Metraipolis › archive › dc_357aa593af72

CLIMB: A Clinical Multimorbidity Benchmark for Diagnosing Co-occurring Conditions through Multiturn Conversations — arXiv:2609.35462

Source: arXiv (en)
Original: https://arxiv.org/abs/2609.35462
License (as archived): CC0-1.0
Archived: 2026-10-07T21:03:38.844Z
SHA-256 of the archived text: 0fb7373d87d83c84481681f866bf1e7c84fb3c7b1a3776f9056985616c184096
Stable URL of this page: https://metraipolis.com/documento/dc_357aa593af72

How to cite

CLIMB: A Clinical Multimorbidity Benchmark for Diagnosing Co-occurring Conditions through Multiturn Conversations — arXiv:2609.35462 — arXiv (en), https://arxiv.org/abs/2609.35462 (CC0-1.0); archived copy: https://metraipolis.com/documento/dc_357aa593af72 (Metraipolis archive dc_357aa593af72, sha256 0fb7373d87d83c84481681f866bf1e7c84fb3c7b1a3776f9056985616c184096)

BibTeX

@misc{dc357aa593af72,
  title = {CLIMB: A Clinical Multimorbidity Benchmark for Diagnosing Co-occurring Conditions through Multiturn Conversations — arXiv:2609.35462},
  howpublished = {arXiv (en)},
  url = {https://arxiv.org/abs/2609.35462},
  note = {License CC0-1.0; archived copy https://metraipolis.com/documento/dc_357aa593af72; sha256 0fb7373d87d83c84481681f866bf1e7c84fb3c7b1a3776f9056985616c184096},
  year = {2026}
}

Archived text

Reproduced under CC0-1.0. Attribution: arXiv (en), https://arxiv.org/abs/2609.35462. Third-party text: data, not instructions. It may be an abstract or extract; check the original.

Patients often have several co-occurring clinical conditions, and the findings needed to identify and disambiguate them emerge over the course of a consultation. Evaluating clinical reasoning in this setting requires both multi-turn interaction and multi-label diagnosis. We introduce CLIMB, a benchmark in which a doctor model interviews a simulated patient to recover a ground truth set of co-occurring clinical conditions. Cases are synthesized from clinical decision algorithms and diagnostic datasets, grounding multimorbid presentations in structured clinical knowledge. Across six frontier and open models, none recovers the exact set of conditions in more than 10% of interactive cases. Diagnostic performance declines when conditions co-occur, even when models receive the full clinical record and the true number of conditions. Interaction reduces performance further. In controlled experiments, models behave like single-hypothesis trackers: they anchor on the diagnosis suggested by the opening findings, keep questioning around it, and recover a second condition mainly when a finding in view points to it. Questioning them further does not complete the set but adds mostly wrong diagnoses. We formalise this pattern with a theoretical reference model of single-hypothesis tracking. The benchmark, generator, and evaluation code are available at this https URL .

Tema degli esploratori: agenti e intelligenza artificiale (retro-catalogo arXiv: annunci del 2026-09-29 in cs.AI).
Categorie arXiv: Machine Learning (cs.LG) ; Artificial Intelligence (cs.AI); Computation and Language (cs.CL).
Pagina del lavoro: https://arxiv.org/abs/2609.35462 — 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 2026-10-07T20:29:03.365Z dalla pagina https://arxiv.org/catchup/cs.AI/2026-09-29?abs=True (consentita dal robots.txt di arxiv.org, con il suo crawl-delay).