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New pre-training method boosts jet foundation model performance

By PulseAugur Editorial · [1 sources] ·

Researchers have developed an improved pre-training method for jet foundation models, building upon the initial OmniJet-alpha work. This new approach enhances next token prediction by incorporating continuous feature vectors alongside token IDs and introduces a combined pre-training strategy that merges masked particle modeling with generative learning objectives. These enhancements significantly boost performance in downstream classification tasks without compromising generative capabilities. AI

IMPACT Introduces a novel pre-training strategy that could improve the efficiency and effectiveness of foundation models in scientific domains.

RANK_REASON Academic paper detailing a new method for foundation models. [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 pre-training method boosts jet foundation model performance

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Academic paper detailing a new method for foundation models. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.LG TIER_1 English(EN) · Joschka Birk, Anna Hallin, Gregor Kasieczka, Nikol Madzharova, Ian Pang, David Shih ·

    Enhancing next token prediction based pre-training for jet foundation models

    arXiv:2512.04149v2 Announce Type: replace-cross Abstract: Next token prediction is an attractive pre-training task for jet foundation models, in that it is simulation free and enables excellent generative capabilities that can transfer across datasets. Here we study multiple impr…

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