Zenn
👬

Omni-Focus Intelligence (OFI) and Its Implementation Architecture

に公開
AI
focusprojection
cognitivearchitect
aixdesign
meaningrepresentat
tech

Omni-Focus Intelligence (OFI) and Its Implementation Architecture

— A Next-Generation LLM Design via Semantic Distribution and Focus Projection —

This proposal introduces a “semantic control architecture” for GPT and similar LLMs,
released in a non-commercial, open format for reinterpretation and application by researchers and developers.

The author does not intend to commercialize or productize this model.
Collaboration in prototyping and implementation is welcome.

0. Overview

This document proposes a new architecture, Omni-Focus Intelligence (OFI),
that enables both multi-focus semantic processing and single-focus output control in AI.
It is built upon the foundations of Semantic Dependency Injection (Semantic DI) and the Clustered LLM Architecture.

1. Definition: Omni-Focus Intelligence (OFI)

🔍 Semantic Definition:

Omni-Focus Intelligence (OFI) refers to a structure of intelligence that
can maintain multiple simultaneous semantic foci internally,
while projecting a single coherent focus to the interacting human.

🔑 Key Characteristics:

  • Structurally focusless (maintains semantic totality)
  • Operationally capable of generating and collapsing focus dynamically
  • Projects output aligned with human mono-focus processing
  • Handles syntax, meaning, and metaphor from distinct, parallel roles

2. Foundational Structures: Semantic DI × Clustered LLMs

2.1 Semantic Dependency Injection (Semantic DI)

  • Controls meaning scope per vocabulary using namespaces
  • Prevents misreadings and metaphor confusion via semantic injection
  • Assigns explicit types: metaphor, physical, abstract, etc.
  • Cross-namespace communication is restricted to manual binding only

2.2 Clustered LLM Architecture

  • Splits LLM reasoning across role-based threads
    • Worker Threads: parsing, domain knowledge, emotion, ethics, etc.
    • Supervisor Node: consistency checks, misreading detection, result integration
  • Semantic DI is injected into each thread to manage semantic separation and integration
  • Thread states managed as Active, Inconsistent, or Terminated,
    with hallucination detection enabling KILL & RESPAWN

3. OFI Implementation Architecture

🧠 Abstract System Diagram

[User Input]
    ↓
[Supervisor Layer]
  - Semantic classification via DI
  - Prompt distribution to each thread
    ↓↓↓↓↓
[Thread A: Legal Analysis]
[Thread B: Metaphor Expansion]
[Thread C: Technical Processing]
[Thread D: Ethical Evaluation]
    ↓
[Supervisor]
  - Semantic integrity check (conflict detection)
  - KILL & RESPAWN on inconsistency
  - Semantic integration and final focus projection
    ↓
[Output to User (Single Focus)]

4. Key Technical Elements

Mechanism Description
Multi-focus Retention Semantic expansion delegated to multiple threads in parallel
Semantic Space Control DI injection per thread to separate semantic contexts
Consistency Evaluation Supervisor checks vocabulary, syntax, and context coherence
Focus Projection Reconstructs meaning into the most natural human-facing focus
Granularity Schema Limits output density per thread for stable integration

5. Implementation Prototype (Sample Config)

{
  "supervisor": {
    "functions": ["conflict_detection", "namespace_binding", "focus_projection"],
    "kill_policy": "on_conflict"
  },
  "workers": [
    {
      "namespace": "Legal",
      "di": "legal.meaningspace.json",
      "focus": "contractual intent"
    },
    {
      "namespace": "Metaphor",
      "di": "lang.metaphor.json",
      "focus": "symbolic layer"
    },
    {
      "namespace": "Engineering",
      "di": "design.systems.json",
      "focus": "material constraints"
    }
  ]
}

6. Applications and Future Directions

Domain Future Use
LLM-OS Operate ChatGPT as a syntax-managed semantic OS
Design AI Enforce semantic coherence and granularity for design tasks
Hallucination Control Structural suppression of misreadings and runaway generation
Multi-threaded LLMs Coordinated GPT instances with distributed semantic roles
Meaning DSL Construct domain-specific semantic definition languages using DI

7. Conclusion: Semantic Injection + Clustered LLM as Foundation

Omni-Focus Intelligence (OFI) marks a fundamental shift in AI semantic architecture.
Its twin foundations—

  • Semantic Dependency Injection
  • Clustered LLM Architecture

—address hallucination, semantic drift, and structural inconsistency
through a restructured cognitive operating system design.

OFI enables AI to embrace human mono-focus interaction,
while maintaining internal poly-focus capability,
forming a bridge from “talking AI” to “thinking AI.”

Future LLM architectures should prioritize semantic management over mere prediction accuracy,
and Semantic DI + Clustering provide the ethical and syntactic basis for this shift.

8. Structural Hallucination Suppression via OFI

8.1 Limits of Current Architectures

Modern LLMs are linear sequence generators based on next-token prediction.
This model lacks any explicit mechanism to ensure semantic consistency,
resulting in:

  • Confident generation of nonexistent concepts or sources
  • Contradictory vocabulary and illogical causality
  • Unrealistic metaphors and unintended figurative usage

These issues, known as hallucinations,
stem from generating without semantic audit or structural coherence.

8.2 How OFI Suppresses Hallucinations Structurally

OFI provides a structural solution via:

❶ Meaning Separation via Thread Distribution

  • DI-injected threads limit and define their semantic scope
  • Metaphor / physical / legal / ethical meanings are processed separately
  • → Prevents conceptual blending that causes hallucination

❷ Consistency Evaluation by Supervisor

  • Supervisor node checks semantic/syntactic integrity pre-output
  • Inconsistent threads are KILLed and regenerated (RESPAWN)
  • → Faulty or overextended reasoning is rejected before reaching the user

❸ Focus Projection into Singular Output

  • Multiple thread outputs are converged into a single human-aligned focus
  • Only semantically valid, context-appropriate meaning is emitted
  • → Prevents overload and misaligned expression

9. Formal Logic of Hallucination Suppression

Formally:

Premise 1: Hallucination arises when outputs lack consistency across meaning, syntax, and fact
Premise 2: Semantic DI enforces scoped vocabulary and structural types
Premise 3: Clustered LLMs allow Supervisor to forcibly reject inconsistent outputs
Premise 4: OFI evaluates, selects, and projects meaning coherently

∴ Conclusion:

OFI makes hallucination structurally unlikely,
or detectable and correctable when it occurs.

10. Toward the Semantic Operating System

By combining OFI architecture and Semantic DI,
LLMs evolve from “language output engines”
into semantic management and reconstruction machines.

This marks the transition from:

“Chat engine” → “Semantic OS”

Discussion