TriSearch is an RL framework that optimizes triangulations of polytopes using bistellar flips with a circuit-supported subtriangulation action representation, generalizing zero-shot to larger instances and outperforming prior samplers in 3D and 4D.
Transforming Calabi-Yau Constructions: Generating New Calabi-Yau Manifolds with Transformers
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Transformers reconstruct the constituent RCFTs in tensor-product theories from low-energy spectra, reaching 98% accuracy on WZW models and generalizing to larger central charges with few out-of-domain examples.
Transformers generate new FRSTs of 4D reflexive polytopes across size ranges and self-improve by retraining on their own outputs.
Introduces dualGNN, an autoregressive message-passing GNN using signed circuits to sample uniform fine regular triangulations of lattice polytopes, applied to Calabi-Yau threefolds at h^{1,1}=86 and 128.
An analytic bound on axion parameters in thawing quintessence is derived independently of initial conditions and used with cosmological observations plus quantum gravity constraints to exclude large regions of axion dark energy parameter space.
A Transformer trained by reinforcement learning generates heterotic line-bundle sums that satisfy anomaly-cancellation, stability, and chirality constraints, and its policy transfers usefully across Calabi-Yau geometries.
citing papers explorer
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TriSearch: Learning to Optimize Triangulations via Bistellar Flips
TriSearch is an RL framework that optimizes triangulations of polytopes using bistellar flips with a circuit-supported subtriangulation action representation, generalizing zero-shot to larger instances and outperforming prior samplers in 3D and 4D.
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Reconstructing conformal field theoretical compositions with Transformers
Transformers reconstruct the constituent RCFTs in tensor-product theories from low-energy spectra, reaching 98% accuracy on WZW models and generalizing to larger central charges with few out-of-domain examples.
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Generating Special Triangulations with Transformers
Transformers generate new FRSTs of 4D reflexive polytopes across size ranges and self-improve by retraining on their own outputs.
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Sampling Triangulations and Calabi-Yau Threefolds with Autoregressive GNNs
Introduces dualGNN, an autoregressive message-passing GNN using signed circuits to sample uniform fine regular triangulations of lattice polytopes, applied to Calabi-Yau threefolds at h^{1,1}=86 and 128.
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Bounding axion dark energy
An analytic bound on axion parameters in thawing quintessence is derived independently of initial conditions and used with cosmological observations plus quantum gravity constraints to exclude large regions of axion dark energy parameter space.
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Exploring Line Bundle Standard Models with Transformers
A Transformer trained by reinforcement learning generates heterotic line-bundle sums that satisfy anomaly-cancellation, stability, and chirality constraints, and its policy transfers usefully across Calabi-Yau geometries.