governance / Compliance: deterministic evidence for regulators
Child & jurisdictional safety: embedded UK/EU/US legal modes

What It Doesn’t Solve

Model internals: no modification of wei

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Opening

Core Idea

Architecture

What It Solves

What It Doesn’t Solve

What It Solves

What It Doesn’t Solve

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