Learning to Trace Seiberg Dualities

arXiv:2607.28628 · hep-th, cs.AI, cs.LG, hep-ph · Submitted 2026-07-30 · Read on arXiv

hep-th, cs.AI, cs.LG, hep-ph

Submitted: 2026-07-30

Updated: 2026-08-29

Terminology

• Accuracy — 23× in this paper · explained in 1 episode
• Transformer — 23× in this paper · explained in 2 episodes
• Success Rate — 20× in this paper
• Efficiency Ratio — 17× in this paper
• Benchmark — 11× in this paper · explained in 2 episodes
• Adjacency Matrix — 10× in this paper · explained in 1 episode
• Effective Efficiency Ratio — 8× in this paper
• GNNs — 7× in this paper · explained in 2 episodes
• Guidance Efficiency — 7× in this paper
• Embeddings — 6× in this paper · explained in 2 episodes
• Trace — 6× in this paper · explained in 2 episodes
• Consistency — 5× in this paper · explained in 3 episodes
• Generalization — 5× in this paper · explained in 13 episodes
• Pruning — 4× in this paper · explained in 4 episodes
• Activation Function — 3× in this paper · explained in 1 episode
• Attention Mechanism — 3× in this paper · explained in 3 episodes
• Benchmarking — 3× in this paper · explained in 3 episodes
• Breadth-First Search — 3× in this paper
• Failure Modes — 3× in this paper · explained in 3 episodes
• Graph Neural Networks (GNNs) — 3× in this paper · explained in 7 episodes
• Hybrid Approach — 2× in this paper · explained in 4 episodes
• Latent Space — 2× in this paper · explained in 7 episodes
• Loss Function — 2× in this paper · explained in 2 episodes
• Mean Absolute Error — 2× in this paper
• Multi-Head Attention — 2× in this paper
• Robustness — 2× in this paper · explained in 5 episodes
• Verifier — 2× in this paper · explained in 2 episodes
• Action Masking — 1× in this paper · explained in 1 episode
• Admissibility — 1× in this paper · explained in 2 episodes
• Aggregation — 1× in this paper · explained in 1 episode
• Calibration — 1× in this paper · explained in 7 episodes
• Generative Model — 1× in this paper · explained in 2 episodes
• Generative Models — 1× in this paper · explained in 2 episodes
• Hybrid Models — 1× in this paper · explained in 2 episodes
• Large Language Models (LLMs) — 1× in this paper · explained in 6 episodes
• Mean Squared Error — 1× in this paper
• Message Passing Neural Network — 1× in this paper
• Physical Constraints — 1× in this paper · explained in 7 episodes
• Redundancy — 1× in this paper · explained in 2 episodes
• Regularization — 1× in this paper · explained in 3 episodes
• Reinforcement Learning — 1× in this paper · explained in 4 episodes
• Scaling Laws — 1× in this paper · explained in 4 episodes
• Tokenization — 1× in this paper · explained in 2 episodes

Sources

• Electric-Magnetic Duality in Supersymmetric Non-Abelian Gauge Theories
• Cluster algebras I: Foundations
• Cluster algebras II: Finite type classification
• Supergravity and a Confining Gauge Theory: Duality Cascades and $\chi$SB-Resolution of Naked Singularities
• Superconformal Field Theory on Threebranes at a Calabi-Yau Singularity
• Gravity Duals of Supersymmetric SU(N) x SU(N+M) Gauge Theories
• Chaotic Duality in String Theory
• Statistical Inference and String Theory
• Relative Entropy and Proximity of Quantum Field Theories
• Misanthropic Entropy and Renormalization as a Communication Channel
• Higher Symmetries of 5d Orbifold SCFTs
• Quiver Approach to Symmetry Theories
• Quiver Mutations, Seiberg Duality and Machine Learning
• BPS spectroscopy with reinforcement learning
• Learning to Unknot
• Grokking modular arithmetic
• Scattering Amplitudes and the Positive Grassmannian
• Lectures on D-branes, Gauge Theories and Calabi-Yau Singularities
• D-Branes on Calabi-Yau Manifolds
• A Comprehensive Survey of Brane Tilings

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