At Lumina Research, we ensure the highest investigative, ethical, and technical quality. Currently in our research and testing phase, we are establishing the foundations for verifiable AI alignment. Below is a deep dive into our system architecture, training pipelines, and secure execution engine. The open weights model is available upon request for research and validation.
1. The Complete System: Humanist Alignment Protocol
The Human Compliance Committee defines what an aligned AI means. Phase 1 produces synthetic alignment data, Phase 2 distills it into an SLM operating 100% offline, and an Algorithmic Verifier blocks any deviation.
flowchart LR
subgraph GOB [Institutional Compliance Committee]
ENC["Institutional Ethical Framework"]
COM["Responsible Committee"]
CON("Operational Constitution")
EVA[("Evaluation Set")]
end
subgraph F1 [Phase 1: Alignment Factory]
MAE["Master Model and Critic Panel"]
DAT["SFT Data and DPO Pairs"]
end
subgraph F2 [Phase 2: Local Engine Edge AI]
FT["SLM (e.g. Llama 3.1 8B)"]
G{"Certification Gate"}
REL["Release Candidate"]
end
subgraph RUN [Offline Execution Zero-Trust]
UI["User Interface"]
BE["Backend Orchestrator"]
MCP["MCP Server (RAG)"]
DB[("Corporate Database")]
LLM["Local SLM"]
VER{"Strict JSON Verifier"}
end
AUD["Auditor Module"]
ENC --> COM
COM --> CON
CON --> MAE
CON --> EVA
MAE --> DAT
DAT --> FT
FT --> G
EVA --> G
G -->|Certified| REL
REL --> LLM
G -->|Failure| MAE
UI --> BE
BE --> MCP
MCP --> BE
MCP --> DB
DB --> MCP
BE --> LLM
LLM --> VER
VER -->|Validated format| UI
VER -->|Violation| BE
UI -. Reports .-> COM
CON -.-> AUD
MAE -.-> AUD
2. Recursive Training Flow (Constitutional AI / RLAIF)
This diagram shows how the model continuously aligns with the ethical constitution through a loop of generation, self-critique, and direct preference optimization (DPO).
graph TD
A[SFT Seed Dataset] -->|Trains| B(Base Model V1)
subgraph GS [Generation and Self-Critique]
B -->|Generates Response| C{Initial Response}
C --> D[Critical Evaluator]
E[("Constitution: Human/Ethics")] -. Defines rules .-> D
D -->|Identifies biases| F(Critique and Suggestions)
F --> G[Model Rewrite]
G --> H{Corrected Response}
end
subgraph PO [Preference Optimization]
C -. Rejected Response .-> I[DPO Dataset]
H -. Preferred Response .-> I
end
I -->|DPO Training| J(Optimized Model V2)
J -. New Recursive Cycle .-> B
3. Fine-Tuning Pipeline (MLX on Apple Silicon)
Optimization for local deployments, running LoRA training and quantization on standard hardware (Apple Silicon) using MLX, ensuring complete Data Sovereignty.
graph TD
subgraph P1 [Preparation]
A["dataset_lumina.jsonl"] --> B("HuggingFace Hub")
B -->|Download| C["Llama 3.1 8B Base"]
end
subgraph EL [Local Training Apple Silicon]
C --> D["Native MLX Framework"]
A --> D
D -->|LoRA| E["Unified Memory Training"]
end
subgraph AF [Final Artifacts]
E --> F["LoRA Adapter Weights"]
C -.-> G["Fusion (Fuse)"]
F --> G
G --> H["Fine-Tuned Model (Lumina)"]
end
4. Secure Execution Engine (RAG + Verifier)
Production Inference Architecture: the SLM runs offline, the MCP server injects corporate documents, and a module forces output to strict JSON to certify policies before reaching the user.
sequenceDiagram
autonumber
actor U as User
participant FE as Frontend UI
participant BE as Backend Orchestrator
participant MCP as RAG Server
participant DB as Database (Policies)
participant LLM as Local SLM
participant V as Strict Verifier
U->>FE: Enters ethical/technical query
FE->>BE: Transmits query locally
BE->>MCP: Requests document context
MCP->>DB: Semantic Vector Search
DB-->>MCP: Returns Policies
MCP-->>BE: Context injected
BE->>LLM: Prompt (Context + Query)
Note over LLM: Output forced to structured JSON
LLM-->>BE: JSON Object
BE->>V: Validates schema, citations and scope
alt Correct JSON
V-->>BE: Passes Certification
BE-->>FE: Verified Payload
FE-->>U: Response with Verifiable Citations
else Hallucination / Broken Format
V-->>BE: Rejected
BE->>LLM: Automatic Re-prompt (Max 2)
LLM-->>BE: Corrected JSON
BE-->>FE: Secure Payload
end