The work demonstrates masked-token prediction with transformers for model-independent anomaly detection in LHC data, achieving strong results on top-rich BSM signatures like four-top production using VQ-VAE tokenization.
Ramprasad et al., Large Language Models in Science, arXiv:2501.05382 (2025)
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HEP-CoPilot is a new multi-agent retrieval framework that retrieves, reconstructs, and compares experimental limits from HEP literature and HEPData to support interpretation of beyond-Standard-Model searches.
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Masked-Token Prediction for Anomaly Detection at the Large Hadron Collider
The work demonstrates masked-token prediction with transformers for model-independent anomaly detection in LHC data, achieving strong results on top-rich BSM signatures like four-top production using VQ-VAE tokenization.
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From Experimental Limits to Physical Insight: A Retrieval-Augmented Multi-Agent Framework for Interpreting Searches Beyond the Standard Model
HEP-CoPilot is a new multi-agent retrieval framework that retrieves, reconstructs, and compares experimental limits from HEP literature and HEPData to support interpretation of beyond-Standard-Model searches.