Prompting Particle Physics: Tokenized Multi-modal Foundation Models for Combinatorially Many Tasks
Reconstruction and simulation at a collider experiment are long chains of specialised algorithms, each tuned to a single step. We explore how one model can serve many of those steps at once, while still producing the intermediate objects (tracks, calorimeter cells, clusters, particles and jets) that make the chain interpretable. To do so, we represent every object in a jet in a single shared token vocabulary and train one model to map any subset of these modalities to any other. A task is then only a choice of which modalities to provide and which to request: particle flow, detector simulation and charged energy subtraction are all directions through the same set of weights. We train over...
| Authors | Nilotpal Kakati, Daniel Murnane, Baran Hashemi et al. |
|---|---|
| Primary category | hep-ph |
| Published | 25 September 2026 |
| Last updated at source | 25 September 2026 |
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