LLM embeddings condition generative networks for LHC events, yielding faster convergence, higher quality, and generalization to unseen processes.
Learning the language of QCD jets with transformers
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Transformers trained on cosmic ray simulations learn physically plausible features in positional encodings for symmetric air showers and in attention mechanisms for galaxy-origin particles.
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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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What exactly did the Transformer learn from our physics data?
Transformers trained on cosmic ray simulations learn physically plausible features in positional encodings for symmetric air showers and in attention mechanisms for galaxy-origin particles.