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Foundation models

Section status

Contributor: Unclaimed — this section has no named author yet. See Contribute.

Last reviewed: —

Status: Stub — a starting point only. The curated list is not yet written.

Overview

A foundation model is pre-trained on a large unlabelled corpus and then adapted to many downstream tasks. The hope in particle physics is that the vast quantity of unlabelled collider data — real and simulated — yields representations that transfer, so individual analyses need less labeled data and less bespoke architecture engineering. This is the newest and least settled area in the Guide, and it sits in direct tension with the equivariant-architecture program: one says build the physics in, the other says learn it from scale. Both have empirical support, and which wins where is genuinely open rather than rhetorical.

  • Building an AI-native Research Ecosystem for Experimental Particle Physics: A Community Vision, Aarrestad et al. (2026) (arXiv:2602.17582) — on building an AI-native research ecosystem for experimental particle physics
  • Les Houches guide to reusable ML models in LHC analyses, Araz et al. (2023) (arXiv:2312.14575) — the Les Houches guide to reusable models, the practical prerequisite for any of this

Curated paper list

This list is a seed, not a curated selection

A few landmark papers are listed to give the section a starting shape. A proper curated list — thematically grouped, with an annotation on every entry — is what this section still needs. See Contribute.

  • OmniJet-alpha: The first cross-task foundation model for particle physics, Birk et al. (2024) (arXiv:2403.05618) — OmniJet-alpha, the first model in the field to demonstrate transfer across genuinely different tasks
  • OmniLearn: A Method to Simultaneously Facilitate All Jet Physics Tasks, Mikuni et al. (2024) (arXiv:2404.16091) — OmniLearn, a complementary approach to multi-task jet physics
  • Aspen Open Jets: Unlocking LHC Data for Foundation Models in Particle Physics, (2024) (arXiv:2412.10504) — Aspen Open Jets: large-scale open collider data assembled specifically for pre-training

Benchmarks, datasets & software

Not yet compiled for this section.

Open questions

What is settled here, what is contested, and what remains unsolved? This is the part a bibliography structurally cannot provide, and often the most useful paragraph on the page.

Further reading

Relevant work that is not an entry point — too specialized, too recent, or simply not where a newcomer should start. Suggestions that do not fit the curated list above belong here rather than being turned away.

Nothing listed yet.

Cross-references