Pith. sign in

REVIEW 4 major objections 6 minor 30 references

A portable clinical policy plus public biomedical tools lets one unmodified language model diagnose rare diseases at high accuracy without fine-tuning or large case banks.

Reviewed by Pith at T0; open to challenge. T0 means a machine referee read the full paper against a public rubric. the ladder, T0–T4 →

Challenge this review Re-run · record.json Download PDF Read on arXiv ↗

A clinician-audited diagnostic policy plus public tools lets a single unmodified LLM reach high phenotype-first rare-disease Recall@1 and modestly beat baselines on real UDN patients.

T0 review reviewed 2026-07-12 challenge →

load-bearing objection Solid systems paper: external clinician-vetted policy + public tools lifts phenotype-first rare-disease ranking without fine-tunes or case banks; SOTA numbers rest partly on a permissive LLM judge, but the direction of the effect is real. the 4 major comments →

arxiv 2606.16149 v2 pith:7AAHRUSN submitted 2026-06-15 cs.AI

LiteOdyssey: A Lightweight Reasoning AI Agent for Interpretable Rare-Disease Diagnosis

Minh-Ha Nguyen , Erica Gray , Bryce A. Schuler , Chih-Ting Yang , Rizwan Hamid , Lingyao Li , Siyuan Ma , Thomas A. Cassini
show 1 more author
Cathy Shyr
This is my paper
classification cs.AI
keywords rare-disease diagnosislarge language modelspolicy iteration with human feedbackphenotype-first reasoningtool-augmented agentsinterpretable clinical AIUndiagnosed Diseases NetworkHuman Phenotype Ontology
verification ladder T0 review T1 audit T2 compute T3 formal T4 reserved

The pith

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The reading

Rare-disease diagnosis is hard because it demands long chains of phenotype interpretation, evidence gathering, and differential refinement that most AI systems either split into isolated tools or support only by adding heavy infrastructure around the model. This paper asks whether the same end-to-end reasoning can instead live in a lightweight, human-auditable policy that steers a single general-purpose language model and a handful of public biomedical tools. The authors build LiteOdyssey by Policy Iteration with Human Feedback: clinicians inspect the model’s reasoning traces and iteratively revise a natural-language diagnostic policy without ever changing model weights. On two large phenotype-first benchmarks heavy with ultra-rare diseases, and on a private cohort of 515 real Undiagnosed Diseases Network patients, the structured policy produces large, transferable gains over the same models run without tools, while leaving every diagnosis open to step-by-step clinical review. The claim is that expert diagnostic reasoning can be externalized as a reusable policy layer rather than baked into weights or multi-agent machinery.

Core claim

LiteOdyssey shows that a single unmodified reasoning language model, guided by an eight-phase clinical-genetics policy developed through Policy Iteration with Human Feedback and eight public or cached biomedical tools, reaches state-of-the-art phenotype-first disease Recall@1 of 58.6% on LIRICAL and 59.6% on the PhenoPacket Store, with large structured-environment lifts over the identical model without tools, transfer to an open-weights model never used in development, and statistically significant gains on 515 Undiagnosed Diseases Network patients.

What carries the argument

Policy Iteration with Human Feedback (PIHF): a weight-free loop in which a frozen model runs a natural-language diagnostic policy, clinicians review scores and full reasoning traces, and the retained critiques revise the policy itself—producing a portable, inspectable eight-phase workflow that dictates tool use, evidence weighing, and reflective adjudication.

Load-bearing premise

That clinical features encoded as HPO terms plus public curated knowledge tools, scored mainly by a language-model judge, are a fair enough stand-in for real diagnostic performance even when laboratory, biochemical, and genetic-variant evidence are mostly absent and absolute accuracy falls sharply on hard real-world cases.

What would settle it

A prospective bedside trial that supplies the system with incomplete or evolving phenotypes plus laboratory and genetic data, then measures whether physician-adjudicated top-1 and top-5 accuracy still exceeds the same unmodified model without the policy and tools.

Watch this falsifier. Get emailed when new claim-graph text bears on it.

If this is right

  • Diagnostic support for rare disease can be shipped as a readable policy document plus public tools rather than as fine-tuned weights or multi-gigabyte retrieval corpora.
  • The same policy can be executed unchanged across closed and open model families, so improvements travel without re-training.
  • Every differential comes with a step-by-step reasoning trace that a clinician can audit, contest, or revise.
  • Groups without large compute or curated case banks can still field competitive phenotype-first rare-disease AI.
  • PIHF offers a general pattern for turning expert diagnostic practice into portable, weight-free policy artifacts.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

  • If the policy is the true carrier of performance, later work can distill a stabilized PIHF document into a small adapter while keeping the original policy as the human-readable source of truth.
  • The same external-policy pattern could be tried on other multi-step clinical tasks that currently rely on multi-agent orchestration or heavy retrieval, such as oncology staging or complex infectious-disease workups.
  • Because the policy is natural language, institutions could maintain local forks that encode site-specific testing pathways without retraining foundation models.
  • Absolute accuracy remaining low on real undiagnosed cohorts suggests the next binding constraint is richer multimodal inputs rather than further policy refinement alone.
Share X Bluesky LinkedIn Reddit HN

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit.

Referee Report

4 major / 6 minor

Summary. LiteOdyssey is a single-agent, phenotype-first rare-disease diagnostic system that guides an unmodified reasoning LLM with a natural-language eight-phase clinical genetics policy (developed via Policy Iteration with Human Feedback, PIHF) and eight public/cached biomedical tools. On LIRICAL (n=370) and the PhenoPacket Store (n=873), the system reports disease Recall@1 of 58.6% and 59.6%, large lifts over a same-backbone tool-free baseline (e.g., +23.5 and +36.7 points), transfer of the environment gain to Qwen3.6-35B without re-tuning, and statistically significant but smaller gains on 515 UDN patients (R@1 20.4% vs 16.7%, McNemar p=0.027). The authors argue that externalizing diagnostic reasoning as a portable, auditable policy can match or exceed heavier multi-agent/retrieval/fine-tuning systems while remaining deployable and inspectable.

Significance. If the core result holds under stricter, comparator-aligned scoring, the paper is significant for medical AI: it shows that much of rare-disease diagnostic yield can come from organizing inference-time reasoning and public tool use rather than from fine-tuning, multi-agent orchestration, or large solved-case banks. Strengths that should be credited include same-backbone tool ablations, development-excluded public cases, model-level holdout on an open-weights backbone, a private multi-site UDN cohort with paired McNemar tests, gene-level secondary endpoints, exact-OMIM sensitivity preserving direction, and worked reasoning-trace cases that illustrate rescues and regressions. PIHF as a weight-free, clinician-auditable policy iteration process is a useful methodological contribution for settings where model weights cannot be modified.

major comments (4)
  1. [Methods §5.6; Results §3.1; Appendix Figure A1 / Table A1] Methods §5.6 and Results §3.1: Primary disease Recall@1 is defined by an LLM-as-a-judge that can credit synonym/alias agreement, while deterministic exact-OMIM is only a sensitivity analysis. Appendix Figure A1 / Table A1 show material absolute drops under exact-OMIM (e.g., held-out LIRICAL full-system R@1 54.4% vs 57.5% judged). The headline SOTA claim (58.6% LIRICAL; 59.6% PhenoPacket; Figure 1 vs DeepRare 56.0%) is therefore not fully secured at identifier level. Please report exact-OMIM R@1/R@5 as co-primary (or primary) for all main tables/figures, quantify the fraction of judged-only top-1 credits, and restate absolute SOTA claims only where they survive the stricter metric.
  2. [Results §3.1; Figure 1–2] §3.1 comparator framing: DeepRare is the main published reasoning comparator (39.5% without retrieval; 51.6–56.0% with a 67,795-case retrieval corpus that can include benchmark-like cases). The manuscript scores LiteOdyssey with “DeepRare-style” matching but does not show that DeepRare’s published numbers were obtained under the same judge/alias protocol, nor re-score DeepRare outputs on the identical case set and metric. Without matched scoring (and an explicit statement of which DeepRare configuration/backbone is being compared), the absolute “higher still” SOTA claim is not load-bearing. Either re-evaluate under a shared protocol or demote absolute ranking language and emphasize the controlled same-model environment lift.
  3. [Methods §5.3–5.4; §5.8] Methods §5.3–5.4 / §5.8: PIHF and the eight-phase policy are central inventions, but only 50 LIRICAL and 50 UDN cases informed development, the actual policy artifact is not provided (code/demo “URL TBD”), and the iteration loop (how many rounds, which critiques were accepted/rejected, how phase-transition rules changed) is described at a high level. Because portability and auditability are core claims, the final natural-language policy, tool schemas, judge prompt, and a minimal PIHF change log should be released or included as supplementary material so that the weight-free policy—not only the model—can be inspected and re-run.
  4. [Abstract; Results §3.5–3.6; Discussion] §3.5 and Discussion: On the UDN cohort the absolute full-system disease R@1 is 20.4% (+3.7 points over baseline). The gain is statistically significant and directionally consistent on held-out cases, but the abstract and introduction lean heavily on this “external evaluation” as clinical support. Please keep absolute yield, selection for diagnostic difficulty, phenotype-only inputs, and the non-significant gene R@1 (p=0.21) co-equal with the p-values so readers do not over-read clinical readiness from a modest absolute top-1 rate.
minor comments (6)
  1. [Abstract / title] Naming is inconsistent across abstract/title casing (liteOdyssey vs LiteOdyssey).
  2. [Figure 1] Figure 1 deployment-burden axis (0.66 GB vs 100+ GB) needs a short methods note on what is counted (cached indices, tool DBs, model weights excluded/included) so the comparison is reproducible.
  3. [Results §3.2; Figure 2] §3.2 states PhenoPacket parametric baseline 22.9% while Figure 2 panels use subset-specific baselines (e.g., 10.7% unmapped); clarify full-corpus vs subset baselines in one place.
  4. [Appendix A; §3.7] Appendix A worked cases are valuable; consider one additional regression/rescue pair from PhenoPacket or UDN (de-identified) to show the trace behavior outside LIRICAL.
  5. [References; §5.8] References include several 2025–2026 arXiv preprints and a Nature DeepRare citation with 2026 date; verify bibliographic completeness and version pins for reproducibility manifests.
  6. [References] Minor prose issues: missing spaces in concatenated author/affiliation strings in references; “pointprevalenceofrare diseases” etc. in ref [1] rendering.

Circularity Check

0 steps flagged

No circularity: claims are empirical performance of a policy+tools system on external ground-truth diagnoses, with held-out ablations.

full rationale

LiteOdyssey's load-bearing claims are measured diagnostic accuracy (disease/gene Recall@k) of a single unmodified LLM under a natural-language PIHF policy plus public tools, versus the same model without tools, on LIRICAL, PhenoPacket Store, and UDN cases whose ground-truth diagnoses are external (benchmark labels or UDN-established). PIHF revises the policy using scores/traces from only 50 LIRICAL + 50 UDN development cases; the paper reports essentially identical environment lifts on the complementary held-out sets (oos320 LIRICAL, full PhenoPacket, oos465 UDN), on a never-used open-weights backbone, and under exact-OMIM sensitivity. There is no equation or definition that makes the reported Recall equal the development inputs by construction, no fitted numerical parameter re-labeled as a prediction, and no uniqueness theorem or ansatz whose sole support is an overlapping-author citation. Standard train/dev/eval separation plus external ground truth is not circular under the stated criteria.

Axiom & Free-Parameter Ledger

4 free parameters · 5 axioms · 3 invented entities

The central claim rests less on free mathematical constants than on engineering and clinical-domain premises: that an eight-phase natural-language policy plus a fixed public-tool library can stand in for end-to-end diagnostic reasoning; that HPO-encoded phenotypes and curated knowledge bases are adequate inputs for the evaluated task; and that LLM-judge/exact-OMIM scoring on curated and UDN labels measures the intended clinical capability. Free parameters are mostly design choices fixed during PIHF (phase structure, tool set, development-case counts, confidence/routing rules). Invented entities are methodological artifacts (PIHF, the policy document, the tool-mediated agent), not physical objects.

free parameters (4)
  • PIHF development set size = 50 LIRICAL + 50 UDN
    Only 50 LIRICAL and 50 UDN cases informed policy revision; these hand-chosen development budgets shape the final policy and thus measured performance.
  • Eight-phase workflow structure and phase-transition rules = 8 phases (0–7)
    Number of phases, when to re-query, confidence thresholds, and atypical-presentation routing (UDN extended config) are design choices iterated under human feedback rather than derived.
  • Tool library composition and weighting (e.g., HPO information content) = 8 tools over Monarch/OMIM/ClinGen/gnomAD/PubMed/offline variants
    Which eight tools are exposed and how phenotype specificity is weighted affect candidate generation and ranking; composition is author-selected.
  • LLM-as-judge scoring prompt / acceptance criteria = judged R@1 primary; exact-OMIM secondary
    Primary Recall@k depends on a standardized judge prompt that can accept synonym-level matches; this is a free evaluation hyperparameter relative to strict OMIM identity.
axioms (5)
  • domain assumption Public curated biomedical sources (Monarch, OMIM, ClinGen, gnomAD, PubMed) plus offline variant filters supply enough external knowledge for phenotype-first monogenic rare-disease ranking without private solved-case retrieval.
    Core to the lightweight claim and tool library design (§5.2, Discussion).
  • domain assumption HPO term lists (or free-text mapped to HPO) are a sufficient clinical input representation for the evaluated diagnostic task.
    All primary public evaluations are phenotype-first; lab/biochemical/genetic inputs are largely absent (limitations).
  • domain assumption A frozen general-purpose reasoning LLM can faithfully execute a long natural-language clinical policy and integrate tool outputs into a ranked differential.
    Assumed throughout system overview and backbone-swap experiments (§5.1, §3.4).
  • ad hoc to paper LLM-as-judge disease matching, supplemented by exact-OMIM cross-check, is an adequate primary accuracy metric for system comparison.
    Primary endpoint definition in §5.6; sensitivity analyses show lower absolute rates under exact-OMIM but same direction.
  • standard math Standard statistical comparisons (paired exact McNemar tests; Bonferroni on secondary gene endpoints) appropriately support claims of environment lift.
    Used for UDN and held-out significance reporting (§3.3–3.6).
invented entities (3)
  • Policy Iteration with Human Feedback (PIHF) no independent evidence
    purpose: Weight-free iterative procedure that revises a natural-language diagnostic policy using model critiques vetted by clinicians and benchmark traces.
    Named methodological contribution; analogous to RLHF but updates a policy document rather than model weights. Independent evidence is only the empirical system performance under that procedure.
  • LiteOdyssey diagnostic policy (eight-phase workflow artifact) no independent evidence
    purpose: Portable external specification of tool use, evidence weighing, corrective search, and reflective adjudication for rare-disease differentials.
    The reusable object claimed to transfer across model families; existence is definitional to the system, not an external natural kind.
  • LiteOdyssey single-agent tool-mediated diagnostic system independent evidence
    purpose: End-to-end phenotype-to-differential agent combining the policy, tool library, and one reasoning LLM with auditable traces.
    The evaluated system itself; performance claims are the falsifiable handle, but the entity is constructed by the authors.

reviewed 2026-07-12 · how reviews work

0 comments
Cite this review

Pith. "Pith review of LiteOdyssey: A Lightweight Reasoning AI Agent for Interpretable Rare-Disease Diagnosis." pith.science (2026). https://pith.science/paper/7AAHRUSN

@misc{pith2026260616149,
  author       = {Pith},
  title        = {Pith review of: LiteOdyssey: A Lightweight Reasoning AI Agent for Interpretable Rare-Disease Diagnosis},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/7AAHRUSN}},
  note         = {Machine review of arXiv:2606.16149}
}
Share X Bluesky LinkedIn Reddit HN
read the original abstract

Rare disease diagnosis involves interpreting clinical and genetic findings through complex diagnostic reasoning. We investigated whether this reasoning could be translated into a portable policy for guiding general-purpose large language models (LLMs) without modifying model weights or requiring resource-intensive infrastructure. We developed liteOdyssey, a lightweight framework built through Policy Iteration with Human Feedback (PIHF), in which clinicians review the model's reasoning process to iteratively update the policy. This policy guides evidence gathering, tool use, and differential diagnosis generation with an auditable reasoning process. In an external evaluation of 515 Undiagnosed Diseases Network patients, liteOdyssey improved diagnostic accuracy over general-purpose LLMs. These results suggest a strategy for medical AI in which expert reasoning is operationalized as an auditable, reusable policy layer that guides unmodified LLMs without resource-intensive infrastructure.

Figures

Figures reproduced from arXiv: 2606.16149 by Bryce A. Schuler, Cathy Shyr, Chih-Ting Yang, Erica Gray, Lingyao Li, Minh-Ha Nguyen, Rizwan Hamid, Siyuan Ma, Thomas A. Cassini.

Figure 1
Figure 1. Figure 1: Performance versus deployment burden — mean disease Recall@1 on phenotype-first [PITH_FULL_IMAGE:figures/full_fig_p003_1.png] view at source ↗
Figure 2
Figure 2. Figure 2: Structured-environment lift on the public benchmarks — full system versus parametric [PITH_FULL_IMAGE:figures/full_fig_p005_2.png] view at source ↗
Figure 3
Figure 3. Figure 3: Development-excluded robustness — held-out LIRICAL (oos320, public) and held-out [PITH_FULL_IMAGE:figures/full_fig_p007_3.png] view at source ↗
Figure 4
Figure 4. Figure 4: Private UDN validation — 515 UDN cases (GPT-5.3-codex), full system versus parametric [PITH_FULL_IMAGE:figures/full_fig_p008_4.png] view at source ↗
Figure 5
Figure 5. Figure 5: Gene-level prediction where gene truth exists — causal-gene ranking, full system versus [PITH_FULL_IMAGE:figures/full_fig_p009_5.png] view at source ↗

discussion (0)

Sign in with ORCID, Apple, or X to comment. Anyone can read and Pith papers without signing in.

Reference graph

Works this paper leans on

30 extracted references · 9 canonical work pages · 1 internal anchor

  1. [1]

    Lambert, Annie Olry, Charlotte Rodwell, Charlotte Gueydan, ValérieLanneau, DanielMurphy, YannLeCam, andAnaRath

    Stéphanie Nguengang Wakap, Deborah M. Lambert, Annie Olry, Charlotte Rodwell, Charlotte Gueydan, ValérieLanneau, DanielMurphy, YannLeCam, andAnaRath. Estimatingcumulative pointprevalenceofrare diseases: analysisof theOrphanet database.European Journal of Human Genetics, 28(2):165–173, February 2020. ISSN 1476-5438. doi: 10.1038/s41431-019-0508-0. URL http...

    doi:10.1038/s41431-019-0508-0 2020

  2. [2]

    Why rare diseases are an important medical and social issue.The Lancet, 371(9629):2039–2041, June 2008

    Arrigo Schieppati, Jan-Inge Henter, Erica Daina, and Anita Aperia. Why rare diseases are an important medical and social issue.The Lancet, 371(9629):2039–2041, June 2008. ISSN 0140-6736, 1474-547X. doi: 10.1016/S0140-6736(08)60872-7. URL https://www.thelancet.co m/journals/lancet/article/PIIS0140-6736(08)60872-7/abstract

    work page doi:10.1016/s0140-6736(08)60872-7 2039

  3. [3]

    Cassini, Fan Ma, Rizwan Hamid, Lisa Bas- tarache, Josh F

    Minh-Ha Nguyen, Chih-Ting Yang, Thomas A. Cassini, Fan Ma, Rizwan Hamid, Lisa Bas- tarache, Josh F. Peterson, Hua Xu, Lingyao Li, Siyuan Ma, and Cathy Shyr. Diagnostic Accuracy of Large Language Models for Rare Diseases: A Systematic Review and Meta- Analysis.medRxiv, page 2026.03.26.26349194, March 2026. doi: 10.64898/2026.03.26.26349194. URL https://pmc...

    work page doi:10.64898/2026.03.26.26349194 2026

  4. [4]

    The Human Phenotype Ontology: A Tool for Annotating and Analyzing Human Hereditary Disease.The American Journal of Human Genetics, 83(5):610–615, November 2008

    PeterN.Robinson, SebastianKöhler, SebastianBauer, DominikSeelow, DeniseHorn, andStefan Mundlos. The Human Phenotype Ontology: A Tool for Annotating and Analyzing Human Hereditary Disease.The American Journal of Human Genetics, 83(5):610–615, November 2008. ISSN 0002-9297. doi: 10.1016/j.ajhg.2008.09.017. URL http://dx.doi.org/10.1016/j.ajhg.2008.0 9.017

    doi:10.1016/j.ajhg.2008.09.017 2008

  5. [5]

    Xuanzhong Chen, Xiaohao Mao, Qihan Guo, Lun Wang, Shuyang Zhang, and Ting Chen. RareBench: Can LLMs Serve as Rare Diseases Specialists? InProceedings of the 30th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, KDD ’24, pages 4850–4861, New York, NY, USA, August 2024. Association for Computing Machinery. ISBN 979-8-4007- 0490-1. doi: 10.1145/...

    doi:10.1145/3637528.3671576 2024

  6. [6]

    Next-generation diagnostics and disease-gene discovery with the Exomiser.Nature Protocols, 10(12):2004–2015, November 2015

    Damian Smedley, Julius O B Jacobsen, Marten Jäger, Sebastian Köhler, Manuel Holtgrewe, Max Schubach, Enrico Siragusa, Tomasz Zemojtel, Orion J Buske, Nicole L Washington, William P Bone, Melissa A Haendel, and Peter N Robinson. Next-generation diagnostics and disease-gene discovery with the Exomiser.Nature Protocols, 10(12):2004–2015, November 2015. ISSN ...

    work page doi:10.1038/nprot.2015.124 2004

  7. [7]

    An LLM-Driven Multi-Agent Debate System for Mendelian Diseases

    Xinyang Zhou, Yongyong Ren, Qianqian Zhao, Daoyi Huang, Xinbo Wang, Tingting Zhao, Zhixing Zhu, Wenyuan He, Shuyuan Li, Yan Xu, Yu Sun, Yongguo Yu, Shengnan Wu, Jian Wang, Guangjun Yu, Dake He, Bo Ban, and Hui Lu. An LLM-Driven Multi-Agent Debate System for Mendelian Diseases. 2025. doi: 10.48550/ARXIV.2504.07881. URL https: //arxiv.org/abs/2504.07881

    work page internal anchor Pith review Pith/arXiv arXiv doi:10.48550/arxiv.2504.07881 2025

  8. [8]

    LA-MARRVEL: A Knowledge- Grounded, Language-Aware LLM Framework for Clinically Robust Rare Disease Gene Prioriti- zation

    Jaeyeon Lee, Lin Yao, Hyun-Hwan Jeong, and Zhandong Liu. LA-MARRVEL: A Knowledge- Grounded, Language-Aware LLM Framework for Clinically Robust Rare Disease Gene Prioriti- zation. 2025. doi: 10.48550/ARXIV.2511.02263. URL https://arxiv.org/abs/2511.02263. 16

    work page doi:10.48550/arxiv.2511.02263 2025

  9. [9]

    A Specialized Large Language Model for Clinical Reasoning and Diagnosis in Rare Diseases

    Tao Yang, Dandan Huang, Yunting Lin, Pengfei Wu, Zhikun Wu, Gangyuan Ma, Yulan Lu, Xinran Dong, Dingpeng Li, Junshuang Ge, Zhiyan Zhang, Xuanzhao Huang, Wenyan Nong, Yao Zhou, Hui Tang, Hongxi Yang, Shijie Zhang, Juan Li, Xiaojun Cao, Lin Yang, Xia Gao, Kaishou Xu, Xiaoqiong Gu, Wen Zhang, Huimin Xia, Li Liu, Wenhao Zhou, and Mulin Jun Li. A Specialized L...

  10. [10]

    URL https://arxiv.org/abs/2511.14638

    doi: 10.48550/ARXIV.2511.14638. URL https://arxiv.org/abs/2511.14638

    work page doi:10.48550/arxiv.2511.14638

  11. [11]

    An agentic system for rare disease diagnosis with traceable reasoning.Nature, 651 (8106):775–784, February 2026

    Weike Zhao, Chaoyi Wu, Yanjie Fan, Pengcheng Qiu, Xiaoman Zhang, Yuze Sun, Xiao Zhou, Shuju Zhang, Yu Peng, Yanfeng Wang, Xin Sun, Ya Zhang, Yongguo Yu, Kun Sun, and Weidi Xie. An agentic system for rare disease diagnosis with traceable reasoning.Nature, 651 (8106):775–784, February 2026. ISSN 0028-0836. doi: 10.1038/s41586-025-10097-9. URL http://dx.doi....

    doi:10.1038/s41586-025-10097-9 2026

  12. [12]

    RareAgents: Autonomous Multi-disciplinary Team for Rare Disease Diagnosis and Treat- ment

    Xuanzhong Chen, Ye Jin, Xiaohao Mao, Lun Wang, Shuyang Zhang, and Ting Chen. RareAgents: Autonomous Multi-disciplinary Team for Rare Disease Diagnosis and Treat- ment. 2024. doi: 10.48550/ARXIV.2412.12475. URL https://arxiv.org/abs/2412.12475

    doi:10.48550/arxiv.2412.12475 2024

  13. [13]

    MEDDxAgent: A Unified Modular Agent Framework for Explainable Automatic Differential Diagnosis

    Daniel Rose, Chia-Chien Hung, Marco Lepri, Israa Alqassem, Kiril Gashteovski, and Carolin Lawrence. MEDDxAgent: A Unified Modular Agent Framework for Explainable Automatic Differential Diagnosis. 2025. doi: 10.48550/ARXIV.2502.19175. URL https://arxiv.org/abs/25 02.19175

    Pith/arXiv arXiv doi:10.48550/arxiv.2502.19175 2025

  14. [14]

    End-to-End Agentic RAG System Training for Traceable Diagnostic Reasoning

    Qiaoyu Zheng, Yuze Sun, Chaoyi Wu, Weike Zhao, Pengcheng Qiu, Yongguo Yu, Kun Sun, Jian Zhang, Yanfeng Wang, Ya Zhang, and Weidi Xie. End-to-End Agentic RAG System Training for Traceable Diagnostic Reasoning. 2025. doi: 10.48550/ARXIV.2508.15746. URL https://arxiv.org/abs/2508.15746

    doi:10.48550/arxiv.2508.15746 2025

  15. [15]

    MDAgents: An Adaptive Collaboration of LLMs for Medical Decision-Making

    Yubin Kim, Chanwoo Park, Hyewon Jeong, Yik Siu Chan, Xuhai Xu, Daniel McDuff, Hyeonhoon Lee, Marzyeh Ghassemi, Cynthia Breazeal, and Hae Won Park. MDAgents: An Adaptive Collaboration of LLMs for Medical Decision-Making. 2024. doi: 10.48550/ARXIV.2404.15155. URL https://arxiv.org/abs/2404.15155

    Pith/arXiv arXiv doi:10.48550/arxiv.2404.15155 2024

  16. [16]

    Lewis, and Satinder Singh

    Ethan Brooks, Logan Walls, Richard L. Lewis, and Satinder Singh. Large Language Models can Implement Policy Iteration. 2022. doi: 10.48550/ARXIV.2210.03821. URL https: //arxiv.org/abs/2210.03821

    Pith/arXiv arXiv doi:10.48550/arxiv.2210.03821 2022

  17. [17]

    Reflexion: Language Agents with Verbal Reinforcement Learning

    Noah Shinn, Federico Cassano, Edward Berman, Ashwin Gopinath, Karthik Narasimhan, and Shunyu Yao. Reflexion: Language Agents with Verbal Reinforcement Learning. 2023. doi: 10.48550/ARXIV.2303.11366. URL https://arxiv.org/abs/2303.11366

    Pith/arXiv arXiv doi:10.48550/arxiv.2303.11366 2023

  18. [18]

    Prompt Optimization with Human Feedback

    Xiaoqiang Lin, Zhongxiang Dai, Arun Verma, See-Kiong Ng, Patrick Jaillet, and Bryan Kian Hsiang Low. Prompt Optimization with Human Feedback. 2024. doi: 10.48550/ARXIV.2 405.17346. URL https://arxiv.org/abs/2405.17346

    Pith/arXiv arXiv doi:10.48550/arxiv.2 2024

  19. [19]

    Reward Is Enough: LLMs Are In-Context Reinforcement Learners

    Kefan Song, Amir Moeini, Peng Wang, Lei Gong, Rohan Chandra, Shangtong Zhang, and Yanjun Qi. Reward Is Enough: LLMs Are In-Context Reinforcement Learners. 2025. doi: 10.48550/ARXIV.2506.06303. URL https://arxiv.org/abs/2506.06303

    Pith/arXiv arXiv doi:10.48550/arxiv.2506.06303 2025

  20. [20]

    A Survey of In-Context Reinforcement Learning

    Amir Moeini, Jiuqi Wang, Jacob Beck, Ethan Blaser, Shimon Whiteson, Rohan Chandra, and Shangtong Zhang. A Survey of In-Context Reinforcement Learning. 2025. doi: 10.48550/ARX IV.2502.07978. URL https://arxiv.org/abs/2502.07978

    Pith/arXiv arXiv doi:10.48550/arx 2025

  21. [21]

    In-Context Reinforcement Learning for Tool Use in Large Language Models

    Yaoqi Ye, Yiran Zhao, Keyu Duan, Zeyu Zheng, Kenji Kawaguchi, Cihang Xie, and 17 Michael Qizhe Shieh. In-Context Reinforcement Learning for Tool Use in Large Language Models. 2026. doi: 10.48550/ARXIV.2603.08068. URL https://arxiv.org/abs/2603.08068

    doi:10.48550/arxiv.2603.08068 2026

  22. [22]

    Learning by Distilling Context

    Charlie Snell, Dan Klein, and Ruiqi Zhong. Learning by Distilling Context. 2022. doi: 10.48550/ARXIV.2209.15189. URL https://arxiv.org/abs/2209.15189

    Pith/arXiv arXiv doi:10.48550/arxiv.2209.15189 2022

  23. [23]

    On-Policy Context Distillation for Language Models

    Tianzhu Ye, Li Dong, Xun Wu, Shaohan Huang, and Furu Wei. On-Policy Context Distillation for Language Models. 2026. doi: 10.48550/ARXIV.2602.12275. URL https://arxiv.org/abs/26 02.12275

    Pith/arXiv arXiv doi:10.48550/arxiv.2602.12275 2026

  24. [24]

    Doc-to- LoRA: Learning to Instantly Internalize Contexts

    Rujikorn Charakorn, Edoardo Cetin, Shinnosuke Uesaka, and Robert Tjarko Lange. Doc-to- LoRA: Learning to Instantly Internalize Contexts. 2026. doi: 10.48550/ARXIV.2602.15902. URL https://arxiv.org/abs/2602.15902

    doi:10.48550/arxiv.2602.15902 2026

  25. [25]

    Mungall, Julie A

    Christopher J. Mungall, Julie A. McMurry, Sebastian Köhler, James P. Balhoff, Charles Borromeo, Matthew Brush, Seth Carbon, Tom Conlin, Nathan Dunn, Mark Engelstad, Erin Foster, J.P. Gourdine, Julius O.B. Jacobsen, Dan Keith, Bryan Laraway, Suzanna E. Lewis, Jeremy NguyenXuan, Kent Shefchek, Nicole Vasilevsky, Zhou Yuan, Nicole Washington, Harry Hochheise...

    doi:10.1093/nar/gkw1128 2016

  26. [26]

    Amberger, Carol A

    Joanna S. Amberger, Carol A. Bocchini, François Schiettecatte, Alan F. Scott, and Ada Hamosh. OMIM.org: Online Mendelian Inheritance in Man (OMIM®), an online catalog of human genes and genetic disorders.Nucleic Acids Research, 43(D1):D789–D798, November 2014. ISSN 1362-4962. doi: 10.1093/nar/gku1205. URL http://dx.doi.org/10.1093/nar/gku1205

    work page doi:10.1093/nar/gku1205 2014

  27. [27]

    Rehm, Jonathan S

    Heidi L. Rehm, Jonathan S. Berg, Lisa D. Brooks, Carlos D. Bustamante, James P. Evans, Melissa J. Landrum, David H. Ledbetter, Donna R. Maglott, Christa Lese Martin, Robert L. Nussbaum, Sharon E. Plon, Erin M. Ramos, Stephen T. Sherry, and Michael S. Watson. ClinGen — The Clinical Genome Resource.New England Journal of Medicine, 372(23):2235–2242, June

  28. [28]

    doi: 10.1056/nejmsr1406261

    ISSN 0028-4793. doi: 10.1056/nejmsr1406261. URL http://dx.doi.org/10.1056/NEJMsr1 406261

    work page doi:10.1056/nejmsr1406261

  29. [29]

    Karczewski, Laurent C

    Konrad J. Karczewski, Laurent C. Francioli, Grace Tiao, Beryl B. Cummings, Jessica Alföldi, Qingbo Wang, Ryan L. Collins, Kristen M. Laricchia, Andrea Ganna, Daniel P. Birnbaum, Laura D. Gauthier, Harrison Brand, Matthew Solomonson, Nicholas A. Watts, Daniel Rhodes, Moriel Singer-Berk, Eleina M. England, Eleanor G. Seaby, Jack A. Kosmicki, Raymond K. Walt...

    work page doi:10.1038/s41586-020-2308-7 2020

  30. [30]

    re-scored under the stricter deterministic exact-OMIM cross-check. 68.0% 63.3% 60.7% 58.0% 58.6% 57.6% 59.0% 61.2% 82.7% 83.3% 78.7% 72.0% 73.2% 71.1% 74.0% 76.6% Recall@1 Recall@5 PhenoPacket mapped PhenoPacket unmapped LIRICAL Pooled non-MME PhenoPacket mapped PhenoPacket unmapped LIRICAL Pooled non-MME 0% 15% 30% 45% 60% 75% 90%Cases with DeepRare-styl...

This paper was first reviewed by grok-4.5 on July 12, 2026.