Pith Number

pith:6OWTT67G

pith:2026:6OWTT67GEENK3QTDCHZIF4BL3M

not attested not anchored not stored refs resolved

Large Language Models Lack Temporal Awareness of Medical Knowledge

Anil Vullikanti, Fangyuan Chen, Guangzhi Xiong, Mengxuan Hu, Qiao Jin, Qingyu Chen, Yifan Peng, Zhiyong Lu, Zihan Guan

Large language models lack awareness of when medical knowledge applies in time.

arxiv:2605.13045 v1 · 2026-05-13 · cs.LG · cs.CL

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2 Internet Archive

3 Author claim open · sign in to claim

4 Citations open

5 Replications open

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The bundle contains the canonical record plus signed events. A mirror can host it anywhere and recompute the same current state with the deterministic merge algorithm.

Claims

C1strongest claim

LLMs lack temporal awareness in medical knowledge: performance on up-to-date knowledge declines gradually rather than showing sharp cutoff, historical knowledge accuracy is only 25.37%-53.89% of up-to-date, and models exhibit temporally inconsistent behaviors.

C2weakest assumption

That the selected evolving medical guidelines in TempoMed-Bench are representative of temporal medical knowledge changes and that model outputs reflect internal parametric knowledge rather than prompt or retrieval artifacts.

C3one line summary

LLMs lack temporal awareness of medical knowledge, showing gradual performance decline on up-to-date facts, much lower accuracy on historical knowledge (25-54% relative), and inconsistent year-to-year predictions.

References

62 extracted · 62 resolved · 9 Pith anchors

[1] gpt-oss-120b & gpt-oss-20b Model Card 2025 · arXiv:2508.10925

[2] The distracting effect: Understanding irrelevant passages in rag 2025

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

[4] Jae Hyun Bae, Ji-Hee Haam, Eonju Jeon, Seo Young Kang, SuJin Song, Cheol-Young Park, Hyuktae Kwon, Committee of Clinical Practice Guidelines, et al. 2024 clinical practice guidelines for the diagnosis 2024

[5] Diagnostic accuracy of a large language model in pediatric case studies.JAMA pediatrics, 178(3):313–315, 2024 2024

Formal links

2 machine-checked theorem links

Receipt and verification

First computed	2026-05-18T03:08:59.453598Z
Builder	pith-number-builder-2026-05-17-v1
Signature	Pith Ed25519 (`pith-v1-2026-05`) · public key
Schema	pith-number/v1.0

Canonical hash

f3ad39fbe6211aadc26311f282f02bdb01ceef388619218b4861cf6b57152355

Aliases

arxiv: 2605.13045 · arxiv_version: 2605.13045v1 · doi: 10.48550/arxiv.2605.13045 · pith_short_12: 6OWTT67GEENK · pith_short_16: 6OWTT67GEENK3QTD · pith_short_8: 6OWTT67G

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Resolver JSON Graph JSON Events JSON Schema Signing key

Verify this Pith Number yourself

curl -sH 'Accept: application/ld+json' https://pith.science/pith/6OWTT67GEENK3QTDCHZIF4BL3M \
  | 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: f3ad39fbe6211aadc26311f282f02bdb01ceef388619218b4861cf6b57152355

Canonical record JSON

{
  "metadata": {
    "abstract_canon_sha256": "c1118bfb2465e9a059dac4b7aaa921c2ade30a82eda494c702fb624d3f7bcca1",
    "cross_cats_sorted": [
      "cs.CL"
    ],
    "license": "http://creativecommons.org/licenses/by/4.0/",
    "primary_cat": "cs.LG",
    "submitted_at": "2026-05-13T06:04:40Z",
    "title_canon_sha256": "38c4a622fdb66904cc042f5cf4bff2c4478828d6af137e30665ecca493009f48"
  },
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  "source": {
    "id": "2605.13045",
    "kind": "arxiv",
    "version": 1
  }
}