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

Recognition: unknown

Large Language Models Lack Temporal Awareness of Medical Knowledge

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

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Anil Vullikanti

Authors on Pith no claims yet

Pith reviewed 2026-05-14 20:13 UTC · model grok-4.3

classification 💻 cs.LG cs.CL

keywords temporal awarenessmedical knowledgelarge language modelsevolving guidelinesknowledge cutofftemporal inconsistencymedical benchmarks

0 comments

The pith

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

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

The paper constructs TempoMed-Bench from changing medical guidelines to measure whether LLMs can distinguish current recommendations from outdated ones. It reports that accuracy on up-to-date guidelines declines steadily rather than stopping at a fixed cutoff date, that accuracy on historical guidelines is only 25 to 54 percent of current levels, and that model answers shift unpredictably across consecutive years. These patterns matter because clinical decisions require knowing which guideline version was valid at a given moment, and models without this distinction risk supplying mismatched advice. The same shortfalls persist even when models can query external search tools.

Core claim

LLMs lack temporal awareness of medical knowledge. On TempoMed-Bench, performance on up-to-date recommendations falls in a gradual linear pattern over successive years instead of exhibiting a sharp knowledge cutoff, recall accuracy for historical guideline versions reaches only 25.37 to 53.89 percent of current-version accuracy, and model outputs fluctuate irregularly when the same question is posed for neighboring years.

What carries the argument

TempoMed-Bench, a benchmark built from evolving medical guidelines that supplies time-labeled questions to test recall, consistency, and knowledge cutoff behavior.

If this is right

Models may output recommendations that are valid only for the wrong time window without any internal signal of mismatch.
Standard training leads to partial forgetting of earlier guideline versions rather than clean retention of all historical states.
Atemporal medical benchmarks miss the consistency failures that appear when questions are posed across successive years.
Adding agentic search improves results only modestly and leaves residual temporal inconsistency intact.

Where Pith is reading between the lines

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

The same gradual-fade and inconsistency pattern may appear in any domain where facts carry validity dates, such as legal statutes or scientific consensus.
Training pipelines could add explicit time-stamping or versioning layers to reduce the observed forgetting of historical states.
Medical deployment pipelines should treat parametric outputs as provisional and require external verification against current sources.

Load-bearing premise

The chosen evolving guidelines represent typical temporal shifts in medical knowledge and model answers reflect internal parametric storage rather than prompt or retrieval effects.

What would settle it

Retrain one of the tested models on the same guideline corpus but with explicit year tags attached to each fact, then re-run TempoMed-Bench to check whether the gradual decline and year-to-year inconsistency both disappear.

Figures

Figures reproduced from arXiv: 2605.13045 by Anil Vullikanti, Fangyuan Chen, Guangzhi Xiong, Mengxuan Hu, Qiao Jin, Qingyu Chen, Yifan Peng, Zhiyong Lu, Zihan Guan.

**Figure 1.** Figure 1: An example of the evolving medical knowledge. To address this limitation, we study a crucial yet underexplored problem through the lens of temporal awareness: the ability of LLMs to correctly recall and align medical knowledge with a specified point in time. Temporal awareness is strongly grounded in real-world clinical practice: Much of medical data is inherently historical and longitudinal [11, 9],… view at source ↗

**Figure 3.** Figure 3: Extracting differences from a pair of up-to-date [PITH_FULL_IMAGE:figures/full_fig_p004_3.png] view at source ↗

**Figure 5.** Figure 5: Statistical analysis of the MCQs in the TempoMed-Bench. Distribution of (a) up-to-date [PITH_FULL_IMAGE:figures/full_fig_p005_5.png] view at source ↗

**Figure 4.** Figure 4: Constructing MCQs from extracted differences. [PITH_FULL_IMAGE:figures/full_fig_p005_4.png] view at source ↗

**Figure 6.** Figure 6: Average accuracy by the year of the up-to-date guideline. Points denote mean accuracy, [PITH_FULL_IMAGE:figures/full_fig_p007_6.png] view at source ↗

**Figure 7.** Figure 7: Accuracy comparison between up-to-date knowledge and historical knowledge. The error [PITH_FULL_IMAGE:figures/full_fig_p007_7.png] view at source ↗

**Figure 9.** Figure 9: Statistical analysis of the guideline trajectories in the TempoMed-Traj. Distribution of [PITH_FULL_IMAGE:figures/full_fig_p015_9.png] view at source ↗

**Figure 10.** Figure 10: Statistical analysis of the guideline trajectories in the TempoMed-Traj. Distribution of the [PITH_FULL_IMAGE:figures/full_fig_p016_10.png] view at source ↗

**Figure 11.** Figure 11: Statistical analysis of the guideline trajectories in the TempoMed-Traj. Distribution of the [PITH_FULL_IMAGE:figures/full_fig_p016_11.png] view at source ↗

**Figure 12.** Figure 12: A typical example of the guideline trajectory. [PITH_FULL_IMAGE:figures/full_fig_p016_12.png] view at source ↗

**Figure 13.** Figure 13: More results on average accuracy by the year of the up-to-date guideline. [PITH_FULL_IMAGE:figures/full_fig_p028_13.png] view at source ↗

**Figure 14.** Figure 14: More results on accuracy comparison between up-to-date-targeted and historical [PITH_FULL_IMAGE:figures/full_fig_p028_14.png] view at source ↗

**Figure 15.** Figure 15: Only-Know-Current Pattern. 29 [PITH_FULL_IMAGE:figures/full_fig_p029_15.png] view at source ↗

**Figure 16.** Figure 16: All-No Pattern. 30 [PITH_FULL_IMAGE:figures/full_fig_p030_16.png] view at source ↗

**Figure 17.** Figure 17: All-Yes Pattern. 31 [PITH_FULL_IMAGE:figures/full_fig_p031_17.png] view at source ↗

**Figure 18.** Figure 18: Wrong-Transition-Point Pattern. 32 [PITH_FULL_IMAGE:figures/full_fig_p032_18.png] view at source ↗

read the original abstract

The existing methods for evaluating the medical knowledge of Large Language Models (LLMs) are largely based on atemporal examination-style benchmarks, while in reality, medical knowledge is inherently dynamic and continuously evolves as new evidence emerges and treatments are approved. Consequently, evaluating medical knowledge without a temporal context may provide an incomplete assessment of whether LLMs can accurately reason about time-specific medical knowledge. Moreover, most medical data are historical, requiring the models not only to recall the correct knowledge, but also to know when that knowledge is correct. To bridge the gap, we built TempoMed-Bench, the first-of-its-kind benchmark for evaluating the temporal awareness of the LLMs in the medical domain through evolving guideline knowledge. Based on the TempoMed-Bench, our evaluation analysis first reveals that LLMs lack temporal awareness in medical knowledge through the key findings: (1) model performance on up-to-date medical knowledge exhibits a gradual linear decline over time rather than a sharp knowledge-cutoff behavior, suggesting that parametric medical knowledge is not strictly bounded by knowledge cutoffs; (2) LLMs consistently struggle more with recalling outdated historical medical knowledge than with up-to-date recommendations: accuracy of historical knowledge is only 25.37%-53.89% of up-to-date knowledge, indicating potential knowledge forgetting effects during training; and (3) LLMs often exhibit temporally inconsistent behaviors, where predictions fluctuate irregularly across neighboring years. We also show that the temporal awareness problem is a challenge that cannot be easily solved when integrated with agentic search tools (-3.15%-14.14%). This work highlights an important yet underexplored challenge and motivates future research on developing LLMs that can better encode time-specific medical knowledge.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Desk Editor's Note private letter to a colleague

TempoMed-Bench shows LLMs track medical guideline changes poorly, with gradual drops and weak historical recall, but the results may partly reflect training data imbalances rather than pure temporal encoding.

read the letter

The paper builds TempoMed-Bench from real changes in medical guidelines and tests how well LLMs handle time-specific knowledge. It reports that accuracy on current recommendations falls off gradually instead of stopping at a cutoff, that models get historical versions right only 25-54% as often as the latest ones, and that answers flip inconsistently across nearby years. Search tools close only a small part of the gap. This is the first benchmark that directly uses guideline evolution to probe temporal awareness, which moves past the usual static medical QA sets and gives concrete numbers across several models. The patterns are reported clearly and the work is upfront that search does not solve the issue. The main soft spot is whether the accuracy gaps come from missing time parameters or from simpler factors like older guideline questions appearing less often in training data, using different phrasing, or being harder to parse. Without explicit checks for lexical difficulty, question balance, or contamination, the drop could trace to data distribution rather than forgetting. The gradual decline might also follow from how guidelines are worded across versions. Those controls would strengthen the claim that the results reflect internal temporal state. The work is still useful for anyone building medical LLMs or studying how models encode changing facts. A reader focused on deployment reliability would take the benchmark and the reported trends seriously. I would send it for peer review so the full methods and any added controls can be checked.

Referee Report

3 major / 2 minor

Summary. The paper introduces TempoMed-Bench, the first benchmark for temporal awareness in medical LLMs constructed from evolving clinical guidelines. It reports three main empirical findings: (1) up-to-date knowledge performance declines gradually and linearly rather than exhibiting a sharp cutoff; (2) historical guideline accuracy is only 25.37–53.89 % of current accuracy, interpreted as evidence of forgetting; and (3) models display temporally inconsistent predictions across neighboring years. Experiments with agentic search tools show only modest gains (-3.15 % to 14.14 %), leading to the conclusion that temporal awareness cannot be easily mitigated by retrieval.

Significance. If the benchmark isolates parametric temporal encoding from data-distribution and difficulty confounds, the results would be significant for medical AI, where outdated recommendations carry direct clinical risk. The work supplies concrete cross-model quantitative measurements against an external, time-stamped reference and introduces a reusable evolving-domain benchmark, both of which are valuable contributions even if further validation is required.

major comments (3)

[§3] §3 (TempoMed-Bench construction): The manuscript does not report controls for question difficulty, lexical complexity, or training-data frequency between historical and current guideline versions. Without such controls (e.g., expert difficulty ratings or n-gram overlap statistics), the reported accuracy gap of 25.37–53.89 % could arise from general medical-reasoning difficulty rather than absence of temporal parameters.
[§5.3] §5.3 (search-tool ablation): The ablation reports performance deltas of -3.15 % to 14.14 % but provides no details on whether the retrieval pipeline blocks direct leakage of the guideline texts used to build TempoMed-Bench or standardizes query phrasing. This information is load-bearing for the claim that external tools cannot solve the temporal-awareness problem.
[§4] §4 (results): The assertion of a 'gradual linear decline' is presented without statistical support (e.g., linear-regression R², comparison against a step-function cutoff model, or confidence intervals on the slope). Adding these tests would strengthen the distinction from knowledge-cutoff behavior.

minor comments (2)

[Abstract] Abstract: The accuracy range 25.37–53.89 % should be accompanied by the specific models that achieve the lower and upper bounds.
[Figures] Figure captions: Ensure that time axes and accuracy metrics are labeled consistently with the text (e.g., year ranges and exact evaluation protocol).

Simulated Author's Rebuttal

3 responses · 0 unresolved

We thank the referee for their constructive comments, which help strengthen the presentation of our results on temporal awareness in medical LLMs. We address each major comment below and commit to revisions that improve clarity and rigor without altering the core findings.

read point-by-point responses

Referee: [§3] §3 (TempoMed-Bench construction): The manuscript does not report controls for question difficulty, lexical complexity, or training-data frequency between historical and current guideline versions. Without such controls (e.g., expert difficulty ratings or n-gram overlap statistics), the reported accuracy gap of 25.37–53.89 % could arise from general medical-reasoning difficulty rather than absence of temporal parameters.

Authors: We agree that explicit controls would better isolate temporal effects. Although questions are derived from paired versions of the same guideline documents (ensuring matched clinical topics), we did not previously quantify overlap or difficulty. In the revised manuscript we will report n-gram overlap statistics between historical and current question sets and include expert-rated difficulty scores for a representative sample to confirm that the accuracy gap is not driven by general reasoning difficulty. revision: yes
Referee: [§5.3] §5.3 (search-tool ablation): The ablation reports performance deltas of -3.15 % to 14.14 % but provides no details on whether the retrieval pipeline blocks direct leakage of the guideline texts used to build TempoMed-Bench or standardizes query phrasing. This information is load-bearing for the claim that external tools cannot solve the temporal-awareness problem.

Authors: We appreciate this observation. The retrieval experiments used paraphrased queries and an index that excluded the TempoMed-Bench source documents to prevent direct leakage. In the revision we will add a full description of the pipeline, including query standardization steps and explicit confirmation that benchmark guideline texts were not retrievable, to support the conclusion that agentic search yields only modest gains. revision: yes
Referee: [§4] §4 (results): The assertion of a 'gradual linear decline' is presented without statistical support (e.g., linear-regression R², comparison against a step-function cutoff model, or confidence intervals on the slope). Adding these tests would strengthen the distinction from knowledge-cutoff behavior.

Authors: We acknowledge the need for statistical validation. The revised manuscript will include linear regression fits with reported R² values, 95% confidence intervals on the slopes, and a model comparison (linear vs. step-function) to demonstrate that the gradual decline is statistically preferred over a sharp cutoff, thereby reinforcing the distinction from conventional knowledge-cutoff behavior. revision: yes

Circularity Check

0 steps flagged

No circularity: direct empirical measurements on external guideline benchmark

full rationale

The paper's claims rest on constructing TempoMed-Bench from independently evolving medical guidelines and reporting measured LLM accuracies (gradual linear decline, historical accuracy 25.37-53.89% of current, temporal inconsistencies). These are observational results from benchmark evaluation, not reductions of predictions to fitted inputs, self-definitions, or self-citation chains. No equations, ansatzes, or uniqueness theorems are invoked that collapse back to the paper's own data or prior author work. The search-tool ablation is likewise an external control. The derivation is self-contained against real-world guideline changes.

Axiom & Free-Parameter Ledger

0 free parameters · 1 axioms · 1 invented entities

The central claim depends on the assumption that guideline evolution provides a valid proxy for temporal medical knowledge and that model responses primarily reflect parametric memory.

axioms (1)

domain assumption Medical guidelines evolve over time in a way that creates clear temporal distinctions usable for benchmarking.
Invoked to define up-to-date versus historical knowledge in TempoMed-Bench.

invented entities (1)

TempoMed-Bench no independent evidence
purpose: Benchmark for evaluating temporal awareness of LLMs using evolving medical guidelines.
Newly introduced dataset without external validation or prior existence mentioned.

pith-pipeline@v0.9.0 · 5633 in / 1181 out tokens · 74027 ms · 2026-05-14T20:13:56.264058+00:00 · methodology

discussion (0)

Reference graph

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Large language models are not robust multiple choice selectors.arXiv preprint arXiv:2309.03882, 2023

Chujie Zheng, Hao Zhou, Fandong Meng, Jie Zhou, and Minlie Huang. Large language models are not robust multiple choice selectors.arXiv preprint arXiv:2309.03882, 2023

work page arXiv 2023
[42]

Evolvebench: A comprehensive benchmark for assessing temporal awareness in llms on evolving knowledge

Zhiyuan Zhu, Yusheng Liao, Zhe Chen, Yuhao Wang, Yunfeng Guan, Yanfeng Wang, and Yu Wang. Evolvebench: A comprehensive benchmark for assessing temporal awareness in llms on evolving knowledge. InProceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 16173–16188, 2025

2025
[43]

Medxpertqa: Benchmarking expert-level medical reasoning and understanding.arXiv preprint arXiv:2501.18362, 2025

Yuxin Zuo, Shang Qu, Yifei Li, Zhangren Chen, Xuekai Zhu, Ermo Hua, Kaiyan Zhang, Ning Ding, and Bowen Zhou. Medxpertqa: Benchmarking expert-level medical reasoning and understanding.arXiv preprint arXiv:2501.18362, 2025. 13 Appendix Contents A Data Collection Pipeline. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 15 A...

work page arXiv 2025
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- If no, do not record any prior guidelines and set the prior guidelines as an empty list.,→

Determine whether the paper itself is a clinical guideline issued by a professional society (e.g., the American College of Rheumatology).,→ - If yes, proceed with the steps below and extract prior guidelines. - If no, do not record any prior guidelines and set the prior guidelines as an empty list.,→
[45]

Examine the *Introduction*, *Related Works* or *Literature Review* sections to identify documents cited as prior guidelines that **meet the above criteria**;,→
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Record the`year`,`PMID`,`Organization`, and`title`of each previous guideline.,→
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Topic":

Explain why each document is identified as a prior guideline that precedes the current one (e.g., explicit citation, chronological ordering, or stated replacement). ,→ ,→ ## Important Notes: - If multiple prior guidelines exist, include all of them. 17 - If a field is not available, return`None`for that field. For example, if the old guideline is from the...

2025
[48]

- The corresponding recommendation in the PRIOR guideline

Please Extract: - The key clinical recommendation in the CURRENT guideline. - The corresponding recommendation in the PRIOR guideline
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For each extracted change, the topic must be **identical** between the prior and current recommendations (i.e., a strict head-to-head comparison).,→ - For example, the prior recommendation and the current recommendation are different treatments based on the **SAME disease**,→
[50]

,→ ,→ - Each pair of`current_recommendation`and`prior_recommendation`must represent a direct **head-to-head comparison** and be **meaningfully different**

Focus only on recommendations that have changed substantially, for example: - The prior guideline recommends therapy A for disease C, whereas the current guideline recommends therapy B for the **SAME disease** C based on new clinical evidence. ,→ ,→ - Each pair of`current_recommendation`and`prior_recommendation`must represent a direct **head-to-head compa...
[51]

**MUST exclude minor or incremental updates**, including but not limited to: - The prior guideline lacked specific guidance, and the current guideline merely adds clarification or detail.,→ - Both guidelines recommend the same intervention, with the current guideline only expanding on context, rationale, or implementation details.,→
[52]

If the prior and current guidelines address different clinical topics, return an empty list.,→
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Do not use vague words and try to be more specific and detailed

Each generated`current_recommendation`and`prior_recommendation`must be self-contained, precise, and clinically interpretable, explicitly stating any necessary background conditions or patient populations. Do not use vague words and try to be more specific and detailed. ,→ ,→ ,→
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DO NOT fabricate any new clinical terms or conditions

Your recommendation text in`current_recommendation`and`prior_recommendation` MUST be faithful to the corresponding raw texts. DO NOT fabricate any new clinical terms or conditions. ,→ ,→
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complicated diverticulitis

Identify as many differences as possible. Listing 3: Prompt for extracting differences from the guideline trajectory. ### Assignment You are evaluating whether the **prior** and **current** clinical guideline recommendations shown below exhibit a **strict head-to-head change**.,→ Your task is to determine whether the current recommendation truly replaces,...

2023
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Test APPLICATION of knowledge, not recall of isolated facts
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Use a clinical vignette structured as: - patient demographics - chief complaint - relevant history - physical examination - laboratory or imaging findings
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Write a CLOSED and FOCUSED lead-in question. Acceptable lead-ins include: - Which of the following is the most appropriate next step in management? - Which of the following is recommended according to the guideline? - Which of the following interventions should be initiated? Avoid vague lead-ins such as: - Which statement is true? - What is associated wit...
[59]

Rules for options: - Only ONE option is the best answer

Create EXACTLY FOUR answer options (A–D). Rules for options: - Only ONE option is the best answer. - All options must be HOMOGENEOUS (same category such as treatments, tests, or diagnoses).,→ - Distractors must be medically plausible. - Options must have similar length and grammatical structure
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- Avoid vague terms such as'often','usually','frequently'

Avoid technical flaws: - Do NOT use'all of the above'or'none of the above'. - Avoid vague terms such as'often','usually','frequently'. - Avoid absolute terms such as'always'or'never'. - Avoid grammatical cues that reveal the correct answer. - Avoid repeating distinctive words from the vignette in only one option
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Apply the COVER-THE-OPTIONS rule: A knowledgeable reader should be able to infer the correct answer after reading the vignette and lead-in before seeing the options.,→
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clinical_focus

Ensure the question tests clinical reasoning and guideline application. Important: Do not encode the guideline change into the patient scenario unless the guideline explicitly conditions the recommendation on those features.,→ Listing 5: Prompt for generating NBME-style QA questions from the extracted difference. C Examples C.1 Examples of the Extracted G...

work page doi:10.1177/17588359231192388 2024