{"total":41,"rows":[{"id":"2403.05618","title":"OmniJet-$\\alpha$: The first cross-task foundation model for particle physics","created":"2024-03-08","cited_by_count":43,"area":null},{"id":"2403.07066","title":"Re-Simulation-based Self-Supervised Learning for Pre-Training Foundation Models","created":"2024-03-11","cited_by_count":22,"area":null},{"id":"2409.12589","title":"Is Tokenization Needed for Masked Particle Modelling?","created":"2024-09-19","cited_by_count":22,"area":null},{"id":"2403.08851","title":"PAPERCLIP: Associating Astronomical Observations and Natural Language with Multi-Modal Models","created":"2024-03-13","cited_by_count":15,"area":null},{"id":"2405.06605","title":"Calo-VQ: Vector-Quantized Two-Stage Generative Model in Calorimeter Simulation","created":"2024-05-10","cited_by_count":15,"area":null},{"id":"2501.05382","title":"Large Physics Models: Towards a collaborative approach with Large Language Models and Foundation Models","created":"2025-01-09","cited_by_count":14,"area":null},{"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":"2602.15781","title":"Neural Scaling Laws for Boosted Jet Tagging","created":"2026-02-17","cited_by_count":12,"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":"2505.08736","title":"Towards Foundation Models for Experimental Readout Systems Combining Discrete and Continuous Data","created":"2025-05-13","cited_by_count":5,"area":null},{"id":"2511.15779","title":"SURFing to the Fundamental Limit of Jet Tagging","created":"2025-11-19","cited_by_count":3,"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":"2512.15230","title":"ColliderML: The First Release of an OpenDataDetector High-Luminosity Physics Benchmark Dataset","created":"2025-12-17","cited_by_count":3,"area":null},{"id":"2604.12364","title":"Cross-Domain Transfer with Particle Physics Foundation Models: From Jets to Neutrino Interactions","created":"2026-04-14","cited_by_count":3,"area":null},{"id":"2606.14813","title":"JetParticle-JEPA: An Efficient Self-Supervised Representation Learning method for Jet Tagging in High-Energy Physics","created":"2026-06-12","cited_by_count":3,"area":null},{"id":"2412.12074","title":"Extrapolating Jet Radiation with Autoregressive Transformers","created":"2024-12-16","cited_by_count":2,"area":null},{"id":"2509.01397","title":"Double Descent and Overparameterization in Particle Physics Data","created":"2025-09-01","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":"2510.06691","title":"Latent Representation Learning in Heavy-Ion Collisions with MaskPoint Transformer","created":"2025-10-08","cited_by_count":1,"area":null},{"id":"2511.12829","title":"An Evaluation of Representation Learning Methods in Particle Physics Foundation Models","created":"2025-11-16","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":"2607.27501","title":"A Lightweight Foundation Model for Collider Physics with Multi-Domain Adaptation","created":"2026-07-29","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":"2608.14278","title":"Pairton: Iterative Reconstruction of Short-Lived Particles","created":"2026-08-14","cited_by_count":1,"area":null},{"id":"2609.00611","title":"Panda Diplomacy: Foundation Model Pre-training across Particle Imaging Detectors for High Energy and Nuclear Physics","created":"2026-09-01","cited_by_count":1,"area":null},{"id":"2610.00569","title":"Scaling Collider Event Generation with Residual-Quantized Tokens","created":"2026-09-30","cited_by_count":1,"area":null},{"id":"2407.21290","title":"TrackSorter: A Transformer-based sorting algorithm for track finding in High Energy Physics","created":"2024-07-31","cited_by_count":0,"area":null},{"id":"2505.07769","title":"Tagging fully hadronic exotic decays of the vectorlike $\\mathbf{B}$ quark using a graph neural network","created":"2025-05-12","cited_by_count":0,"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":"2607.16144","title":"Learning Standard Model structure from LHC data with Riemannian flow matching","created":"2026-07-17","cited_by_count":0,"area":null},{"id":"2609.09275","title":"\"Transforming\" LHCb: self-supervised maps of heavy-flavour decays","created":"2026-09-08","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},{"id":"2610.13437","title":"Local Diagnostics of Learned Event Representations for Anomaly Detection at the HL-LHC","created":"2026-10-09","cited_by_count":0,"area":null}],"next_cursor":null}