LLM embeddings condition generative networks for LHC events, yielding faster convergence, higher quality, and generalization to unseen processes.
Resimulation-based self-supervised learning for pretraining physics foundation models.Phys
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Pretraining data composition can engineer scaling laws for jet classification to favor data scaling over model scaling.
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One Generator, Any Process: LLM-Conditioning for the LHC
LLM embeddings condition generative networks for LHC events, yielding faster convergence, higher quality, and generalization to unseen processes.
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Towards Engineering Scaling Laws with Pretraining Data Composition
Pretraining data composition can engineer scaling laws for jet classification to favor data scaling over model scaling.