Provenance and the Use of LLMs in CAGF Development

Demonstrating Governance Through Practice

The Citadel Reasoning Framework (CAGF) is more than a model for governing cognitive systems, it is itself a product of cognitive architecture governance. Every concept, decision, and section within CAGF was shaped by the deliberate and transparent use of a multi-agent generative AI pipeline designed to embody the very principles the framework advocates: traceability, bias mitigation, iterative refinement, and responsible orchestration of intelligent systems.

Rather than conceal the development process, CAGF exposes and codifies it. The intent is not only to model best practices in AI-augmented governance, but to set a precedent: that future frameworks, policies, and standards in the age of intelligent systems must be accountable not just for their content, but for how they came into being.

Purpose of Provenance

In a time where intellectual artifacts are increasingly shaped or co-created by artificial intelligence, provenance is no longer optional, it is essential.

Provenance clarifies:

Which systems contributed to which ideas
What assumptions were challenged
How critiques were reconciled
Where human judgment prevailed over automation

For CAGF, provenance affirms legitimacy, establishes trust, and demonstrates that multi-agent cognitive workflows can yield superior strategic outcomes when architected with intent.

LLM Pipeline Overview

CAGF was developed through a recurring four-stage multi-LLM pipeline. Each LLM was chosen for its domain strengths, cognitive profile, and its ability to serve a unique role within a layered system of critical discourse.

LLM Pipeline Roles

Architect: Claude 4 Sonnet – Framing new concepts, deriving first principles, and ethical boundary-setting.

Adjudicator: Gemini 2.5 Pro – Simulated critique, stress-testing against academic and industry expectations.

Synthesizer: ChatGPT-4o – Narrative refinement, structural harmonization, and integration across modules.

Sanitizer: Microsoft Copilot / QA LLMs – Enterprise-readiness checks, policy alignment, and compliance phrasing.

Human-in-the-Loop Governance

While LLMs served as catalysts, critics, and collaborators, human judgment remained the final authority. All model outputs were reviewed, interpreted, and either adopted, modified, or rejected by the principal architect based on domain expertise, cross-checking, and alignment with CAGF’s core mission.

Bias Mitigation Strategy

To reduce systemic bias and model-specific blind spots:

No single LLM was used as a sole authority on any section
Contradictory outputs were intentionally solicited to surface assumptions
Model behaviors were compared across versions and providers
Rewrites were prompted in adversarial tones to simulate real-world pushback

Traceability and Section Metadata

For future iterations, online versions of CAGF may include section-level metadata indicating:

LLMs used in development
Key challenges addressed
Prompts or decision logs
Revision lineage (inspired by software version control)

Lessons Learned from LLM-Oriented Development

AI systems don’t just produce text, they provoke insight.
Using multiple models is not redundant, it’s essential.
Governance is stronger when modeled, not just explained.

Why This Matters

The future of governance will not be written about AI. It will be written with AI. CAGF demonstrates how that can be done responsibly.

By exposing its cognitive development lineage, CAGF does not just present a theory of governance, it enacts it.