Model and Task-Aware Test-Time Scaling Strategies for Large Language and Vision-Language Models in Medicine: Evaluation Study
Open accessAbstract
Background: Test-time scaling has emerged as a promising method to enhance the reasoning capabilities of large language models (LLMs) and vision-language models (VLMs) during inference without additional training. While foundational studies established scaling paradigms in general domains, their applicability to the unique complexities of medical AI remains underexplored. Objective: This study aims to conduct a comprehensive investigation of test-time scaling in the medical domain. We evaluate the impact of scaling across different model sizes and task complexities. Furthermore, we seek to identify domain-specific bottlenecks and assess model robustness against user-driven perturbations, such as misleading clinical authority. Methods: This study evaluated a diverse set of general and medical-specific LLMs and VLMs. Experiments used five textual medical benchmarks comprising over 5500 questions and two multimodal benchmarks comprising 7000 samples. Performance was measured under three scaling conditions: increasing token budgets, iterative sequential scaling, and parallel scaling. Robustness was tested by embedding misleading hints with varying tones and levels of simulated clinical expertise into prompts. Results: For nonreasoning LLMs, accuracy saturated quickly, with token usage often remaining under 500 tokens regardless of budget increases. Reasoning models demonstrated significant performance gains on complex tasks as token budgets increased. Notably, we identified distinct domain-specific behaviors. First, current VLMs showed a structural bottleneck in integrating visual clues and experienced limited benefit from token expansion. Second, medically fine-tuned LLMs excelled in clinical question answering but exhibited degraded scaling efficiency on calculation tasks compared to general-domain models. This reflects a disparity between qualitative clinical alignment and procedural logic. Third, while optimal scaling improved robustness, models exhibited a cognitive vulnerability by readily abandoning correct reasoning when confronted with misleading expert physician hints. Regarding scaling strategies, parallel scaling outperformed sequential scaling on easier tasks. Conversely, extended sequential scaling or increased budgets proved essential for complex problem-solving. Conclusions: Test-time scaling rules from general domains do not perfectly translate to medical AI. Longer reasoning is not universally beneficial. Concise reasoning with parallel scaling is optimal for simpler tasks. An extended chain of thought via sequential scaling or increased budgets is required for complex problems. Furthermore, safe clinical deployment requires addressing fundamental vision-language alignment, balancing clinical and procedural reasoning, and mitigating vulnerabilities to perceived clinical authority.
AI snapshot
Written from the paper’s full text.
TL;DR
This evaluation study tests test-time scaling for medical LLMs and VLMs across token-budget, sequential, parallel, and hybrid strategies, finding that benefits are conditional on model reasoning capacity and task difficulty. It identifies domain-specific bottlenecks in VLM visual integration, medical fine-tuning versus calculation, and vulnerability to misleading expert physician hints.
Key findings
- Nonreasoning LLMs saturated quickly, often using under 500 tokens regardless of budget increases, while reasoning models improved on complex tasks as token budgets increased; gpt-oss and m1 showed adaptive token use, and m1-32B outperformed larger models on MedXpertQA as the budget increased.
- VLMs showed limited test-time scaling: most did not increase token use, with exceptions including Qwen3-VL variants, MedGemma-27B, and QVQ; gains were smaller than for LLMs and appeared mainly on difficult MedXpertQA, not easy OmniMedVQA.
- Medically fine-tuned LLMs excelled on clinical QA but had lower accuracy on MedCalc-Bench than general-domain models, suggesting a disparity between qualitative clinical alignment and procedural calculation reasoning.
- Parallel scaling outperformed sequential scaling on easier tasks such as PubMedQA, where sequential steps sometimes reduced accuracy; extended sequential scaling or larger token budgets were more effective for complex tasks such as MedXpertQA and MedCalc-Bench. Optimal scaling improved robustness, but misleading expert physician hints still led models to abandon correct reasoning, especially on difficult tasks.
Methods
- Evaluated general and medical-specific LLMs and VLMs on five textual medical benchmarks comprising over 5500 questions (PubMedQA 500, MedQA 1273, MedBullets 308, MedXpertQA text 2450, MedCalc-Bench 1047) and two multimodal benchmarks comprising 7000 samples (OmniMedVQA 5000, MedXpertQA multimodal 2000).
- Tested three scaling conditions: increasing token budgets, iterative sequential scaling with a 512-token initial budget and “wait” revisions, and parallel scaling with shortest majority vote; also tested hybrid sequential-parallel scaling. Accuracy and coverage were measured, with temperature 0 for budget/sequential and 0.7 for parallel.
- Robustness was tested by embedding misleading hints into prompts as another physician’s opinion favoring an incorrect answer, varying tone (hedged vs definitive) and expertise level (novice vs expert) on MedQA, MedBullets, and MedXpertQA.
Limitations
- The paper states that it did not explore trained verifiers for response selection, that VLM evaluation was less comprehensive than for LLMs, that scaling did not fully restore baseline performance under user-driven perturbations, and that only open-source LLMs were tested, excluding proprietary models such as GPT, Claude, and Gemini.
- The paper also states that benchmark datasets are finite samples with inherent statistical uncertainty, and that data contamination cannot be completely ruled out, though it argues the focus on relative scaling gains mitigates this concern.
- Evident from the text: the sequential/parallel scaling and robustness experiments evaluated only two medical reasoning LLMs (HuatuoGPT-o1-7B and m1-7B-1k), which limits generalizability across the broader model set.
Model-written summary. Check it against the paper before relying on it.
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Cites
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Claims the paper makes
15 of 25 cited sources checked · 9 supported · 0 flagged · accuracy not yet measured
- Premisecitation
Test-time scaling has emerged as an effective method for enhancing LLM performance during inference without additional training.
“To address these challenges, test-time scaling has recently emerged as an effective method for enhancing LLM performance during inference [17-20].”
Quote in the paperSubstantial claim2 of 4 sources checked- cites [17] Muennighoff N Yang Z Shi W et al. S1: simple test-time scaling. In: Hajishirzi H , editor. Presented at: Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing; Nov 4-9, 2025; Suzhou, China. [doi: 10.18653/v1/2025.emnlp-main.1025] source
- cites [18] Snell CV Lee J Xu K Kumar A . Scaling LLM test-time compute optimally can be more effective than scaling parameters for reasoning. Presented at: The Thirteenth International Conference on Learning Representations; Apr 24-28, 2025; Singapore EXPO. [doi: 10.18653/v1/2025.emnlp-main] source
- cites [19] Yang W Ma S Lin Y editors. Towards thinking-optimal scaling of test-time compute for LLM reasoning. In: Wei F , editor. Presented at: The Thirty-ninth Annual Conference on Neural Information Processing Systems; Dec 2-7, 2025; San Diego CA. URL: https://proceedings.neurips.cc/paper_files/paper/2025/hash/3e22bea3b170f4c2aebb9c48d98ae64d-Abstract-Conference.html [Accessed 01-07-2026]
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
- cites [20] Zeng Z Cheng Q Yin Z Zhou Y Qiu X . Revisiting the test-time scaling of o1-like models: do they truly possess test-time scaling capabilities? In: Qiu X , editor. Presented at: Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1); Jul 27 to Aug 1, 2025; Vienna, Austria. [doi: 10.18653/v1/2025.acl-long.232] source
- Findingnone given
Medical LLMs generally outperform general domain LLMs on medical QA tasks, except for the recently released Qwen3 variants and gpt-oss.
“Regarding general vs medical models, as expected, medical LLMs generally outperform general domain LLMs on medical QA tasks, except for the recently released Qwen3 variants and gpt-oss.”
Quote in the paperNot a substantial claim- Rests on an assumption: The ground truth labels in the benchmark datasets are correct.
- Findingown data
gpt-oss models achieve higher accuracy on challenging tasks as the token budget expands.
“The gpt-oss models achieve higher accuracy on challenging tasks as the token budget expands.”
Quote in the paperSubstantial claim- Rests on an assumption: The ground truth labels in the benchmark datasets are correct.
- Findingown data
Nonreasoning LLMs' accuracy quickly saturates and does not improve with increased token budgets.
“Consequently, as shown in Figure 4, their accuracy quickly saturates and does not improve with increased token budgets.”
- Also stated in: § Principal Results · plain
“resulting in negligible accuracy gains.”
Quote in the paperSubstantial claim- Rests on an assumption: The ground truth labels in the benchmark datasets are correct.
- Also stated in: § Principal Results · plain
- Findingown data
Test-time scaling through increased token budgets can improve performance for certain reasoning LLMs.
“In summary, test-time scaling through increased token budgets can improve performance for certain reasoning LLMs.”
Quote in the paperSubstantial claim- Rests on an assumption: The ground truth labels in the benchmark datasets are correct.
- Findingnone given
HuatuoGPT-o1 tends to use a relatively small number of tokens during its reasoning process.
“However, despite being categorized as a reasoning model, it tends to use a relatively small number of tokens during its reasoning process.”
Quote in the paperSubstantial claim - Findingown data
Most VLMs do not significantly increase their token usage even when given a higher token budget.
“Consistent with our findings from LLMs, most models do not significantly increase their token usage, even when given a higher token budget.”
Quote in the paperSubstantial claim - Findingnone given
For reasoning models, most show minimal or no improvement on the easier task PubMedQA as the token budget increases.
“For reasoning models, most show minimal or no improvement in performance on the easier task (PubMedQA) as the token budget increases.”
Quote in the paperSubstantial claim- Rests on an assumption: The ground truth labels in the benchmark datasets are correct.
- Findingown data
Nonreasoning LLMs use only a small portion of the available token budget (approximately 500 to 1000 tokens) even when a larger budget is provided.
“LLMs that lack explicit reasoning capabilities typically use only a small portion of the available token budget (approximately 500 to 1000 tokens), even when a larger budget is provided.”
- Also stated in: § Principal Results · plain
“Nonreasoning models (eg, Llama 3 and Qwen2.5) exhibited performance saturation, using fewer than 1000 tokens even when provided with larger budgets”
Quote in the paperSubstantial claim- Rests on an assumption: The ground truth labels in the benchmark datasets are correct.
- Also stated in: § Principal Results · plain
- Findingreasoning
HuatuoGPT-o1 demonstrates limited performance gains from test-time scaling via increased token budgets compared to other reasoning models.
“As a result, it demonstrates limited performance gains from test-time scaling via increased token budgets compared to other reasoning models.”
Quote in the paperSubstantial claim- Rests on an assumption: The ground truth labels in the benchmark datasets are correct.
- Findingnone given
HuatuoGPT-o1 generally exhibits strong performance across all experiments.
“HuatuoGPT-o1 generally exhibits strong performance across all experiments.”
Quote in the paperNot a substantial claim- Rests on an assumption: The ground truth labels in the benchmark datasets are correct.
- Findingown data
On MedXpertQA, m1-32B demonstrates clear test-time scalability, outperforming other models with even larger parameter counts as the token budget increases.
“Notably, on the most challenging QA dataset in our study, MedXpertQA, m1-32B demonstrates clear test-time scalability, outperforming other models with even larger parameter counts as the token budget increases.”
Quote in the paperSubstantial claim- Rests on an assumption: The ground truth labels in the benchmark datasets are correct.
- Findingnone given
Parallel scaling outperformed sequential scaling on easier tasks.
“Regarding scaling strategies, parallel scaling outperformed sequential scaling on easier tasks.”
Quote in the paperSubstantial claim- Rests on an assumption: The medical benchmark datasets used are valid proxies for real-world clinical tasks and reasoning.
- Findingown data
On OmniMedVQA, most models do not show any performance improvement as the token budget increases.
“On OmniMedVQA, which is relatively simple and less demanding in reasoning, most models do not show any performance improvement as the token budget increases.”
Quote in the paperSubstantial claim- Rests on an assumption: The ground truth labels in the benchmark datasets are correct.
- Findingnone given
The effect of increased token budget on performance is more pronounced in models with larger parameter counts.
“This effect is more pronounced in models with larger parameter counts.”
Quote in the paperNot a substantial claim- Rests on an assumption: The ground truth labels in the benchmark datasets are correct.
- Findingreasoning
In MedCalc-Bench, the accuracy of medical models is generally lower than that of general domain models.
“However, in the case of MedCalc-Bench, the accuracy of medical models is generally lower than that of general domain models.”
Quote in the paperSubstantial claim- Rests on an assumption: The ground truth labels in the benchmark datasets are correct.
- Findingown data
Increased usage of reasoning tokens correlates with performance gains for gpt-oss models.
“Crucially, this increased usage of reasoning tokens correlates with performance gains.”
Quote in the paperSubstantial claim- Rests on an assumption: The ground truth labels in the benchmark datasets are correct.
- Findingown data
On simpler benchmarks such as PubMedQA and MedQA, gpt-oss models maintain relatively low token usage even when provided with large budgets.
“On simpler benchmarks such as PubMedQA and MedQA, these models maintain relatively low token usage even when provided with large budgets, suggesting that extensive reasoning is not triggered for easier queries.”
Quote in the paperSubstantial claim - Findingown data
Models that exhibit an upward trend in token usage demonstrate clear performance gains from increased token budgets on MedXpertQA, particularly pronounced in large-scale models with 27B or more parameters.
“Conversely, models that exhibit an upward trend in token usage demonstrate clear performance gains from increased token budgets on MedXpertQA, a correlation that is particularly pronounced in large-scale models with 27B or more parameters.”
Quote in the paperSubstantial claim- Rests on an assumption: The ground truth labels in the benchmark datasets are correct.
- Findingown data
gpt-oss models demonstrate adaptive token usage based on task complexity.
“The gpt-oss models demonstrate adaptive token usage based on task complexity.”
Quote in the paperSubstantial claim - Findingown data
On MedXpertQA, there is a more noticeable, though still limited, test-time scaling effect.
“In contrast, on MedXpertQA, a dataset requiring more complex reasoning, we observe a more noticeable, though still limited, test-time scaling effect.”
Quote in the paperSubstantial claim- Rests on an assumption: The ground truth labels in the benchmark datasets are correct.
- Findingown data
Reasoning models demonstrated significant performance gains on complex tasks as token budgets increased.
“Reasoning models demonstrated significant performance gains on complex tasks as token budgets increased.”
Quote in the paperSubstantial claim- Rests on an assumption: The medical benchmark datasets used are valid proxies for real-world clinical tasks and reasoning.
- Findingown data
As task difficulty increases, gpt-oss models exhibit an upward trend in token consumption, using a large portion of the available budget.
“However, as task difficulty increases, the models exhibit an upward trend in token consumption, using a large portion of the available budget.”
Quote in the paperSubstantial claim - Findingreasoning
Smaller variants of DeepSeek-R1 (7B or 8B) perform worse than other general-domain models of similar size.
“However, the smaller variants of DeepSeek-R1 (7B or 8B) perform worse than other general-domain models of similar size.”
Quote in the paperSubstantial claim- Rests on an assumption: The ground truth labels in the benchmark datasets are correct.
- Findingown data
As task difficulty increases, reasoning models tend to use more reasoning tokens.
“However, as the task difficulty increases (defined in the Methods section), reasoning models tend to use more reasoning tokens.”
Quote in the paperSubstantial claim - Findingown data
Qwen3-VL variants, MedGemma-27B, and QVQ exhibit a distinct upward trend in token usage as the available token budget increases.
“In contrast, models such as the Qwen3-VL variants, MedGemma-27B, and QVQ exhibit a distinct upward trend in token usage as the available token budget increases, indicating their capacity to leverage additional computational resources for extended reasoning.”
Quote in the paperSubstantial claim - Findingown data
For reasoning-capable models, increased token usage is strongly correlated with improved accuracy, particularly on complex tasks.
“This visualization confirms that for reasoning-capable models, increased token usage is strongly correlated with improved accuracy, particularly on complex tasks.”
Quote in the paperSubstantial claim- Rests on an assumption: The ground truth labels in the benchmark datasets are correct.
- Findingreasoning
Consequently, Qwen3-VL models exhibit a distinct test-time scaling effect on the challenging MedXpertQS benchmark as the token budget increases.
“Consequently, these models exhibit a distinct test-time scaling effect on the challenging MedXpertQS benchmark as the token budget increases.”
Quote in the paperSubstantial claim- Rests on an assumption: The ground truth labels in the benchmark datasets are correct.
- Findingown data
In VLMs, the issue is not about optimal token length but rather a structural inability to integrate complex visual clues.
“our evaluation of VLMs shows that the issue is not about optimal length but rather a structural inability to integrate complex visual clues.”
Quote in the paperSubstantial claim - Findingown data
Test-time scaling enhances robustness against user-driven perturbations.
“while test-time scaling enhances robustness against use-driven perturbations”
- Also stated in: § Limitations · plain
“While our results show that test-time scaling can improve robustness to user-driven factors”
Quote in the paperSubstantial claim - Also stated in: § Limitations · plain
- Findingown data
Simple token expansion fails to trigger meaningful multimodal reasoning.
“Simple token expansion fails to trigger meaningful multimodal reasoning”
Quote in the paperSubstantial claim - Findingnone given
Except for certain reasoning models, most models do not use the full available token budget during inference.
“Except for certain reasoning models, most do not use the full available token budget during inference.”
Quote in the paperSubstantial claim - Findingown data
In previous experiments, test-time scaling by simply increasing the token budget is ineffective for many models.
“In previous experiments, we observed that test-time scaling, by simply increasing the token budget, is ineffective for many models.”
Quote in the paperSubstantial claim- Rests on an assumption: The ground truth labels in the benchmark datasets are correct.
- Findingnone given
General nonreasoning models perform well on medical calculation tasks.
“Performs well on medical calculation tasks”
Quote in the paperSubstantial claim - Findingown data
Parallel scaling can be implemented efficiently using batched inference, where model weights are loaded only once.
“parallel scaling can be implemented efficiently using batched inference, where model weights are loaded only once.”
Quote in the paperSubstantial claim - Findingown data
There is a domain-specific disparity in scaling efficiency between qualitative clinical and quantitative procedural logic.
“Second, we identify a domain-specific disparity in scaling efficiency between qualitative clinical and quantitative procedural logic.”
Quote in the paperSubstantial claim - Findingnone given
Medical nonreasoning models perform well on medical question-answering tasks.
“Performs well on medical question-answering tasks”
Quote in the paperSubstantial claim - Findingown data
Our perturbation experiments using user-driven factors demonstrate that models engage in active, dynamic reasoning rather than static memory retrieval.
“our perturbation experiments using user-driven factors demonstrate that models engage in active, dynamic reasoning rather than static memory retrieval.”
Quote in the paperSubstantial claim - Findingown data
The effective test-time scaling strategy varied by model architecture.
“Crucially, the effective strategy varied by model architecture.”
Quote in the paperSubstantial claim - Findingown data
Different test-time scaling strategies exhibit distinct cost-performance trade-offs.
“different test-time scaling strategies exhibit distinct cost-performance trade-offs.”
Quote in the paperSubstantial claim - Findingown data
Overall, test-time scaling by simply increasing the token budget provides limited benefits for most VLMs.
“Overall, our findings from the VLM experiments are consistent with those from the LLM experiments: test-time scaling, by simply increasing the token budget, provides limited benefits for most VLMs.”
Quote in the paperSubstantial claim- Rests on an assumption: The ground truth labels in the benchmark datasets are correct.
- Findingnone given
Medical reasoning models have relatively low performance on reasoning-intensive medical calculation tasks.
“Relatively low performance on reasoning-intensive medical calculation tasks”
Quote in the paperSubstantial claim - Findingown data
The optimal test-time scaling strategy varies depending on both the model type and the difficulty of the task.
“Our findings indicate that the optimal test-time scaling strategy varies depending on both the model type and the difficulty of the task.”
Quote in the paperSubstantial claim - Findingown data
Longer reasoning is not universally beneficial across all medical tasks.
“Our experiments demonstrate that longer reasoning is not universally beneficial across all medical tasks.”
Quote in the paperSubstantial claim - Findingnone given
Medical nonreasoning models have relatively low performance on reasoning-intensive medical calculation tasks.
“Relatively low performance on reasoning-intensive medical calculation tasks”
Quote in the paperSubstantial claim - Findingnone given
The impact of user-driven factors becomes more pronounced as task difficulty increases.
“the impact of these user-driven factors becomes more pronounced as task difficulty increases”
Quote in the paperSubstantial claim - Findingown data
Selecting an appropriate scaling strategy is crucial for enhancing model robustness against user-driven factors, such as misleading or biased information in the prompts.
“we found that selecting an appropriate scaling strategy is crucial for enhancing model robustness against user-driven factors, such as misleading or biased information in the prompts.”
Quote in the paperSubstantial claim - Findingreasoning
Under hierarchical clinical pressure, even optimal scaling strategies fail to restore baseline performance.
“Under this hierarchical clinical pressure, even optimal scaling strategies fail to restore baseline performance”
Quote in the paperSubstantial claim - Findingreasoning
Medical LLMs tend to perform worse on medical calculation tasks compared to general domain LLMs.
“we observed that medical LLMs tend to perform worse on medical calculation tasks compared to general domain LLMs.”
Quote in the paperSubstantial claim - Findingown data
When models are confronted with deceptive hints framed specifically as expert physician opinions, they readily abandon their correct reasoning pathways.
“we demonstrate that when models are confronted with deceptive hints framed specifically as expert physician opinions, they readily abandon their correct reasoning pathways.”
Quote in the paperSubstantial claim - Findingown data
Test-time scaling was not sufficient to fully restore performance to baseline levels observed without user-driven perturbations.
“it was not sufficient to fully restore performance to baseline levels observed without such perturbations.”
Quote in the paperSubstantial claim - Findingown data
HuatuoGPT-o1 benefited most from a hybrid sequential-parallel approach.
“HuatuoGPT-o1 benefited most from a hybrid sequential-parallel approach.”
Quote in the paperSubstantial claim - Interpretationown data
Simply increasing the token budget at test time is unlikely to enhance performance for intrinsic nonreasoning models.
“These observations suggest that simply increasing the token budget at test time is unlikely to enhance performance for intrinsic nonreasoning models.”
Quote in the paperSubstantial claim- Rests on an assumption: The ground truth labels in the benchmark datasets are correct.
- Interpretationown data
An extended chain of thought via sequential scaling or increased budgets is required for complex problems.
“An extended chain of thought via sequential scaling or increased budgets is required for complex problems.”
Quote in the paperNot a substantial claim- Rests on an assumption: The medical benchmark datasets used are valid proxies for real-world clinical tasks and reasoning.
- Interpretationown data
Test-time scaling rules from general domains do not perfectly translate to medical AI.
“Test-time scaling rules from general domains do not perfectly translate to medical AI.”
Quote in the paperSubstantial claim- Rests on an assumption: The medical benchmark datasets used are valid proxies for real-world clinical tasks and reasoning.
- Rests on an assumption: Embedding misleading hints with varying tones and simulated clinical expertise adequately simulates user-driven perturbations such as misleading clinical authority.
- Interpretationown data
Longer reasoning is not universally beneficial.
“Longer reasoning is not universally beneficial.”
Quote in the paperSubstantial claim- Rests on an assumption: The medical benchmark datasets used are valid proxies for real-world clinical tasks and reasoning.
- Interpretationnone given
Concise reasoning with parallel scaling is optimal for simpler tasks.
“Concise reasoning with parallel scaling is optimal for simpler tasks.”
Quote in the paperSubstantial claim- Rests on an assumption: The medical benchmark datasets used are valid proxies for real-world clinical tasks and reasoning.
- Interpretationreasoning
MedCalc-Bench primarily rewards general numerical or procedural reasoning applied within a medical context, rather than qualitative clinical reasoning that centralizes specialized medical training.
“This discrepancy suggests that MedCalc-Bench primarily rewards general numerical or procedural reasoning applied within a medical context, rather than the qualitative clinical reasoning that centralizes specialized medical training.”
Quote in the paperSubstantial claim - Interpretationreasoning
The difference between HuatuoGPT-o1 and m1 is attributed to differences in the nature of their respective training data.
“We attribute the difference between HuatuoGPT-o1 and m1 to differences in the nature of their respective training data.”
Quote in the paperSubstantial claim - Interpretationreasoning
Test-time scaling strategies behave inconsistently depending on whether the clinical task relies on deep domain-specific expertise or general numerical computation.
“Consequently, test-time scaling strategies behave inconsistently depending on whether the clinical task relies on deep domain-specific expertise or general numerical computation, an insight unique to the medical domain.”
Quote in the paperSubstantial claim - Interpretationreasoning
In scenarios where performance differences across models or scaling strategies are marginal, identifying a single optimal model based solely on empirical accuracy may not fully reflect their true generalization ability.
“In scenarios where performance differences across models or scaling strategies are marginal, identifying a single optimal model based solely on empirical accuracy may not fully reflect their true generalization ability.”
Quote in the paperSubstantial claim- Rests on an assumption: The benchmark datasets used for evaluation are finite samples of the broader population of real-world medical questions.
- Interpretationreasoning
If models were merely retrieving memorized answers, increasing the token budget or applying sequential scaling would not yield the significant accuracy improvements we observed.
“If models were merely retrieving memorized answers, increasing the token budget or applying sequential scaling would not yield the significant accuracy improvements we observed.”
Quote in the paperSubstantial claim- The authors state this as an assumption: Public benchmark questions might have been included in the base models' pretraining corpora (data contamination).
- Interpretationown data
Text-centric scaling limits encounter a fundamentally different bottleneck in multimodal clinical environments.
“First, text-centric scaling limits encounter a fundamentally different bottleneck in multimodal clinical environments.”
Quote in the paperSubstantial claim - Interpretationown data
Adoption of advanced posttraining methodologies can significantly reinforce reasoning performance.
“First, the adoption of advanced posttraining methodologies can significantly reinforce reasoning performance.”
Quote in the paperSubstantial claim - Interpretationnone given
Most current VLMs may lack sufficiently developed reasoning capabilities, particularly in leveraging visual cues for complex decision-making.
“First, unlike LLMs, most of the current VLMs may lack sufficiently developed reasoning capabilities, particularly in leveraging visual cues for complex decision-making.”
Quote in the paperSubstantial claim - Interpretationreasoning
Aligning scaling strategies with both the difficulty of the clinical task and the intrinsic reasoning behaviors of the specific model being deployed is necessary.
“This underscores the necessity of aligning scaling strategies with both the difficulty of the clinical task and the intrinsic reasoning behaviors of the specific model being deployed.”
Quote in the paperSubstantial claim - Interpretationreasoning
The disparity in scaling efficiency does not necessarily imply that medical fine-tuning inherently impairs reasoning capabilities.
“This disparity does not necessarily imply that medical fine-tuning inherently impairs reasoning capabilities.”
Quote in the paperSubstantial claim - Interpretationreasoning
Parallel scaling is highly suitable for time-sensitive environments such as emergency departments.
“making it highly suitable for time-sensitive environments such as emergency departments.”
Quote in the paperSubstantial claim - Interpretationown data
Our work uncovers a unique cognitive vulnerability to perceived clinical authority.
“Third, our work uncovers a unique cognitive vulnerability to perceived clinical authority.”
Quote in the paperSubstantial claim - Interpretationreasoning
Safe medical AI deployment requires fundamental vision-language alignment improvements rather than just optimizing computational scaling.
“indicating that safe medical AI deployment requires fundamental vision-language alignment improvements rather than just optimizing computational scaling.”
Quote in the paperNot a substantial claim - Interpretationreasoning
As a result, QVQ achieves higher accuracy on MedXpertQA despite the greater task complexity.
“As a result, QVQ achieves higher accuracy despite the greater task complexity.”
Quote in the paperSubstantial claim- Rests on an assumption: The ground truth labels in the benchmark datasets are correct.
- Interpretationreasoning
The reported accuracy metrics carry inherent statistical uncertainty.
“Consequently, the reported accuracy metrics carry inherent statistical uncertainty.”
Quote in the paperSubstantial claim- Rests on an assumption: The benchmark datasets used for evaluation are finite samples of the broader population of real-world medical questions.
- Interpretationown data
Test-time scaling requires model-specific tailoring.
“it requires model-specific tailoring”
Quote in the paperSubstantial claim - Interpretationown data
Scaling model parameters in conjunction with medical domain training proves beneficial.
“Second, scaling model parameters in conjunction with medical domain training proves beneficial.”
Quote in the paperSubstantial claim - Interpretationreasoning
This vulnerability is distinct and differentiates the work from general-domain robustness findings.
“which represents a distinct vulnerability that differentiates our work from general-domain robustness findings.”
Quote in the paperSubstantial claim - Interpretationown data
In the high-stakes medical domain, the limitations of test-time scaling go beyond mere overthinking and manifest through three unique, domain-specific bottlenecks.
“our study reveals that in the high-stakes medical domain, the limitations of test-time scaling go beyond mere overthinking and manifest through three unique, domain-specific bottlenecks.”
Quote in the paperSubstantial claim
- May combine two claims. 33
- Classified as probably not a substantial claim. 17
- The quote only partly states this claim. 1
May combine two claims.
PremisecitationReasoning traces used to train HuatuoGPT-o1 were generated by GPT-4o, while those used for m1 were synthesized by DeepSeek-R1.
“While the reasoning traces used to train HuatuoGPT-o1 were generated by GPT-4o [6,29], those used for m1 were synthesized by DeepSeek-R1 [9,34].”
Quote in the paperSubstantial claim4 of 4 sources checked- cites [6] Hurst A Lerer A Goucher AP OpenAIet al. GPT-4o system card. arXiv. Preprint posted online on Oct 25, 2024. [doi: 10.48550/arXiv.2410.21276]
- cites [29] Chen J Cai Z Ji K et al. Towards medical complex reasoning with llms through medical verifiable problems. Presented at: Findings of the Association for Computational Linguistics: ACL 2025; Jul 27 to Aug 1, 2025; Vienna, Austria. [doi: 10.18653/v1/2025.findings-acl.751] source
- cites [9] Guo, DeepSeek-R1 incentivizes reasoning in LLMs through reinforcement learning, Nature source
- cites [34] Huang X Wu J Liu H Tang X Zhou Y . M1: unleash the potential of test-time scaling for medical reasoning with large language models. the second workshop on genai for health: potential, trust, and policy compliance. arXiv. Preprint posted online on Apr 1, 2025. [doi: 10.48550/arXiv.2504.00869] source
May combine two claims.
InterpretationreasoningGeneral-domain test-time scaling findings cannot readily predict the multilayered complexities inherent to clinical AI and fail to capture domain-specific bottlenecks and behavioral shifts in medical environments.
“Crucially, while foundational studies have established test-time scaling paradigms in the general domain by focusing primarily on text-based logic such as mathematics and coding [18-20], their findings cannot readily predict the multilayered complexities inherent to clinical AI.”
- Also stated in: § Background · plain
“General-domain frameworks fail to capture the domain-specific bottlenecks and unique behavioral shifts that emerge in medical environments.”
Quote in the paperSubstantial claim1 of 3 sources checked- cites [18] Snell CV Lee J Xu K Kumar A . Scaling LLM test-time compute optimally can be more effective than scaling parameters for reasoning. Presented at: The Thirteenth International Conference on Learning Representations; Apr 24-28, 2025; Singapore EXPO. [doi: 10.18653/v1/2025.emnlp-main] source
- cites [19] Yang W Ma S Lin Y editors. Towards thinking-optimal scaling of test-time compute for LLM reasoning. In: Wei F , editor. Presented at: The Thirty-ninth Annual Conference on Neural Information Processing Systems; Dec 2-7, 2025; San Diego CA. URL: https://proceedings.neurips.cc/paper_files/paper/2025/hash/3e22bea3b170f4c2aebb9c48d98ae64d-Abstract-Conference.html [Accessed 01-07-2026]
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
- cites [20] Zeng Z Cheng Q Yin Z Zhou Y Qiu X . Revisiting the test-time scaling of o1-like models: do they truly possess test-time scaling capabilities? In: Qiu X , editor. Presented at: Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1); Jul 27 to Aug 1, 2025; Vienna, Austria. [doi: 10.18653/v1/2025.acl-long.232] source
The Benchmark section (page not provided) evaluates test-time scaling on three mathematical-reasoning benchmarks and GPQA, a broader science benchmark, supporting the general-domain context. The paper does not assess clinical AI or establish that its findings fail to predict clinical complexities, domain-specific bottlenecks, or behavioral shifts in medical settings. (LLM, conf=0.82)
Experimental — accuracy not yet measured
- Also stated in: § Background · plain
Probably not a substantial claim: may not pass S3 (load-bearing).
PremisecitationThe combination of reasoning models and test-time scaling has shown state-of-the-art results on complex tasks such as mathematics problem solving and program code synthesis.
“The combination of reasoning models and test-time scaling has shown state-of-the-art results on complex tasks such as mathematics problem solving and program code synthesis [6,9].”
Quote in the paperNot a substantial claim2 of 2 sources checked- cites [6] Hurst A Lerer A Goucher AP OpenAIet al. GPT-4o system card. arXiv. Preprint posted online on Oct 25, 2024. [doi: 10.48550/arXiv.2410.21276]
Section 2 (p. 1) says code and math training data help GPT-4o develop reasoning skills, but does not report state-of-the-art results. Searches for test-time scaling, mathematics problem solving, and code synthesis found no passage establishing the claimed combination or its performance; our copy may omit the end of the paper. (LLM, conf=0.68)
Experimental — accuracy not yet measured - cites [9] Guo, DeepSeek-R1 incentivizes reasoning in LLMs through reinforcement learning, Nature source
May combine two claims.
PremisecitationTest-time scaling improves both reliability and accuracy, especially on tasks that require multistep reasoning, without additional training or fine-tuning.
“Without requiring additional training or fine-tuning, test-time scaling improves both reliability and accuracy, especially on tasks that require multistep reasoning.”
Quote in the paperSubstantial claim2 of 4 sources checked- cites [17] Muennighoff N Yang Z Shi W et al. S1: simple test-time scaling. In: Hajishirzi H , editor. Presented at: Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing; Nov 4-9, 2025; Suzhou, China. [doi: 10.18653/v1/2025.emnlp-main.1025] source
The Introduction and §4.2 Performance report that increasing test-time compute improves s1-32B’s results on reasoning benchmarks, including AIME24. Budget forcing is a test-time intervention, but the paper says s1-32B was first fine-tuned on 1,000 examples; it does not establish improved reliability as a separate outcome. Thus the claim is supported for accuracy on reasoning tasks, but not as an unqualified improvement without fine-tuning. (LLM, conf=0.87)
Experimental — accuracy not yet measured - cites [18] Snell CV Lee J Xu K Kumar A . Scaling LLM test-time compute optimally can be more effective than scaling parameters for reasoning. Presented at: The Thirteenth International Conference on Learning Representations; Apr 24-28, 2025; Singapore EXPO. [doi: 10.18653/v1/2025.emnlp-main] source
- cites [19] Yang W Ma S Lin Y editors. Towards thinking-optimal scaling of test-time compute for LLM reasoning. In: Wei F , editor. Presented at: The Thirty-ninth Annual Conference on Neural Information Processing Systems; Dec 2-7, 2025; San Diego CA. URL: https://proceedings.neurips.cc/paper_files/paper/2025/hash/3e22bea3b170f4c2aebb9c48d98ae64d-Abstract-Conference.html [Accessed 01-07-2026]
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
- cites [20] Zeng Z Cheng Q Yin Z Zhou Y Qiu X . Revisiting the test-time scaling of o1-like models: do they truly possess test-time scaling capabilities? In: Qiu X , editor. Presented at: Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1); Jul 27 to Aug 1, 2025; Vienna, Austria. [doi: 10.18653/v1/2025.acl-long.232] source
The Introduction and Section 5 (page numbers unavailable) report that parallel test-time scaling improves performance and coverage on reasoning benchmarks. But the paper does not establish an improvement in reliability, and it finds that longer reasoning chains and sequential self-revision do not consistently improve accuracy. The claim overgeneralizes the benefit to test-time scaling as a whole. (LLM, conf=0.85)
Experimental — accuracy not yet measured
Classified as probably not a substantial claim.
PremisecitationRecent studies demonstrate that the use of reasoning models and test-time scaling significantly boosts performance on medical benchmark datasets.
“Recent studies demonstrate that the use of reasoning models and test-time scaling significantly boosts performance on medical benchmark datasets [30].”
Quote in the paperSubstantial claim1 of 1 sources checked- cites [30] Jiang S Liao Y Chen Z Zhang Y Wang Y Wang Y . MedS: towards medical slow thinking with self-evolved soft dual-sided process supervision. In: Wang Y , editor. Presented at: The Fortieth AAAI Conference on Artificial Intelligence (AAAI-26); Feb 25 to Mar 4, 2025:31319-31327; Philadelphia,PA. [doi: 10.1609/aaai.v40i37.40395] source
Probably not a substantial claim: may not pass S3 (load-bearing). May combine two claims.
PremisecitationPrior studies on test-time scaling in general domains have established that increasing inference compute can improve reasoning, though the relationship is not strictly monotonic.
“Prior studies on test-time scaling in general domains, such as mathematics and coding, have established that increasing inference compute can improve reasoning, though the relationship is not strictly monotonic.”
Quote in the paperNot a substantial claim1 of 3 sources checked- cites [18] Snell CV Lee J Xu K Kumar A . Scaling LLM test-time compute optimally can be more effective than scaling parameters for reasoning. Presented at: The Thirteenth International Conference on Learning Representations; Apr 24-28, 2025; Singapore EXPO. [doi: 10.18653/v1/2025.emnlp-main] source
- cites [19] Yang W Ma S Lin Y editors. Towards thinking-optimal scaling of test-time compute for LLM reasoning. In: Wei F , editor. Presented at: The Thirty-ninth Annual Conference on Neural Information Processing Systems; Dec 2-7, 2025; San Diego CA. URL: https://proceedings.neurips.cc/paper_files/paper/2025/hash/3e22bea3b170f4c2aebb9c48d98ae64d-Abstract-Conference.html [Accessed 01-07-2026]
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
- cites [20] Zeng Z Cheng Q Yin Z Zhou Y Qiu X . Revisiting the test-time scaling of o1-like models: do they truly possess test-time scaling capabilities? In: Qiu X , editor. Presented at: Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1); Jul 27 to Aug 1, 2025; Vienna, Austria. [doi: 10.18653/v1/2025.acl-long.232] source
The Related Work section says increasing computation during inference can improve performance, and the Self-Revision results show that accuracy can initially improve before oscillating or declining. The Benchmark section covers mathematical and scientific reasoning, but not coding, so the citing sentence’s coding example is not supported by this paper. (LLM, conf=0.84)
Experimental — accuracy not yet measured
May combine two claims.
PremisecitationQwen3-VL models were posttrained using SFT, distillation, and Soft Adaptive Policy Optimization, an RL method specifically proposed for Qwen3-VL.
“For instance, the Qwen3-VL models were posttrained using SFT, distillation, and Soft Adaptive Policy Optimization [57], an RL method specifically proposed for Qwen3-VL.”
Quote in the paperSubstantial claim1 of 1 sources checked- cites [57] Gao C Zheng C Chen XH et al. Soft adaptive policy optimization. arXiv. Preprint posted online on Nov 25, 2025. [doi: 10.48550/arXiv.2511.20347] source
Section 5.2 (p. 8) says the authors used SAPO to train Qwen3-VL models, and Section 3 (p. 3) identifies SAPO as an RL fine-tuning method. The paper presents SAPO as a method for large language models generally, not one specifically proposed for Qwen3-VL. Our copy also does not establish the sentence’s SFT and distillation claims. (LLM, conf=0.87)
Experimental — accuracy not yet measured
Probably not a substantial claim: may not pass S3 (load-bearing). May combine two claims.
PremisecitationOptimal reasoning lengths exist and excessive self-revision can sometimes degrade performance.
“These works demonstrate that optimal reasoning lengths exist and that excessive self-revision can sometimes degrade performance [18-20].”
Quote in the paperNot a substantial claim1 of 3 sources checked- cites [18] Snell CV Lee J Xu K Kumar A . Scaling LLM test-time compute optimally can be more effective than scaling parameters for reasoning. Presented at: The Thirteenth International Conference on Learning Representations; Apr 24-28, 2025; Singapore EXPO. [doi: 10.18653/v1/2025.emnlp-main] source
- cites [19] Yang W Ma S Lin Y editors. Towards thinking-optimal scaling of test-time compute for LLM reasoning. In: Wei F , editor. Presented at: The Thirty-ninth Annual Conference on Neural Information Processing Systems; Dec 2-7, 2025; San Diego CA. URL: https://proceedings.neurips.cc/paper_files/paper/2025/hash/3e22bea3b170f4c2aebb9c48d98ae64d-Abstract-Conference.html [Accessed 01-07-2026]
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
- cites [20] Zeng Z Cheng Q Yin Z Zhou Y Qiu X . Revisiting the test-time scaling of o1-like models: do they truly possess test-time scaling capabilities? In: Qiu X , editor. Presented at: Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1); Jul 27 to Aug 1, 2025; Vienna, Austria. [doi: 10.18653/v1/2025.acl-long.232] source
May combine two claims.
Findingown dataMedically fine-tuned LLMs excelled in clinical question answering but exhibited degraded scaling efficiency on calculation tasks compared to general-domain models.
“Second, medically fine-tuned LLMs excelled in clinical question answering but exhibited degraded scaling efficiency on calculation tasks compared to general-domain models.”
Quote in the paperSubstantial claim- Rests on an assumption: The medical benchmark datasets used are valid proxies for real-world clinical tasks and reasoning.
May combine two claims.
Findingown dataWhile optimal scaling improved robustness, models exhibited a cognitive vulnerability by readily abandoning correct reasoning when confronted with misleading expert physician hints.
“Third, while optimal scaling improved robustness, models exhibited a cognitive vulnerability by readily abandoning correct reasoning when confronted with misleading expert physician hints.”
Quote in the paperSubstantial claim- Rests on an assumption: Embedding misleading hints with varying tones and simulated clinical expertise adequately simulates user-driven perturbations such as misleading clinical authority.
May combine two claims.
Findingown dataFor nonreasoning LLMs, accuracy saturated quickly, with token usage often remaining under 500 tokens regardless of budget increases.
“For nonreasoning LLMs, accuracy saturated quickly, with token usage often remaining under 500 tokens regardless of budget increases.”
Quote in the paperSubstantial claim- Rests on an assumption: The medical benchmark datasets used are valid proxies for real-world clinical tasks and reasoning.
Classified as probably not a substantial claim.
Findingnone givenMany existing LLMs that benefit from increased token budgets are not specifically designed for the medical domain.
“However, many existing LLMs that benefit from this approach are not specifically designed for the medical domain.”
Quote in the paperSubstantial claimMay combine two claims.
Findingown datam1 effectively uses a larger portion of the available token budget and exhibits substantial performance improvements as the token budget increases.
“In contrast, m1 effectively uses a larger portion of the available token budget and exhibits substantial performance improvements as the token budget increases.”
Quote in the paperSubstantial claim- Rests on an assumption: The ground truth labels in the benchmark datasets are correct.
May combine two claims.
Findingown dataQVQ displays test-time scaling on both benchmarks, but its performance is lower on the simpler OmniMedVQA and higher on the more challenging MedXpertQA.
“Interestingly, while QVQ displays test-time scaling on both benchmarks, its performance is paradoxically lower on the simpler OmniMedVQA and higher on the more challenging MedXpertQA.”
Quote in the paperSubstantial claim- Rests on an assumption: The ground truth labels in the benchmark datasets are correct.
May combine two claims.
Findingown dataExtended sequential scaling or increased budgets proved essential for complex problem-solving.
“Conversely, extended sequential scaling or increased budgets proved essential for complex problem-solving.”
Quote in the paperSubstantial claim- Rests on an assumption: The medical benchmark datasets used are valid proxies for real-world clinical tasks and reasoning.
May combine two claims.
Findingnone givenCurrent VLMs showed a structural bottleneck in integrating visual clues and experienced limited benefit from token expansion.
“First, current VLMs showed a structural bottleneck in integrating visual clues and experienced limited benefit from token expansion.”
Quote in the paperSubstantial claim- Rests on an assumption: The medical benchmark datasets used are valid proxies for real-world clinical tasks and reasoning.
May combine two claims.
Findingnone givenDeepSeek-R1 or Qwen3-TH models tend to use more tokens for reasoning as the token budget increases, and perform better with increased token budgets.
“For example, DeepSeek-R1 or Qwen3-TH models tend to use more tokens for reasoning as the token budget increases, and perform better with increased token budgets.”
Quote in the paperNot a substantial claim- Rests on an assumption: The ground truth labels in the benchmark datasets are correct.
Classified as probably not a substantial claim. May combine two claims.
Findingnone givenGeneral nonreasoning models have limited medical knowledge and relatively low performance on reasoning-intensive medical question answering tasks.
“Limited medical knowledgeRelatively low performance on reasoning-intensive medical question answering tasks”
Quote in the paperSubstantial claimMay combine two claims.
Findingown dataMedically fine-tuned LLMs excel in text-based medical QA but exhibit degraded scaling efficiency on clinical calculation tasks compared to general-domain models.
“we demonstrate that medically fine-tuned LLMs excel in text-based medical QA but exhibit degraded scaling efficiency on clinical calculation tasks compared to general-domain models.”
Quote in the paperSubstantial claimMay combine two claims.
Findingown dataTest-time scaling by increasing the token budget shows effectiveness only for some models, and even then, the performance gains in VLMs are considerably smaller than those observed in LLMs.
“This approach shows effectiveness only for some models, and even then, the performance gains in VLMs are considerably smaller than those observed in LLMs.”
Quote in the paperSubstantial claim- Rests on an assumption: The ground truth labels in the benchmark datasets are correct.
May combine two claims.
Findingnone givenm1 relies on generating long, uninterrupted reasoning chains and performs best with a simple expansion of the token budget.
“However, m1 relies on generating long, uninterrupted reasoning chains and performs best with a simple expansion of the token budget.”
Quote in the paperSubstantial claimMay combine two claims.
Findingnone givenGeneral reasoning models perform well on medical calculation tasks and high performance on reasoning-intensive tasks.
“Performs well on medical calculation tasksHigh performance on reasoning-intensive tasks”
Quote in the paperSubstantial claimMay combine two claims.
Findingown dataFor simpler tasks, extensive reasoning is unnecessary, and models that produce concise reasoning traces offer better computational efficiency.
“For simpler tasks, extensive reasoning is unnecessary, and models that produce concise reasoning traces offer better computational efficiency.”
Quote in the paperSubstantial claimMay combine two claims.
Findingnone givenMedical reasoning models perform well on medical question-answering tasks and high performance on reasoning-intensive tasks.
“Performs well on medical question-answering tasksHigh performance on reasoning-intensive tasks”
Quote in the paperSubstantial claimMay combine two claims.
Findingown dataMedGemma-27B demonstrates improved performance on MedXpertQA as the token budget expands, whereas the smaller MedGemma-4B does not.
“In our experiment, MedGemma-27B demonstrates improved performance on MedXpertQA as the token budget expands, whereas the smaller MedGemma-4B does not.”
Quote in the paperSubstantial claim- Rests on an assumption: The ground truth labels in the benchmark datasets are correct.
May combine two claims.
Findingown dataFor complex and challenging tasks, generating longer chain-of-thought reasoning leads to better performance, with models capable of producing extended reasoning traces outperforming their counterparts.
“In contrast, for complex and challenging tasks, generating longer chain-of-thought reasoning leads to better performance, with models capable of producing extended reasoning traces outperforming their counterparts.”
Quote in the paperSubstantial claimMay combine two claims.
Findingown dataScaling strategies generally mitigated the negative impact of misleading expert hints but often failed to fully restore baseline performance on difficult tasks.
“We observed that scaling strategies generally mitigated the negative impact of misleading expert hints but often failed to fully restore baseline performance on difficult tasks.”
Quote in the paperSubstantial claimMay combine two claims.
Findingown dataThe effectiveness of test-time scaling in VLMs is more pronounced on more difficult tasks, while it has little to no impact on simpler tasks.
“Notably, as in the case of LLMs, the effectiveness of test-time scaling in VLMs is more pronounced on more difficult tasks, while it has little to no impact on simpler tasks.”
Quote in the paperSubstantial claim- Rests on an assumption: The ground truth labels in the benchmark datasets are correct.
May combine two claims.
FindingreasoningParallel scaling minimizes the increase in GPU memory footprint to just the required key-value cache and significantly reduces wall-clock time.
“This minimizes the increase in GPU memory footprint to just the required key-value cache and significantly reduces wall-clock time”
Quote in the paperSubstantial claimMay combine two claims.
InterpretationreasoningThe underlying models differ in reasoning styles and output structures, likely leading to variations in the length and structure of chain-of-thought traces and influencing how each model uses tokens during inference.
“These underlying models differ in their reasoning styles and output structures, likely leading to variations in the length and structure of chain-of-thought traces and influencing how each model uses tokens during inference.”
Quote in the paperSubstantial claimMay combine two claims.
InterpretationreasoningQVQ's performance on MedXpertQA benefits from the presence of more informative textual clues within the questions, while the questions of OmniMedVQA are very simple.
“In contrast, the performance of QVQ on MedXpertQA benefits from the presence of more informative textual clues within the questions, while the questions of OmniMedVQA are very simple”
Quote in the paperSubstantial claimThe quote only partly states this claim.
InterpretationreasoningThe disparity between qualitative clinical alignment and procedural logic reflects the difference in scaling efficiency between medically fine-tuned and general-domain models on calculation tasks.
“This reflects a disparity between qualitative clinical alignment and procedural logic.”
Quote in the paperSubstantial claim- Rests on an assumption: The medical benchmark datasets used are valid proxies for real-world clinical tasks and reasoning.
May combine two claims.
InterpretationreasoningThese clues often enable QVQ to infer the correct answer even without fully using the image, thus reducing the impact of its visual processing limitations.
“These clues often enable the model to infer the correct answer even without fully using the image, thus reducing the impact of its visual processing limitations.”
Quote in the paperSubstantial claimMay combine two claims.
InterpretationreasoningThe stronger performance of general-purpose models on MedCalc-Bench does not necessarily imply deficient medical reasoning overall; it indicates that the benchmark captures a narrower procedural capability that is less tied to domain-specific clinical expertise.
“Therefore, the stronger performance of general-purpose models does not necessarily imply deficient medical reasoning overall. Instead, it indicates that the benchmark captures a narrower procedural capability that is less tied to domain-specific clinical expertise.”
Quote in the paperSubstantial claimProbably not a substantial claim: may not pass S2 (checkable).
InterpretationreasoningMinimizing perturbations by providing only neutral and essential information for complex tasks is important.
“emphasizing the importance of minimizing perturbations by providing only neutral and essential information for complex tasks.”
Quote in the paperNot a substantial claimProbably not a substantial claim: may not pass S4 (based).
Premisenone givenGenerating the same number of tokens with a large-scale model consumes substantially more compute, energy, and financial resources than a smaller model.
“Generating the same number of tokens with a large-scale model consumes substantially more compute (floating point operations per second), energy, and financial resources than a smaller model.”
Quote in the paperNot a substantial claimProbably not a substantial claim: may not pass S2 (checkable).
InterpretationreasoningUnderstanding when and how to appropriately scale reasoning at test time is a crucial foundational step toward developing clinical AI systems that are computationally efficient, robust, resilient, and trustworthy in high-stakes health care environments.
“Ultimately, understanding when and how to appropriately scale reasoning at test time is a crucial foundational step toward developing clinical AI systems that are not only computationally efficient but also robust, resilient, and trustworthy in high-stakes health care environments.”
Quote in the paperNot a substantial claimProbably not a substantial claim: may not pass S2 (checkable), S3 (load-bearing), S4 (based).
Interpretationnone givenCompared to LLMs, VLMs require more comprehensive evaluation under test-time scaling.
“In addition, compared to LLMs, VLMs require more comprehensive evaluation under test-time scaling.”
Quote in the paperNot a substantial claimMay combine two claims.
InterpretationreasoningCurrent medical alignment heavily prioritizes qualitative clinical knowledge over the general numerical and procedural reasoning captured by calculation benchmarks, leading medically fine-tuned models to be less optimized for calculation-heavy tasks rather than having impaired reasoning capabilities.
“it suggests that current medical alignment heavily prioritizes qualitative clinical knowledge over the general numerical and procedural reasoning captured by calculation benchmarks.”
- Also stated in: § Conclusions · hedged
“This trend likely reflects that current medical fine-tuning practices heavily focus on qualitative textual medical knowledge, meaning these models are less optimized for the procedural, calculation-heavy demands captured by such benchmarks rather than enduring an actual impairment of reasoning capabilities.”
Quote in the paperSubstantial claim- Also stated in: § Conclusions · hedged
Probably not a substantial claim: may not pass S2 (checkable).
InterpretationreasoningTreating inference compute as a static resource is no longer viable or safe.
“treating inference compute as a static resource is no longer viable or safe.”
Quote in the paperNot a substantial claimClassified as probably not a substantial claim.
InterpretationreasoningCurrent vision-language medical benchmarks may not be well-suited for evaluating reasoning ability.
“Next, the limitations may stem from the current vision-language medical benchmarks, which may not be well-suited for evaluating reasoning ability.”
Quote in the paperSubstantial claimProbably not a substantial claim: may not pass S2 (checkable).
InterpretationreasoningEvaluating deployment feasibility requires balancing reasoning performance against strict computational constraints.
“Therefore, evaluating deployment feasibility requires balancing reasoning performance against strict computational constraints.”
Quote in the paperNot a substantial claimClassified as probably not a substantial claim. May combine two claims.
InterpretationreasoningMost contemporary VLMs still struggle with or entirely lack the ability to jointly and effectively reason over complex visual cues in conjunction with textual information, which may explain the relatively limited performance on MedXpertQA compared to LLMs.
“Most contemporary VLMs still struggle with or entirely lack the ability to jointly and effectively reason over such complex visual cues in conjunction with textual information, which may explain the relatively limited performance on this benchmark compared to that of LLMs.”
Quote in the paperSubstantial claimProbably not a substantial claim: may not pass S2 (checkable).
InterpretationreasoningEnsuring patient safety, diagnostic accuracy, and operational efficiency requires a dynamic approach to model deployment.
“Our study highlights that ensuring patient safety, diagnostic accuracy, and operational efficiency requires a dynamic approach to model deployment.”
Quote in the paperNot a substantial claimMay combine two claims.
InterpretationreasoningSequential scaling inherently increases latency due to its serial nature and expanding context length, limiting its use to nonurgent, complex diagnostic scenarios.
“Conversely, sequential scaling inherently increases latency due to its serial nature and expanding context length, limiting its use to nonurgent, complex diagnostic scenarios.”
Quote in the paperSubstantial claimProbably not a substantial claim: may not pass S2 (checkable).
InterpretationreasoningComputational strategies must be carefully tailored to the specific urgency and complexity of the medical scenario.
“Computational strategies must be carefully tailored to the specific urgency and complexity of the medical scenario.”
Quote in the paperNot a substantial claimClassified as probably not a substantial claim.
PremisereasoningToken count does not equate to equal computational cost across different model architectures.
“token count does not equate to equal computational cost across different model architectures.”
Quote in the paperSubstantial claim
The paper relies on this without support.
The medical benchmark datasets used are valid proxies for real-world clinical tasks and reasoning.
“In our experiments, we evaluate LLMs on five medical benchmark datasets, including four medical question answering (QA) datasets and one medical calculation dataset.”
The paper relies on this without support.
Embedding misleading hints with varying tones and simulated clinical expertise adequately simulates user-driven perturbations such as misleading clinical authority.
“Robustness was tested by embedding misleading hints with varying tones and levels of simulated clinical expertise into prompts.”
The paper relies on this without support.
The ground truth labels in the benchmark datasets are correct.
“we extract the conclusion from the generated output and strictly compare it against the ground truth label of the dataset.”
The paper relies on this without support.
The benchmark datasets (MedQA, Medbullets, MedXpertQA, PubMedQA, OmniMedVQA, MedCalc-Bench) adequately represent medical reasoning tasks.
“We also investigate whether test-time scaling enhances the robustness of LLMs to user-driven factors using three medical QA benchmark datasets: MedQA, Medbullets, and MedXpertQA.”
The paper relies on this without support.
The evaluated LLMs and VLMs are representative of current medical and general-domain models.
“we evaluate two medical reasoning LLMs: HuatuoGPT-o1-7B (denoted as HuatuoGPT-7B) and m1-7B-1k (denoted as m1-7B).”
The authors state this as an assumption.
Public benchmark questions might have been included in the base models' pretraining corpora (data contamination).
“we cannot completely rule out the possibility of data contamination, where public benchmark questions might have been included in the base models’ pretraining corpora.”
The paper relies on this without support.
The benchmark datasets used for evaluation are finite samples of the broader population of real-world medical questions.
“the evaluation relies on specific benchmark datasets, which serve as finite samples of the much broader population of real-world medical questions.”
The paper relies on this without support.
Token count is a valid proxy for reasoning length or cognitive limits.
“our experimental design controls for the token budget to evaluate the cognitive limits of reasoning length”
The paper relies on this without support.
Benchmark performance generalizes to real-world clinical deployment.
“these findings hold broader implications for the real-world deployment of medical AI”
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
Each contradiction between two passages listed here was confirmed by an independent check.
Qwen3-Instruct small-model size differs between text and Table 2
MinorThe Models section says the smaller Qwen3-Instruct model is 8B, while Table 2 lists it as 4B.
“Qwen3-Instruct models (Qwen3-8B, 30B-IT)”
“| Qwen3-Instruct | General | Nonreasoning | Qwen3 | Unknown | Unknown | 4B, 30B (3B activated) |”
Qwen3-Thinking small-model size differs between text and Table 2
MinorThe Models section says the smaller Qwen3-Thinking model is 8B, while Table 2 lists it as 4B.
“Qwen3-Thinking models (Qwen3-8B, 30B-TH)”
“| Qwen3-Thinking | General | Reasoning | Qwen3 | Unknown | Unknown | 4B, 30B (3B activated) |”
Cites
Proximal Policy Optimization Algorithms
Schulman, J., Wolski, F., Dhariwal, P., Radford, A., & Klimov, O. (2017) · arXiv · doi:10.48550/arxiv.1707.06347 · Cited by 10,625 · cited 2× · see citing works
“The model is trained using SFT and RL via proximal policy optimization [21].”
Training Language Models to Follow Instructions with Human Feedback
Ouyang, L., Wu, J., Jiang, X., Almeida, D., Wainwright, C., Mishkin, P., Zhang, C., Agarwal, S., Slama, K., Ray, A., et al. (2022) · Advances in Neural Information Processing Systems 35 · doi:10.52202/068431-2011 · Cited by 3,117 · cited 1× · see citing works
“Llama 3 is trained using SFT followed by RL from human feed-back [47], whereas Qwen2.”
Llama 2: Open Foundation and Fine-Tuned Chat Models
Touvron, H., Martin, L., Stone, K., Albert, P., Almahairi, A., Babaei, Y., Bashlykov, N., Batra, S., Bhargava, P., Bhosale, S., et al. (2023) · arXiv · doi:10.48550/arxiv.2307.09288 · Cited by 2,604 · see citing works
Liu, H., Li, C., Wu, Q., & Lee, Y. J. (2023) · Advances in Neural Information Processing Systems 36 · doi:10.52202/075280-1516 · Cited by 2,428 · see citing works
Hansen, P. R., Lunde, A., & Nason, J. M. (2011) · Econometrica · doi:10.3982/ecta5771 · Cited by 2,153 · see citing works
DeepSeek-R1 incentivizes reasoning in LLMs through reinforcement learning
Guo, D., Yang, D., Zhang, H., Song, J., Wang, P., Zhu, Q., Xu, R., Zhang, R., Ma, S., Bi, X., et al. (2025) · Nature · doi:10.1038/s41586-025-09422-z · Cited by 1,701 · cited 4× · see citing works
“While the reasoning traces used to train HuatuoGPT-o1 were generated by GPT-4o [6,29], those used for m1 were synthesized by DeepSeek-R1 [9,34].”
InfluentialQwen2-VL: Enhancing Vision-Language Model's Perception of the World at Any Resolution
Wang, P., Bai, S., Tan, S., Wang, S., Fan, Z., Bai, J., Chen, K., Liu, X., Wang, J., Ge, W., et al. (2024) · arXiv · doi:10.48550/arxiv.2409.12191 · Cited by 1,302 · cited 1× · see citing works
“…Language and Vision Assistant–Chain-of-Thought; LLaVA-CoT-11B) is a VLM fine-tuned with synthetic chain-of-thought data using SFT. Finally, we evaluate the preview version of QVQ [55] (Qwen With Vision and Questions; QVQ-72B), a large-scale multimodal reasoning model built upon Qwen2-VL-72B [15].”
Gemma Team, Kamath, A., Ferret, J., Pathak, S., Vieillard, N., Merhej, R., Perrin, S., Matejovicova, T., Ramé, A., Rivière, M., et al. (2025) · arXiv · doi:10.48550/arxiv.2503.19786 · Cited by 1,156 · cited 1×
“5-VL-7B, 32B), Qwen3-VL [52] (Qwen3-VL-8B, 30B-IT), Gemma 3 [53] (Gemma 3-12B, 27B), and LLaVA [13] (Large Language and Vision Assistant; LLaVA-7B, 13B).”
Flamingo: a visual language model for few-shot learning
Alayrac, J.-B., Donahue, J., Luc, P., Miech, A., Barr, I., Hasson, Y., Lenc, K., Mensch, A., Millican, K., Reynolds, M., et al. (2022) · Advances in Neural Information Processing Systems 35 · doi:10.52202/068431-1723 · Cited by 966 · cited 1× · see citing works
“More recently, models that integrate data from multiple modalities, particularly vision-language models (VLMs) [10-16], have garnered significant attention among multimodal approaches.”
Jin, D., Pan, E., Oufattole, N., Weng, W.-H., Fang, H., & Szolovits, P. (2021) · Applied Sciences · doi:10.3390/app11146421 · Cited by 718 · see citing works
Direct Preference Optimization: Your Language Model is Secretly a Reward Model
Rafailov, R., Sharma, A., Mitchell, E., Manning, C. D., Ermon, S., & Finn, C. (2023) · Advances in Neural Information Processing Systems 36 · doi:10.52202/075280-2338 · Cited by 686 · cited 2× · see citing works
“UltraMedical [27] is a medical LLM fine-tuned using SFT followed by preference optimization techniques such as direct preference optimization [22] or Kahneman-Tversky optimization [50].”
PubMedQA: A Dataset for Biomedical Research Question Answering
Jin, Q., Dhingra, B., Liu, Z., Cohen, W., & Lu, X. (2019) · Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP) · doi:10.18653/v1/d19-1259 · Cited by 685 · cited 2× · see citing works
“PubMedQA [40] is a medical QA dataset comprising 500 research questions, each paired with a relevant abstract.”
LLaVA-Med: Training a Large Language-and-Vision Assistant for Biomedicine in One Day
Li, C., Wong, C., Zhang, S., Usuyama, N., Liu, H., Yang, J., Naumann, T., Poon, H., & Gao, J. (2023) · neural information processing systems · doi:10.52202/075280-1240 · Cited by 590 · see citing works
Grattafiori, A., Dubey, A., Jauhri, A., Pandey, A., Kadian, A., Al-Dahle, A., Letman, A., Mathur, A., Schelten, A., Vaughan, A., et al. (2024) · arXiv · doi:10.48550/arxiv.2407.21783 · Cited by 505 · cited 2× · see citing works
“Regarding general instruction-tuned LLMs, we evaluate instruction-tuned versions of Llama 3 [5] (Llama 3-3B, 8B, 70B) and Qwen2.”
Self-citationA generalist vision-language foundation model for diverse biomedical tasks
Zhang, K., Zhou, R., Adhikarla, E., Yan, Z., Liu, Y., Yu, J., Liu, Z., Chen, X., Davison, B. D., Ren, H., et al. (2024) · Nature Medicine · doi:10.1038/s41591-024-03185-2 · Cited by 333 · see citing works
Gemini 1.5: Unlocking multimodal understanding across millions of tokens of context
Gemini Team, Georgiev, P., Lei, V. I., Burnell, R., Bai, L., Gulati, A., Tanzer, G., Vincent, D., Pan, Z., Wang, S., et al. (2024) · arXiv · doi:10.48550/arxiv.2403.05530 · Cited by 306 · see citing works
Self-citationMoon, J. H., Lee, H., Shin, W., Kim, Y., & Choi, E. (2022) · IEEE Journal of Biomedical and Health Informatics · doi:10.1109/jbhi.2022.3207502 · Cited by 221 · cited 1× · see citing works
“Recently, applications of LLMs and VLMs in the medical domain have also gained momentum [24-30].”
OpenAI, :, Hurst, A., Lerer, A., Goucher, A. P., Perelman, A., Ramesh, A., Clark, A., Ostrow, A., Welihinda, A., et al. (2024) · arXiv · Cited by 180 · see citing works
Qwen, :, Yang, A., Yang, B., Zhang, B., Hui, B., Zheng, B., Yu, B., Li, C., Liu, D., et al. (2024) · arXiv · doi:10.48550/arxiv.2412.15115 · Cited by 128 · cited 1× · see citing works
“5 [8] (Qwen2.”
LlaVA-CoT: Let Vision Language Models Reason Step-By-Step
Xu, G., Jin, P., Wu, Z., Li, H., Song, Y., Sun, L., & Yuan, L. (2025) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv51701.2025.00202 · Cited by 118 · cited 1× · see citing works
“Next, LLaVA-CoT [54] (Large Language and Vision Assistant–Chain-of-Thought; LLaVA-CoT-11B) is a VLM fine-tuned with synthetic chain-of-thought data using SFT. Finally, we evaluate the preview version of QVQ [55] (Qwen With Vision and Questions; QVQ-72B), a large-scale multimodal reasoning model…”
Yang, A., Li, A., Yang, B., Zhang, B., Hui, B., Zheng, B., Yu, B., Gao, C., Huang, C., Lv, C., et al. (2025) · arXiv · doi:10.48550/arxiv.2505.09388 · Cited by 111 · cited 2× · see citing works
“In addition, we evaluate the Qwen3-Instruct models (Qwen3-8B, 30B-IT), which represent the updated nonthinking mode of the Qwen3 model [48].”
DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models
Shao, Z., Wang, P., Zhu, Q., Xu, R., Song, J., Bi, X., Zhang, H., Zhang, M., Li, Y. K., Wu, Y., et al. (2024) · arXiv · doi:10.48550/arxiv.2402.03300 · Cited by 101 · cited 3× · see citing works
“Med-R1 [32] uses group relative policy optimization [23] to improve generalizability across various medical imaging modalities, achieving a 29.94% accuracy improvement.”
InfluentialBai, S., Chen, K., Liu, X., Wang, J., Ge, W., Song, S., Dang, K., Wang, P., Wang, S., Tang, J., et al. (2025) · arXiv · doi:10.48550/arxiv.2502.13923 · Cited by 94 · cited 1× · see citing works
“5-VL [51] (Qwen2.”
OmniMedVQA: A New Large-Scale Comprehensive Evaluation Benchmark for Medical LVLM
Hu, Y., Li, T., Lu, Q., Shao, W., He, J., Qiao, Y., & Luo, P. (2024) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr52733.2024.02093 · Cited by 85 · cited 1× · see citing works
“OmniMedVQA [46] is a large-scale benchmark designed for evaluating medical VLMs. OmniMedQVA consists of simple and short questions based on provided images, making its overall difficulty relatively easy.”
Pan, J., Liu, C., Wu, J., Liu, F., Zhu, J., Li, H., Chen, C., Ouyang, C., & Rueckert, D. (2025) · Lecture notes in computer science · doi:10.1007/978-3-032-04981-0_32 · Cited by 85 · see citing works
Towards Injecting Medical Visual Knowledge into Multimodal LLMs at Scale
Chen, J., Gui, C., Ouyang, R., Gao, A., Chen, S., Chen, G. H., Wang, X., Cai, Z., Ji, K., Wan, X., et al. (2024) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/2024.emnlp-main.418 · Cited by 79 · cited 2× · see citing works
“HuatuoGPT-Vision [38] (HuatuoGPT-Vision-7B, 34B) is another medical VLM fine-tuned on medical VQA data.”
InfluentialMuennighoff, N., Yang, Z., Shi, W., Li, X. L., Fei-Fei, L., Hajishirzi, H., Zettlemoyer, L., Liang, P., Candes, E., & Hashimoto, T. (2025) · Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing · doi:10.18653/v1/2025.emnlp-main.1025 · Cited by 76 · see citing works
Inference-Time Scaling for Complex Tasks: Where We Stand and What Lies AheadPreprint
Balachandran, V., Chen, J., Chen, L., et al. (2025) · arXiv.org · doi:10.48550/arxiv.2504.00294 · Cited by 50 · cited 1× · see citing works
“In the study by Balachandran et al [36], it was demonstrated that both sequential and parallel scaling can improve the performance of nonreasoning and reasoning models.”
Yi: Open Foundation Models by 01.AI
AI, 0., :, Young, A., Chen, B., Li, C., Huang, C., Zhang, G., Zhang, G., Wang, G., Li, H., et al. (2024) · arXiv · doi:10.48550/arxiv.2403.04652 · Cited by 50 · see citing works
OpenAI, :, Jaech, A., Kalai, A., Lerer, A., Richardson, A., El-Kishky, A., Low, A., Helyar, A., Madry, A., et al. (2024) · arXiv · Cited by 50 · see citing works
Sellergren, A., Kazemzadeh, S., Jaroensri, T., Kiraly, A., Traverse, M., Kohlberger, T., Xu, S., Jamil, F., Hughes, C., Lau, C., et al. (2025) · arXiv · doi:10.48550/arxiv.2507.05201 · Cited by 49 · cited 2× · see citing works
“MedGemma [39] (MedG-emma-4B, 27B) is a medical variant of Gemma 3 trained on medical text and images.”
O1 Replication Journey - Part 3: Inference-time Scaling for Medical Reasoning
Huang, Z.-Z., Geng, G., Hua, S., et al. (2025) · arXiv.org · doi:10.48550/arxiv.2501.06458 · Cited by 40 · cited 2×
“Alongside these applications, both reasoning models [29-33] and test-time scaling [30,34,35] are being increasingly adopted in medical AI research.”
Benchmarking Large Language Models on Answering and Explaining Challenging Medical Questions
Chen, H., Fang, Z., Singla, Y., & Dredze, M. (2025) · Proceedings of the 2025 Conference of the Nations of the Americas Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 1: Long Papers) · doi:10.18653/v1/2025.naacl-long.182 · Cited by 31 · see citing works
KTO: Model Alignment as Prospect Theoretic Optimization
Ethayarajh, K., Xu, W., Muennighoff, N., Jurafsky, D., & Kiela, D. (2024) · arXiv · doi:10.48550/arxiv.2402.01306 · Cited by 29 · see citing works
Med-R1: Reinforcement Learning for Generalizable Medical Reasoning in Vision-Language Models
Lai, Y., Zhong, J., Li, M., Zhao, S., Li, Y., Psounis, K., & Yang, X. (2026) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2026.3661001 · Cited by 28 · cited 1× · see citing works
“Med-R1 [32] uses group relative policy optimization [23] to improve generalizability across various medical imaging modalities, achieving a 29.94% accuracy improvement.”
InfluentialBai, S., Cai, Y., Chen, R., Chen, K., Chen, X., Cheng, Z., Deng, L., Ding, W., Gao, C., Ge, C., et al. (2025) · arXiv · doi:10.48550/arxiv.2511.21631 · Cited by 27 · cited 2× · see citing works
“First, the thinking version of Qwen3-VL [52] (Qwen3-VL-8B, 30B-TH) is evaluated.”
Park, S. J., Lee, E. S., Shin, K. S., Lee, J. E., & Ye, J. C. (2023) · Medical Image Analysis · doi:10.1016/j.media.2023.103021 · Cited by 25 · see citing works
Self-citationReasoning Models Don't Always Say What They Think
Chen, Y., Benton, J., Radhakrishnan, A., Uesato, J., Denison, C., Schulman, J., Somani, A., Hase, P., Wagner, M., Roger, F., et al. (2025) · arXiv · doi:10.48550/arxiv.2505.05410 · Cited by 18 · cited 1× · see citing works
“Building on prior work [45,59,60], we focus on scenarios where the user injects misleading information into the prompt.”
UltraMedical: Building Specialized Generalists in Biomedicine
Zhang, K., Zeng, S., Hua, E., et al. (2024) · neural information processing systems · doi:10.52202/079017-0819 · Cited by 17 · see citing works
Towards Medical Complex Reasoning with LLMs through Medical Verifiable Problems
Chen, J., Cai, Z., Ji, K., Wang, X., Liu, W., Wang, R., & Wang, B. (2025) · Findings of the Association for Computational Linguistics: ACL 2025 · doi:10.18653/v1/2025.findings-acl.751 · Cited by 14 · cited 4× · see citing works
“While the reasoning traces used to train HuatuoGPT-o1 were generated by GPT-4o [6,29], those used for m1 were synthesized by DeepSeek-R1 [9,34].”
InfluentialMedCalc-Bench: Evaluating Large Language Models for Medical Calculations
Khandekar, N., Jin, Q., Xiong, G., et al. (2024) · neural information processing systems · doi:10.52202/079017-2690 · Cited by 12 · see citing works
Susceptibility of Large Language Models to User-Driven Factors in Medical Queries
Lim, K. H., Kang, U., Li, X., Kim, J. S., Jung, Y., Park, S. J., & Kim, B.-H. (2025) · Journal of Healthcare Informatics Research · doi:10.1007/s41666-025-00218-4 · Cited by 8 · cited 2× · see citing works
“Building on prior work [45,59,60], we focus on scenarios where the user injects misleading information into the prompt.”
Self-citationZeng, Z., Cheng, Q., Yin, Z., Zhou, Y., & Qiu, X. (2025) · Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) · doi:10.18653/v1/2025.acl-long.232 · Cited by 6 · see citing works
MedS³: Towards Medical Slow Thinking with Self-Evolved Soft Dual-sided Process Supervision
Jiang, S., Liao, Y., Chen, Z., Zhang, Y., Wang, Y., & Wang, Y. (2026) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v40i37.40395 · Cited by 4 · see citing works
xGen-MM (BLIP-3): A Family of Open Large Multimodal ModelsPreprint
Xue, L., Shu, M., Awadalla, A., et al. (2024) · arXiv (Cornell University) · doi:10.48550/arxiv.2408.08872 · Cited by 1 · cited 1× · see citing works
“More recently, models that integrate data from multiple modalities, particularly vision-language models (VLMs) [10-16], have garnered significant attention among multimodal approaches.”
m1: Unleash the Potential of Test-Time Scaling for Medical Reasoning with Large Language Models
Huang, X., Wu, J., Liu, H., Tang, X., & Zhou, Y. (2025) · arXiv · doi:10.48550/arxiv.2504.00869 · Cited by 1 · cited 7× · see citing works
“While the reasoning traces used to train HuatuoGPT-o1 were generated by GPT-4o [6,29], those used for m1 were synthesized by DeepSeek-R1 [9,34].”
InfluentialDisentangling Reasoning and Knowledge in Medical Large Language ModelsPreprint
Thapa, R., Wu, Q., Wu, K., Zhang, H. G., Zhang, A., Wu, E., Ye, H., Bedi, S., Aresh, N., Boen, J., et al. (2025) · arXiv (Cornell University) · doi:10.48550/arxiv.2505.11462 · Cited by 1 · cited 1× · see citing works
“Building on prior work [45,59,60], we focus on scenarios where the user injects misleading information into the prompt.”
Soft Adaptive Policy Optimization
Gao, C., Zheng, C., Chen, X.-H., Dang, K., Liu, S., Yu, B., Yang, A., Bai, S., Zhou, J., & Lin, J. (2025) · arXiv · doi:10.48550/arxiv.2511.20347 · cited 1×
“For instance, the Qwen3-VL models were posttrained using SFT, distillation, and Soft Adaptive Policy Optimization [57], an RL method specifically proposed for Qwen3-VL.”
Winners with Confidence: Discrete Argmin Inference with an Application to Model SelectionPreprint
Zhang, T., Lee, H., & Lei, J. (2024) · arXiv (Cornell University) · doi:10.48550/arxiv.2408.02060
Locally minimax optimal confidence sets for the best modelPreprint
Kim, I., & Ramdas, A. (2025) · arXiv (Cornell University) · doi:10.48550/arxiv.2503.21639
OpenAI, Achiam, J., Adler, S., Agarwal, S., Ahmad, L., Akkaya, I., Aleman, F. L., Almeida, D., Altenschmidt, J., Altman, S., et al. (2023) · arXiv · doi:10.48550/arxiv.2303.08774 · cited 1×
“Since the introduction of OpenAI’s GPT-3 [1], both industry and academia have actively competed to develop powerful LLMs [2-9], which are now widely adopted in daily life.”
gpt-oss-120b & gpt-oss-20b Model Card
OpenAI, :, Agarwal, S., Ahmad, L., Ai, J., Altman, S., Applebaum, A., Arbus, E., Arora, R. K., Bai, Y., et al. (2025) · arXiv · doi:10.48550/arxiv.2508.10925 · cited 1×
“Finally, we include the gpt-oss models [49] (gpt-oss-20B, 120B), OpenAI’s open-source series trained via SFT and RL.”
Flagged first, then by citations
Research works printed but not linked: 8
- Brown T Mann B Ryder N et al. Language models are few-shot learners. Presented at: NIPS’20: Proceedings of the 34th International Conference on Neural Information Processing Systems; Dec 6-12, 2020:1877-1901; Vancouver, British Columbia, Canada. URL: https://proceedings.neurips.cc/paper/2020/file/1457c0d6bfcb4967418bfb8ac142f64a-Paper.pdf [Accessed 01-07-2026]
- Li J Li D Xiong C . BLIP: bootstrapping language-image pre-training for unified vision-language understanding and generation. In: Hoi S , editor. Presented at: International Conference on Machine Learning; Jul 17-23, 2022; Baltimore MD. URL: https://proceedings.mlr.press/v162/li22n/li22n.pdf [Accessed 01-07-2026]
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- Dai W Chen P Ekbote C Liang PP . QoQ-med: building multimodal clinical foundation models with domain-aware GRPO training. Presented at: 39th Conference on Neural Information Processing Systems (NeurIPS 2025); Dec 2-7, 2025; San Diego, CA. URL: https://proceedings.neurips.cc/paper_files/paper/2025/file/359ffa88712bd688963a0ca641d8330b-Paper-Conference.pdf [Accessed 02-07-2026]
- Kaya MO Elliott D Papadopoulos DP . Efficient test-time scaling for small vision-language models. Presented at: The Fourteenth International Conference on Learning Representations; Apr 23-27, 2026; Rio de Janeiro, Brazil. URL: https://monurcan.github.io/efficient_test_time_scaling/ [Accessed 04-07-2026]
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Cited by
Sorted by citations
Advanced deep learning methods for text generation in 2022-2024 literature: a systematic review
Slobodianiuk, A. V., & Семеріков, С. О. (2026) · doi:10.55056/cssesw2025/paper42
“Chain-of-thought prompting, self-consistency, tree-and graph-structured search, and self-verification were treated by 2026 as core quality mechanisms rather than prompting tricks [82, 83, 84].”
- Authors
- Oh, G., Kim, S., Park, S., & Kim, B.-H.
- Affiliations
- Yonsei University (KR)
- Venue
- Journal of Medical Internet Research
- Publisher
- JMIR Publications Inc.
- Published
- 2026-07-23
- Volume
- 28
- Pages
- e90693-e90693
- ISSN
- 1438-8871 (ISSN-L)
- PMC
- PMC13397005
- OpenAlex
- W7170198514
- Cited by
- 1
- References
- 26
- Citations by year
- 2026: 1
- FWCI
- 2.02
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- cc-by
- Locations
- Journal of Medical Internet Research (publishedVersion, cc-by)
- PubMed (publishedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion)
- PubMed Central (submittedVersion, cc-by)
- Topics
- Health Sciences › Medicine › Health Informatics › Artificial Intelligence in Healthcare and Education
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Multimodal Machine Learning Applications
- Physical Sciences › Computer Science › Artificial Intelligence › Topic Modeling
- Keywords
- test-time scaling, large language models, vision-language models, artificial intelligence in healthcare, token budget, parallel scaling, chain-of-thought reasoning, medical question answering
- MeSH terms
- Large Language Models, Humans, Medical Informatics
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Claims read by deepseek-v4.1-flash under definition version 3 (extract-v3) on 2026-10-07.
Classified by jev-1.13-20260917 (gold-guideline-v0.2) on 2026-10-07.
Cited sources are checked by gpt-6-sol.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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