Solver Agent: an Agentic AI Framework for Theoretical Physics Computations Applied to F-theory Uplifts of O3-planes and S-folds
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Abstract (arXiv)
We introduce Solver Agent, an AI framework based on large language models for calculations and proofs in mathematics and theoretical physics. The solution process is tracked through a persistent ledger that records assumptions, derivations, and computations. A central agent delegates tasks to specialized sub-agents, while independent agents verify both intermediate steps and the final result. This setup improves the traceability, reproducibility, and verification of computer-assisted calculations. Applying Solver Agent, we study global F-theory uplifts of Type IIB orientifolds and their S-fold generalizations. We establish sufficient conditions for Weierstrass models over projective threefolds with terminal mathbbZk quotient singularities (k∈2,3,4,6) to give mathbbQ-factorial projective elliptically fibered Calabi-Yau fourfolds with isolated Gorenstein terminal quotient singularities. These geometries realize O3-planes and S-folds, where local D3-brane probes of the latter yield four-dimensional N=3 superconformal field theories. Using stringy invariants, we derive fixed-point contributions to Hodge data and Euler characteristics, and show that these Euler corrections determine the localized D3-brane charges required for tadpole cancellation. We illustrate these results using toric hypersurface constructions, where a single three-dimensional polytope determines both the Type IIB Calabi-Yau threefold and the F-theory base; here, the orientifold double cover naturally forms a bisection of an alternative genus-one-fibered uplift with discrete mathbbZ2 gauge symmetry. Finally, we provide methods for toric computations and four-form flux analysis in four-dimensional N=1 compactifications with non-abelian gauge sectors.
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References · 5
- 2022ReAct: Synergizing Reasoning and Acting in Language Modelscited by 8,695
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- 2024Improving Physics Reasoning in Large Language Models Using Mixture of Refinement Agentscited by 24
- 2025Advancing AI-Scientist Understanding: Multi-Agent LLMs with Interpretable Physics Reasoningcited by 5
- 2026Formalization of QFTcited by 4
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