[Submitted on 25 Sep 2025 (v1), last revised 5 Jan 2026 (this version, v2)]
View a PDF of the paper titled On the Robustness of Answer Formats in Medical Reasoning Models, by Pittawat Taveekitworachai and five other authors.
Abstract: Medical reasoning models (MRMs) achieve superior performance on medical benchmarks compared to medical large language models (LLMs); however, high accuracy alone is insufficient for practical deployment. One of the requirements for real-world applications is robustness to varying output constraints. Specifically, posing the same medical question while requesting different answer formats should not affect the underlying correctness of the response. We investigate this phenomenon in this paper, focusing on MRMs. To quantify this behavior, we propose the metric answer-format robustness: the ability to reliably generate correct outputs across varying specified formats. We examine three representative formats: multiple-choice, open-ended question-answering, and ranked lists. Across 15 proprietary and open-weight models, we observe substantial variation in format robustness (35-100%). Furthermore, we conduct controlled fine-tuning experiments on a shared backbone with matched training data to isolate the effects of the fine-tuning paradigm. We find that supervised fine-tuning yields more stable behavior across formats, whereas reinforcement fine-tuning often exhibits higher cross-format brittleness, with the degree of instability strongly dependent on reward design. Overall, answer-format robustness in MRMs is trainable yet brittle and requires careful evaluation for practical medical use.
Submission History
From: Pittawat Taveekitworachai [view email]
[v1] Thu, 25 Sep 2025 07:59:23 UTC (1,531 KB)
[v2] Mon, 5 Jan 2026 04:55:47 UTC (1,801 KB)
### Understanding Medical Reasoning Models (MRMs)
Medical reasoning models (MRMs) represent a significant advancement in healthcare technology, especially in processing and interpreting medical data. Unlike traditional medical large language models (LLMs), which generate language-focused outputs, MRMs are designed to understand and provide medical reasoning. They excel in various benchmarks, evidencing their superiority in accuracy. However, mere accuracy doesn’t guarantee these models’ applicability in real-world scenarios — one critical factor is the robustness of their outputs across varying answer formats.
### The Significance of Answer Format Robustness
Answer format robustness refers to the model’s ability to produce accurate and consistent results across different types of queries. For instance, a model might be asked the same question in multiple formats—multiple-choice, open-ended, or ranked lists—and should ideally return the same core information regardless of the format. This is essential in medical settings where practitioners rely on specific formats for decision-making. Ensuring that MRMs maintain accuracy across differing formats is paramount for their successful implementation in clinical environments.
### Research Methodology and Findings
In the study “On the Robustness of Answer Formats in Medical Reasoning Models,” the researchers undertook a comprehensive analysis of MRMs across various formats. Using a diverse range of both proprietary and open-weight models, they discovered notable differences in format robustness. The findings highlighted a variation in performance, with robustness percentages ranging from 35% to 100%. This variance underscores the necessity for further investigation into the influences affecting these models.
### Controlled Fine-Tuning Experiments
To delve deeper into the characteristics of MRMs, the authors conducted controlled fine-tuning experiments employing a common backbone architecture and matched training datasets. This approach allowed them to isolate the effects of different fine-tuning paradigms on answer format robustness. Their results suggested that supervised fine-tuning significantly enhances the consistency of outputs across various formats. In contrast, reinforcement fine-tuning often resulted in increased brittleness, highlighting that the design of reward mechanisms considerably impacts model stability.
### Implications for Practical Medical Use
The findings of this research emphasize that while MRMs have the potential for robust performance, they require meticulous evaluation before being deployed in real-world medical applications. The researchers advocate for continuous refinement and testing of MRMs to address the brittleness and ensure that robustness remains trainable. Balancing the complexity of model training and the necessity for accuracy across multiple output formats is a critical challenge that developers and researchers must tackle.
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### Future Directions in Medical Reasoning Models
The exploration of answer formats within MRMs opens up intriguing avenues for future research. As the demand for reliable medical AI continues to grow, understanding the nuances of model performance across various scenarios will be essential. There is a clear implication that further studies are needed to enhance both the robustness and the adaptability of MRMs, ensuring they can consistently meet the diverse demands of healthcare professionals.
Through this ongoing research and development, the objective is to create a diverse toolkit of medical reasoning models that not only excel in accuracy but also offer the reliability necessary for complex medical decision-making processes. This will enable healthcare systems to better leverage AI technologies, ultimately improving patient outcomes and healthcare efficiency.
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