LLM embeddings condition a generative transformer to enable faster convergence, better performance, and generalization to unseen LHC processes using a single model.
Bumblebee: Foundation Model for Particle Physics Discovery 2024
3 Pith papers cite this work. Polarity classification is still indexing.
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Pith papers citing it
verdicts
UNVERDICTED 3representative citing papers
A single MoE-based foundation model with transformer backbone unifies simulation, PID, and noise filtering for the GlueX DIRC detector and matches or exceeds traditional geometrical and prior deep-learning methods across kinematics.
A GNN pretrained on 120M simulated HEP events generalizes to unseen processes and ATLAS data; fine-tuning boosts accuracy especially with small datasets, with CKA showing preserved encoders but altered intermediate layers.
citing papers explorer
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One Generator, Any Process: LLM-Conditioning for the LHC
LLM embeddings condition a generative transformer to enable faster convergence, better performance, and generalization to unseen LHC processes using a single model.
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Application of a Mixture of Experts-based Foundation Model to the GlueX DIRC Detector
A single MoE-based foundation model with transformer backbone unifies simulation, PID, and noise filtering for the GlueX DIRC detector and matches or exceeds traditional geometrical and prior deep-learning methods across kinematics.
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Pretrained Event Classification Model for High Energy Physics Analysis
A GNN pretrained on 120M simulated HEP events generalizes to unseen processes and ATLAS data; fine-tuning boosts accuracy especially with small datasets, with CKA showing preserved encoders but altered intermediate layers.