{
  "abstract": "We present the Reconstruct Everything Model (REM), an end-to-end multitask model for sparse, multimodal sensor data, demonstrated in high-energy physics. To our knowledge, REM is the first demonstration of a single learned framework simultaneously reconstructing multiple particle types directly from hit-level measurements across the silicon tracker, calorimeters, and muon system in a full simulation. It performs hit assignment, particle identification, kinematic regression, and resonance reconstruction without externally reconstructed tracks or calorimeter clusters as inputs. REM improves particle-reconstruction efficiency and purity and jet-energy resolution relative to both conventional and machine-learned particle-flow approaches. In diboson events, the learned resonance reconstruction improves W/Z mass separation relative to conventional jet reconstruction. These results demonstrate a reconstruction chain from detector hits to particles to resonances, with potential applications to structured prediction in other large-scale, sparse, multimodal sensor systems.",
  "abstract_shown": true,
  "affiliations": [],
  "article_type": null,
  "arxiv": "2610.41889",
  "author_total": 6,
  "authors": [
    {
      "name": "Max Hart"
    },
    {
      "name": "Kwok Yiu Wong"
    },
    {
      "name": "Samuel Van Stroud"
    },
    {
      "name": "Philippa Duckett"
    },
    {
      "name": "Tim Scanlon"
    },
    {
      "name": "Gabriel Facini"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/bhkhry0vva6xa.bib",
  "cited_by": 0,
  "doi": "10.48550/arxiv.2610.41889",
  "fulltext_url": null,
  "id": "bhkhry0vva6xa",
  "issns": [],
  "landing_url": "https://arxiv.org/abs/2610.41889",
  "language": null,
  "licence": "CC-BY-4.0",
  "licence_label": "CC BY 4.0",
  "licence_source": "article",
  "licence_url": "http://creativecommons.org/licenses/by/4.0/",
  "metadata_licence": "CC0-1.0",
  "native_subjects": [
    [
      "arxiv",
      "hep-ex"
    ]
  ],
  "openalex": null,
  "pdf_url": "https://arxiv.org/pdf/2610.41889",
  "pmcid": null,
  "pmid": null,
  "published": "2026-10-30",
  "publisher": null,
  "ris": "https://research.draconitic.com/paper/bhkhry0vva6xa.ris",
  "sources": [
    {
      "first_seen": "2026-11-02T12:21:15Z",
      "native_id": "2610.41889",
      "source": "arxiv",
      "url": "https://arxiv.org/abs/2610.41889"
    }
  ],
  "subjects": [
    {
      "field": "physics",
      "origin": "arxiv:hep-ex",
      "subfield": "high-energy"
    }
  ],
  "title": "Reconstruct everything everywhere all at once: An end-to-end model for future colliders",
  "title_text": "Reconstruct everything everywhere all at once: An end-to-end model for future colliders",
  "type": "preprint",
  "updated_at": "2026-11-04T15:20:51Z",
  "url": "https://research.draconitic.com/paper/bhkhry0vva6xa",
  "venue": "arXiv",
  "versions": []
}