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New Study Compares Pre-Training Methods for AI in High-Energy Physics

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A new arXiv paper explores pre-training objectives for foundation models in simulation-based sciences, specifically focusing on high-energy physics. The study compares supervised classification, flow-matching generation, and self-supervised masked particle modeling using the OmniLearned High Energy Physics FM framework. Results indicate that pure classifier pre-training is best when labels are abundant, but combining it with masked particle modeling is highly effective in low-label scenarios. For generative tasks, flow matching must be included in pre-training for significant downstream advantages. AI

RANK_REASON The cluster contains an academic paper detailing research findings on AI model training objectives. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.LG →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

New Study Compares Pre-Training Methods for AI in High-Energy Physics

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The cluster contains an academic paper detailing research findings on AI model training objectives. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.LG TIER_1 English(EN) · Ibrahim Elsharkawy, Joschka Birk, Vinicius Mikuni, Wahid Bhimji, Gregor Kasieczka, Benjamin Nachman ·

    Pre-Training for Simulation-Based Science: A Study on Jet Foundation Model Training Objectives

    arXiv:2606.14870v1 Announce Type: cross Abstract: Foundation models (FMs) trained on large datasets and fine-tuned on downstream tasks have emerged as a powerful paradigm in AI for science. Industrial FMs are typically trained using self-supervision with masking due to the lack o…

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