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pith:GXAQE4FT

pith:2026:GXAQE4FTVUHQKRUZWCHLBFS3GV

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RxEval: A Prescription-Level Benchmark for Evaluating LLM Medication Recommendation

Changmiao Wang, James T. Kwok, Shuhao Chen, Weisen Jiang, Xiaoqing Wu, Xuanren Shi, Yu Zhang

RxEval tests LLMs on specific medication-dose-route choices from detailed patient trajectories, revealing top models reach only 46 percent exact match.

arxiv:2605.14543 v1 · 2026-05-14 · cs.LG · cs.AI

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Claims

C1strongest claim

Evaluation of 16 LLMs shows that RxEval is both challenging and discriminative: F1 ranges from 45.18 to 77.10 across models, and the best Exact Match is only 46.10%.

C2weakest assumption

The distractors generated by reasoning-chain perturbation are clinically valid and representative of the kinds of errors or alternatives that would actually arise in real prescribing decisions.

C3one line summary

RxEval benchmark shows frontier LLMs reach at most 46.10% exact match on prescription-level medication, dose, and route selection from real patient trajectories.

References

54 extracted · 54 resolved · 12 Pith anchors

[1] V ALS MedQA leaderboard. Website. URLhttps://www.vals.ai/benchmarks/medqa

[2] K. Agyeman-Manu, T. A. Ghebreyesus, M. Maait, A. Rafila, L. Tom, N. T. Lima, and D. Wangmo. Prioritising the health and care workforce shortage: Protect, invest, together.The Lancet Global Health, 11 2023

[3] Z. Ali, Y . Huang, I. Ullah, J. Feng, C. Deng, N. Thierry, A. Khan, A. U. Jan, X. Shen, W. Rui, et al. Deep learning for medication recommendation: A systematic survey.Data Intelligence, 5(2):303–354, 2023

[4] Qwen2.5 Technical Report 2024 · arXiv:2412.15115

[5] HealthBench: Evaluating Large Language Models Towards Improved Human Health 2025 · arXiv:2505.08775

Receipt and verification

First computed	2026-05-17T23:39:05.800113Z
Builder	pith-number-builder-2026-05-17-v1
Signature	Pith Ed25519 (`pith-v1-2026-05`) · public key
Schema	pith-number/v1.0

Canonical hash

35c10270b3ad0f054699b08eb0965b357c6e966d1bb2afc294a2acc01aa62e35

Aliases

arxiv: 2605.14543 · arxiv_version: 2605.14543v1 · doi: 10.48550/arxiv.2605.14543 · pith_short_12: GXAQE4FTVUHQ · pith_short_16: GXAQE4FTVUHQKRUZ · pith_short_8: GXAQE4FT

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Verify this Pith Number yourself

curl -sH 'Accept: application/ld+json' https://pith.science/pith/GXAQE4FTVUHQKRUZWCHLBFS3GV \
  | jq -c '.canonical_record' \
  | python3 -c "import sys,json,hashlib; b=json.dumps(json.loads(sys.stdin.read()), sort_keys=True, separators=(',',':'), ensure_ascii=False).encode(); print(hashlib.sha256(b).hexdigest())"
# expect: 35c10270b3ad0f054699b08eb0965b357c6e966d1bb2afc294a2acc01aa62e35

Canonical record JSON

{
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    "abstract_canon_sha256": "f790b35532a934599c0a9b7b83ba6e68f34ef850edfedf75c5903348287b9afa",
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      "cs.AI"
    ],
    "license": "http://creativecommons.org/licenses/by/4.0/",
    "primary_cat": "cs.LG",
    "submitted_at": "2026-05-14T08:24:03Z",
    "title_canon_sha256": "1af664d73bb3e41dd433329289b024f61317693e753b8f656274f4d4b30a9918"
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  "source": {
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}