Abstract This paper presents a 97-page formal exploration of the Yang-Mills mass gap problem, one of the Clay Mathematics Institute's Millennium Prize Problems, generated through an extended experiment in agentic AI-assisted research. The work employs standard machinery from constructive quantum field theory: lattice gauge theory with the Wilson action, cluster expansions, Balaban's renormalization group methods, reflection positivity, and the Osterwalder-Schrader axiom framework. The central argument constructs a dimensionless gap function f(g²) = am(a) and attempts to establish a positive minimum via continuity and endpoint asymptotics. This paper has not been verified by domain experts and does not constitute a peer-reviewed proof. It is published as a documented research artifact of an experiment in AI capability, specifically what agentic systems produce when directed toward a precisely constrained, formally rich, unsolved problem. The mathematical structure is coherent at the level of grammar and citation; the author makes no claim as to its validity beyond that. Author's Note This paper was produced in May 2025 as part of an extended experiment using agentic AI systems, primarily Claude Code and Cursor, to explore the limits of what these tools can do when directed at a genuinely hard problem. The goal was not (realistically) to solve the Yang-Mills mass gap. It was to develop intuition about how AI systems pursue mathematical reasoning, where they succeed structurally, and where the reasoning breaks down. The result is a document that reproduces the culture and grammar of mathematical physics with surprising fidelity, while containing a central argument that assumes what it needs to prove. I am not a mathematician, and I would rather not contribute to bloating scientific literature channels with AI-generated slop without clarifying the context of this experiment. I am leaving this paper up because I think the honest version is more interesting than a quietly deleted file. If you are a mathematician: treat this with appropriate skepticism. If you are someone thinking about AI capabilities: the gap between fluency and understanding, documented here at 97 pages, might be worth your attention.
Adrien Sacha Marie Ancri (Thu,) studied this question.