医学AI推理时增强方法研究，提升大模型可靠性。
Rethinking Test-Time Scaling for Medical AI: Model and Task-Aware Strategies for LLMs and VLMs
- 设计模型与任务自适应的测试时扩展策略
- 验证不同模型规模与任务复杂度下的效果差异
- 适用于对可解释性要求高的医疗AI场景
测试时缩放（Test-time scaling）近期被证明是提升大型语言模型或视觉-语言模型推理能力的有效方法。尽管已有多种测试时缩放策略提出，且在医疗领域的应用兴趣日益增长，但其在视觉-语言模型中的有效性、以及针对不同场景的最优策略仍缺乏深入研究。本文对医疗领域中的测试时缩放进行了全面评估，考察了其在大型语言模型和视觉-语言模型上的影响，考虑了模型规模、内在特性及任务复杂度等因素。同时，评估了用户引导因素（如提示中嵌入的误导信息）对策略鲁棒性的影响。研究结果为医疗应用中有效使用测试时缩放提供了实践指南，并揭示了如何进一步优化策略以满足医疗领域对可靠性和可解释性的要求。
原文摘要 · Abstract (English)
Test-time scaling has recently emerged as a promising approach for enhancing the reasoning capabilities of large language models or vision-language models during inference. Although a variety of test-time scaling strategies have been proposed, and interest in their application to the medical domain is growing, many critical aspects remain underexplored, including their effectiveness for vision-language models and the identification of optimal strategies for different settings. In this paper, we conduct a comprehensive investigation of test-time scaling in the medical domain. We evaluate its impact on both large language models and vision-language models, considering factors such as model size, inherent model characteristics, and task complexity. Finally, we assess the robustness of these strategies under user-driven factors, such as misleading information embedded in prompts. Our findings offer practical guidelines for the effective use of test-time scaling in medical applications and provide insights into how these strategies can be further refined to meet the reliability and interpretability demands of the medical domain.
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