{
  "abstract": "Patients with rare diseases often wait years for a diagnosis. Existing HPO-based retrieval tools narrow the differential to a shortlist, but a clinician reading that shortlist has no signal for which candidate to trust or what to test next. We present CausalOdyssey, a local four-agent pipeline on Gemma 4 26B built over a classical IC-weighted gene-rescue backbone: a Diagnostician reranks the slate via chain-of-thought over per-patient FCI causal edges and Reactome mechanism context; a Verifier computes deterministic evidence against the top-1 (missing pathognomonic phenotypes, pathway chain closure) and has Gemma issue a confirm/flag/refute verdict over it; an Interrogator selects one follow-up HPO by split-variance scoring; a Narrator emits citation-validated clinical prose. Only the Diagnostician changes the ranking; the other three agents annotate it. On RareBench n=1,122, reranking a top-30 slate lifts R@1 by +12.57 pp over our structural baseline ([+10.07, +14.97]; p=8.3e-24). A no-LLM variant of that baseline, without its inverse disease-count weight, reaches a similar pooled R@1 (36.3% vs 35.3%): the LLM rerank is far better on metabolic cases (RAMEDIS, +16.0 pp) and far worse on case-report corpora (LIRICAL, MME), so its value is corpus-dependent. We also propose a masked-phenotype protocol for evaluating follow-up suggestions without an annotation-based oracle; under it, the Interrogator asks for a patient's hidden phenotypes no more often than simple baselines (0.6% vs 1.2%), although a perfect follow-up would add 11 pp at top-1. The Verifier's verdict separates correct from incorrect top-1s (51.4% vs 12.1% top-1 accuracy under confirm vs refute). For 30% of patients the gold diagnosis is outside our top-30, though LIRICAL retrieves over half of these. All inference is local; no patient data leaves the machine. Demo code and a live demo are public.",
  "abstract_shown": true,
  "affiliations": [],
  "article_type": null,
  "arxiv": "2610.32995",
  "author_total": 1,
  "authors": [
    {
      "name": "Xueer Chen"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/0xv32xcw7s79b.bib",
  "cited_by": 0,
  "doi": "10.48550/arxiv.2610.32995",
  "fulltext_url": null,
  "id": "0xv32xcw7s79b",
  "issns": [],
  "landing_url": "https://arxiv.org/abs/2610.32995",
  "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",
      "q-bio.QM"
    ],
    [
      "openalex",
      "1312|Biomedical Text Mining and Ontologies"
    ]
  ],
  "openalex": "W7223980466",
  "pdf_url": "https://arxiv.org/pdf/2610.32995",
  "pmcid": null,
  "pmid": null,
  "published": "2026-10-04",
  "publisher": null,
  "ris": "https://research.draconitic.com/paper/0xv32xcw7s79b.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:35:44Z",
      "native_id": "2610.32995",
      "source": "arxiv",
      "url": "https://arxiv.org/abs/2610.32995"
    }
  ],
  "subjects": [
    {
      "field": "biology",
      "origin": "arxiv:q-bio.QM",
      "subfield": "bioinformatics"
    }
  ],
  "title": "CausalOdyssey: Multi-Agent Local LLM Reasoning with Per-Patient Causal Structure for Rare Disease Retrieval",
  "title_text": "CausalOdyssey: Multi-Agent Local LLM Reasoning with Per-Patient Causal Structure for Rare Disease Retrieval",
  "type": "preprint",
  "updated_at": "2026-10-30T12:35:44Z",
  "url": "https://research.draconitic.com/paper/0xv32xcw7s79b",
  "venue": "arXiv",
  "versions": []
}