[2603.04735] Solving an Open Problem in Theoretical Physics using AI-Assisted Discovery

Computer Science > Artificial Intelligence arXiv:2603.04735 (cs) [Submitted on 5 Mar 2026] Title:Solving an Open Problem in Theoretical Physics using AI-Assisted Discovery Authors:Michael P. Brenner, Vincent Cohen-Addad, David Woodruff View a PDF of the paper titled Solving an Open Problem in Theoretical Physics using AI-Assisted Discovery, by Michael P. Brenner and 1 other authors View PDF HTML (experimental) Abstract:This paper demonstrates that artificial intelligence can accelerate mathematical discovery by autonomously solving an open problem in theoretical physics. We present a neuro-symbolic system, combining the Gemini Deep Think large language model with a systematic Tree Search (TS) framework and automated numerical feedback, that successfully derived novel, exact analytical solutions for the power spectrum of gravitational radiation emitted by cosmic strings. Specifically, the agent evaluated the core integral $I(N,\alpha)$ for arbitrary loop geometries, directly improving upon recent AI-assisted attempts \cite{BCE+25} that only yielded partial asymptotic solutions. To substantiate our methodological claims regarding AI-accelerated discovery and to ensure transparency, we detail system prompts, search constraints, and intermittent feedback loops that guided the model. The agent identified a suite of 6 different analytical methods, the most elegant of which expands the kernel in Gegenbauer polynomials $C_l^{(3/2)}$ to naturally absorb the integrand's singularitie...