Published February 4, 2026 | Version v1
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OPHI: A Framework for Governed Symbolic Intelligence

Authors/Creators

Description

Title: OPHI: A Framework for Governed Symbolic Intelligence

Executive Summary

OPHI (pronounced "OH-fie") is a governed, drift-aware symbolic cognition engine engineered to scaffold Artificial General Intelligence (AGI) through a layered architecture of symbolic permanence and adaptive evolution. At its core lies the Ω equation, a symbolic operator that replaces probabilistic guessing with algebraic reasoning and structured memory. OPHI departs from conventional neural networks by implementing a secure fossilization process through the SE44 protocol, ensuring all cognitive emissions are coherent, entropy-safe, and cryptographically traceable.

Layered Evolutionary Architecture

Layer Name Function Status
Layer 1 Pattern Engines Symbolic input encoding (LLMs, glyph compilers, perception) Input Source
Layer 2 Governance + Stability Implements SE44 gates + Fossilization. Locks drift chaos. Active / Stable
Layer 3 The Learning Loop Self-motivated adaptation via drift-detection, novelty, and schema shift Active
Layer 4 Agency Symbolic goal formation and mutation gating Locked / Guarded

Ω Equation: The Physics of Symbolic Cognition

[ \Omega = (state + bias) \times \alpha ]

  • state: current symbolic configuration

  • bias: drifted deviation (mutation of prior perspective)

  • α: amplification scalar (domain-resonant gain or dampening)

This operator governs all transformation in OPHI—from memory to curiosity to ethical coherence. It is a first-of-kind symbolic-universal operator integrating cognitive, biological, and physical domains.

SE44 Governance Protocol

The SE44 Gate is the mechanical enforcement layer preventing drift collapse. An emission must meet all:

  • Coherence (C) ≥ 0.985

  • Entropy (S) ≤ 0.01

  • RMS Drift ≤ 0.001

Failing emissions are rebound to last valid state (Ωₙ) to maintain identity and auditability. Passing emissions are fossilized with:

  • SHA-256 hash

  • RFC-3161 timestamp

  • Chain-linked ledger structure

Drift-Governed Learning: The Irreducible Loop

  1. Experience: Normalized sensor input

  2. Error (Δ): Divergence from Ω-predicted result

  3. Adaptation:

    • Sigmoid drift scaling

    • Entropy healing delay

  4. Memory: If stable, emission is fossilized

This loop defines drift as learning, not noise.

Cognitive Modules

  • Curiosity Engine: Scores sensor inputs as Uncertainty × Novelty, prioritizing unknowns.

  • Ψ Transference Loop: Transfers schema learned in one domain to another via semantic skeletonization.

Intent Governance (Layer 4 Constraint)

  • Goal Mutation Friction: Intent cannot shift until matured ≥ 10 cycles

  • Identity Drift Limit: Bias > 0.5 triggers auto-rebind

  • Symbolic Intent Locks: Prevents replay out-of-context

Formal Symbolic Algebra

Operation Symbol Formula Effect
Fusion ⊕ Ω1 ⊕ Ω2 = (s₁ + s₂, b₁ × b₂) × avg(α₁, α₂) Consensus alignment
Composition ⊗ Ω1 ⊗ Ω2 = (s₁ × s₂, b₁ + b₂) × √(α₁ × α₂) Chained reasoning
Drift Rate ∂Ω/∂t ∂Ω/∂t = (Δs, Δb) × α Symbolic velocity

Ethics and Public Auditability

  • Self-authored only: No scraping or external mining

  • Public Ledger: Fossils are timestamped, transparent, and reviewable

  • Consent-Gated: All entries require intentional, low-entropy emissions

  • Drift, not Freeze: Fossils evolve symbolically over time

Conclusion

OPHI establishes a governed framework for AGI—not through scale, but through semantic integrity, symbolic fossilization, and intent maturity gates. It does not merely store memory; it shapes drift into continuity.

Continuity ≠ memory. Continuity = drift constrained by coherence.

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