Superseded by Neo-V3. Archived for history.

NEO-LAB

Deterministic Local Cognitive Control Plane

NEO-LAB is a local, deterministic cognitive control plane for orchestrating multiple locally hosted large language models (LLMs) as role-separated inference engines under explicit governance, bounded memory, and fully inspectable state.

NEO-LAB is designed as a professional engineering lab assistant, not an autonomous agent.

All execution is local.
All state is explicit.
All behavior is deterministic.

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System Overview

NEO-LAB implements a control-plane architecture for AI inference rather than a traditional chatbot interface.
It treats LLMs as stateless, interchangeable compute engines governed by a stateful, deterministic controller.

The system prioritizes:

• Predictability over emergence
• Inspectability over opacity
• Governance over autonomy

This makes NEO-LAB suitable for professional, technical, and safety-conscious workflows.

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What NEO-LAB Is

NEO-LAB is:

• A deterministic router (rule-based intent → model selection)
• A governed control plane (explicit state, modes, and contracts)
• A file-driven IPC system (atomic, concurrency-safe)
• A local-first AI system (offline-capable, no cloud dependency)
• A single-response generator for summaries, detailed explanations, and multi-page reports

NEO-LAB is not:

• An autonomous agent
• A self-modifying system
• A tool-execution framework
• A blended or ensemble model
• A cloud service

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Core Architecture (Atomic File-Based IPC)

NEO-LAB uses an atomic per-message file queue to eliminate race conditions and ensure deterministic execution.

queue_v2/ ├─ inbox/ # One JSON file per user message ├─ outbox/ │ └─ <message_id>/ │ ├─ status.json # Execution phase, progress counters │ └─ response.txt # Streaming model output └─ processed/ # Archived input messages

Processing Flow

User ↓ neo_chat.ps1 ↓ (atomic JSON message) queue_v2/inbox/.json ↓ neo_loop.ps1 ├─ intent classification ├─ explicit state & persona loading ├─ deterministic model routing ├─ streaming inference ├─ bounded memory update ↓ queue_v2/outbox//response.txt

Key properties:

• No message overwrites
• Safe concurrency
• Replayable execution
• Fully inspectable artifacts

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Model Roles (One Active Model per Request)

NEO-LAB routes requests deterministically.
Exactly one model is active per request.

Typical role mapping (configurable):

Cognitive Role	Model
Chat / Persona	dolphin-llama3
Code	deepseek-coder-v2
Analysis	deepseek-r1
Vision	qwen2.5-vl

Model availability is queried dynamically via Ollama.
If a target model is unavailable, NEO-LAB fails closed or falls back safely.

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Output Modes (Single-Response Guarantees)

NEO-LAB is designed to produce one complete response per request, regardless of domain.

Supported one-shot modes:

• /summary <topic> — concise, complete response
• /detail <topic> — detailed technical explanation
• /report <topic> — single multi-page structured report

Report Writer Mode

When enabled (/reportmode on), NEO-LAB enforces a strict output contract.

Required sections:

1. Executive Summary
2. Scope & Assumptions
3. Core Analysis
4. Methods / Models / Math (if applicable)
5. Implementation (if applicable)
6. Risks, Limitations, Verification Checklist
7. References / Source Guidance (if applicable)

No follow-up questions.
No partial answers.
One complete professional deliverable.

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Streaming Output & Progress Visibility

NEO-LAB streams output incrementally while models generate.

Progress is written to:

queue_v2/outbox//status.json

Including:

• Current execution phase
• Characters written
• Streaming activity indicators

This ensures transparency during long-running analyses and reports.

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Memory System (Bounded & Inspectable)

Memory is:

• JSON-based
• Explicitly bounded
• Stored on disk
• Separated by intent

queue/ ├─ memory_chat.json ├─ memory_code.json ├─ memory_analysis.json └─ memory_vision.json

There are:

• No embeddings
• No hidden vectors
• No implicit recall

Memory can be inspected or cleared at any time.

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Governance & Safety Posture

NEO-LAB is designed with explicit safety constraints:

• No autonomy
• No self-execution
• No self-modification
• No privilege escalation
• Human remains root authority

The system favors control, auditability, and predictability over emergent behavior.

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Requirements

• Windows 10 / 11
• PowerShell 5.1
• Ollama (local inference server)

Recommended Ollama models:

• dolphin-llama3
• deepseek-coder-v2
• deepseek-r1
• qwen2.5-vl

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Quick Start

Start the control loop (Terminal A):

cd C:\ai_control\NEO_Stack
.\neo_loop.ps1


Start the chat client (Terminal B):

cd C:\ai_control\NEO_Stack
.\neo_chat.ps1


Example:

/report Write a multi-page engineering report explaining why atomic IPC queues prevent race conditions.

QA Harness (Optional)

A simple QA harness validates report structure and completeness:

powershell -ExecutionPolicy Bypass -File tests\qa\qa_harness.ps1


Results are written to:

tests/qa/qa_results.json

Roadmap

Report size governor (short / normal / long)

Live-tail streaming output in chat client

Stronger output validation and regression tests

Optional message integrity verification

License

See [LICENSE](LICENSE).

Disclaimer

NEO-LAB provides general informational output only.
For legal, medical, or financial decisions, consult qualified professionals and primary sources.