{"total":20,"rows":[{"id":"2501.05534","title":"OmniJet-${\\alpha}_C$: Learning point cloud calorimeter simulations using generative transformers","created":"2025-01-09","cited_by_count":14,"area":null},{"id":"2509.21434","title":"Foundation models for high-energy physics","created":"2025-09-25","cited_by_count":10,"area":null},{"id":"2503.00131","title":"Fine-tuning machine-learned particle-flow reconstruction for new detector geometries in future colliders","created":"2025-02-28","cited_by_count":9,"area":null},{"id":"2601.17126","title":"EveNet: A Foundation Model for Particle Collision Data Analysis","created":"2026-01-23","cited_by_count":9,"area":null},{"id":"2412.10665","title":"Pretrained Event Classification Model for High Energy Physics Analysis","created":"2024-12-14","cited_by_count":8,"area":null},{"id":"2502.02558","title":"Particle Trajectory Representation Learning with Masked Point Modeling","created":"2025-02-04","cited_by_count":8,"area":null},{"id":"2509.07486","title":"RINO: Renormalization Group Invariance with No Labels","created":"2025-09-09","cited_by_count":6,"area":null},{"id":"2512.04149","title":"Enhancing next token prediction based pre-training for jet foundation models","created":"2025-12-03","cited_by_count":3,"area":null},{"id":"2410.13947","title":"MACK: Mismodeling Addressed with Contrastive Knowledge","created":"2024-10-17","cited_by_count":2,"area":null},{"id":"2510.21935","title":"AutoSciDACT: Automated Scientific Discovery through Contrastive Embedding and Hypothesis Testing","created":"2025-10-24","cited_by_count":2,"area":null},{"id":"2604.07037","title":"Towards foundation-style models for energy-frontier heterogeneous neutrino detectors via self-supervised pre-training","created":"2026-04-08","cited_by_count":2,"area":null},{"id":"2606.19781","title":"Towards Engineering Scaling Laws with Pretraining Data Composition","created":"2026-06-18","cited_by_count":2,"area":null},{"id":"2607.10039","title":"Are We Ready for AI-Driven Discovery? AI Verification Before the Next Fundamental Physics Breakthrough","created":"2026-07-10","cited_by_count":2,"area":null},{"id":"2609.31862","title":"Prompting Particle Physics: Tokenized Multi-modal Foundation Models for Combinatorially Many Tasks","created":"2026-09-25","cited_by_count":2,"area":null},{"id":"2504.03387","title":"BitHEP -- The Limits of Low-Precision ML in HEP","created":"2025-04-04","cited_by_count":1,"area":null},{"id":"2606.14373","title":"Machine-learned particle flow as a foundation model for collider physics","created":"2026-06-12","cited_by_count":1,"area":null},{"id":"2608.13633","title":"Unknown Unknowns: Model Misspecification in Machine Learning for Physics","created":"2026-08-13","cited_by_count":1,"area":null},{"id":"2602.00141","title":"Comparison of Image Processing Models in Quark Gluon Jet Classification","created":"2026-01-29","cited_by_count":0,"area":null},{"id":"2609.17726","title":"Self-Supervised Learning for Robust Resonance Mass Regression in Cascade Decays","created":"2026-09-15","cited_by_count":0,"area":null},{"id":"2609.17738","title":"Similarity Pairing with Energy Mover's Distance for Self-Supervised Pre-Training at the LHC","created":"2026-09-15","cited_by_count":0,"area":null}],"next_cursor":null}