The Ontological Priority of the Void - Multi-AI Structured Synthesis for Vacuum Clockwork Relaxion Modelling: Proof-of-Concept & Audit Trail

This manuscript serves a dual purpose introducing both the Vacuum State Transition Cycles (VSTC/VCR) framework and providing a proof-of-concept for Multi-Agent Adversarial Synthesis in theoretical physics, developed and refined utilising a multi-AI structured synthesis methodology, I was working on this separately, which I then ended up incorporating into my other project as well, leveraging a combination of multiple, easily accessible large language models: including Gemini (Google DeepMind AI), ThetaWise.AI, Kimi (Moonshot AI), and Claude (Anthropic AI) in an adversarial, layered feedback loop, this project demonstrates how complex theoretical frameworks can be audited, improved, and made more internally consistent. The VSTC (Vacuum State Transition Cycles) framework began from a fundamental question and idea I had in my room about the Big Bang and the nature of the pre-existing “space” or vacuum” in which it occurred. Conventional cosmology models treat the vacuum or void as secondary, a backdrop for events like the Big Bang. But this had me curious: what if the vacuum itself is primary, a structured substrate that governs and contains the initial conditions of cosmic events? This idea led me down a rabbit hole to a model reminiscent of Roger Penrose’s Conformal Cyclic Cosmology (CCC), focusing on pre-geometric vacuum states that transition in cycles, starting from the premise that the container of the universe, the vacuum itself, may itself have intrinsic structure, VSTC proposes a mechanism where vacuum states evolve and give rise to physically measurable parameters, rather than assuming them as given. VSTC’s development naturally led to the multi-AI methodology for auditing, refining, and translating these ideas into the VCR (Vacuum Clockwork Relaxion) framework, exploring the idea with theoretical physics concepts within computational exploration. All of the datasets presented, including the audit logs, prompts, screenshots, and generated visuals capture both the scientific progression of the development from the idea of VSTC into the VCR framework and the methodology of multi-AI collaboration, highlighting strengths, weaknesses, and epistemic limits of AI-assisted research. This record serves as a proof-of-concept, a documentation of the iterative methodology, and an invitation for constructive critique, collaboration, and exploration. Researchers, practitioners, and AI enthusiasts are encouraged to examine, test, and build upon this work.