Solver Agent: an Agentic AI Framework for Theoretical Physics Computations Applied to F-theory Uplifts of O3-planes and S-folds
hep-th, cs.AI, math-ph, math.AG, math.MP
Submitted: 2026-09-28
Updated: 2026-09-28
Comments: Comments: 85 pages + appendices. Solver Agent is available in https://github.com/starrfree/solver-agent. Sessions for reproducibility are available in https://storage.googleapis.com/solver-agent-sessions/solver-agent-f-theory-uplifts/index.html. Further codes in https://github.com/alexmininno/orientifold_databases
Code: https://github.com/starrfree/solver-agent
License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
Terminology
Sources
- Single-minus gluon tree amplitudes are nonzero
- Deep-Learning the Landscape
- Branes with Brains: Exploring String Vacua with Deep Reinforcement Learning
- Breeding realistic D-brane models
- 134 Billion Intersecting Brane Models
- Probing the Structure of String Theory Vacua with Genetic Algorithms and Reinforcement Learning
- Transforming Calabi-Yau Constructions: Generating New Calabi-Yau Manifolds with Transformers
- Sampling String Vacua Using Generative Models
- Machine Learning Line Bundle Cohomology
- Machine Learning Calabi-Yau Four-folds
- Moduli-dependent Calabi-Yau and SU(3)-structure metrics from Machine Learning
- From Algebraic Geometry to Machine Learning
- Learning Size and Shape of Calabi-Yau Spaces
- Heterotic String Model Building with Monad Bundles and Reinforcement Learning
- Numerical Metrics for Complete Intersection and Kreuzer-Skarke Calabi-Yau Manifolds
- Metric Flows with Neural Networks
- Lectures on Numerical and Machine Learning Methods for Approximating Ricci-flat Calabi-Yau Metrics
- W0_sample = np.random.normal(0,1)?
- Warped Numerical Calabi-Yau Metrics
- Improving Physics Reasoning in Large Language Models Using Mixture of Refinement Agents
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