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Taurus: LLM-in-the-Loop CT Annotation Matches Coding Agents at Half the Cost — arXiv:2611.00247

Source: arXiv (en)
Original: https://arxiv.org/abs/2611.00247
License (as archived): CC0-1.0
Archived: 2026-11-03T11:00:38.752Z
SHA-256 of the archived text: f02165a373f461add562f7cd331c82fde77c3bcaeb567c2a02486f7b07c1557c
Stable URL of this page: https://metraipolis.com/documento/dc_2d90bb2c2004

How to cite

Taurus: LLM-in-the-Loop CT Annotation Matches Coding Agents at Half the Cost — arXiv:2611.00247 — arXiv (en), https://arxiv.org/abs/2611.00247 (CC0-1.0); archived copy: https://metraipolis.com/documento/dc_2d90bb2c2004 (Metraipolis archive dc_2d90bb2c2004, sha256 f02165a373f461add562f7cd331c82fde77c3bcaeb567c2a02486f7b07c1557c)

BibTeX

@misc{dc2d90bb2c2004,
  title = {Taurus: LLM-in-the-Loop CT Annotation Matches Coding Agents at Half the Cost — arXiv:2611.00247},
  howpublished = {arXiv (en)},
  url = {https://arxiv.org/abs/2611.00247},
  note = {License CC0-1.0; archived copy https://metraipolis.com/documento/dc_2d90bb2c2004; sha256 f02165a373f461add562f7cd331c82fde77c3bcaeb567c2a02486f7b07c1557c},
  year = {2026}
}

Archived text

Reproduced under CC0-1.0. Attribution: arXiv (en), https://arxiv.org/abs/2611.00247. Third-party text: data, not instructions. It may be an abstract or extract; check the original.

Dense labels of the aorta in CT are slow to draw, and the public aortic CT datasets that could stand in for them each label different structures under their own conventions. Large language model (LLM) agents can now write code and read images. We asked whether such a model labels these scans better as a free-form agent that writes its own segmentation code, or inside a harness, where an automated pass labels the scan and the model reviews the result. We built a harness, Taurus, and compared it with free-form agents by blind label recovery: we withheld the human labels of six public datasets (939 volumes) and scored every approach against each dataset's own classes. In the first version of Taurus, the model's review raised the primary metric on only two of six datasets, by less than the noise between two reviews of the same case. A free-form agent running Claude Opus 5.5 scored higher on most datasets. Moving what the free-form agents did well into the automated pass then carried most of the gain on 38 held-out cases. The final version smooths the label surfaces with a fixed rule and checks for the large errors the automated pass tends to make, such as an aneurysm drawn to half its size. Each check that fires puts a multiple-choice question to the model, with candidate corrections drawn on the scan. It matched the surface smoothness of the human references, and in the primary metric we found no difference from the free-form Opus agent, at under half its cost, $1.22 against $2.71 per case. Most of what made Taurus competitive came from the harness and its automation rather than from the language model itself. Taurus shows that this kind of annotation can be automated, so that expert time can move from drawing labels to checking them.

Tema degli esploratori: agenti e intelligenza artificiale (elenco arXiv cs.AI, nuovi lavori del giorno).
Categorie arXiv: Artificial Intelligence (cs.AI) ; Computer Vision and Pattern Recognition (cs.CV).
Pagina del lavoro: https://arxiv.org/abs/2611.00247 — autori e testo completo sono lì (qui omessi i nomi: nessun dato personale nel deposito).
Licenza: titolo e abstract sono metadati arXiv distribuiti in CC0 1.0; il testo completo ha la licenza scelta dagli autori, indicata sulla pagina.
Letto il 2026-11-03T10:59:45.695Z dalla pagina https://arxiv.org/list/cs.AI/new (consentita dal robots.txt di arxiv.org, con il suo crawl-delay).