用大模型筛选更易懂的视频码率决策树，让算法既高效又好懂。
Beyond Interpretability: Exploring the Comprehensibility of Adaptive Video Streaming through Large Language Models
- 生成满足性能要求的完整决策树集，再用大模型评估可读性。
- 实验表明在保持竞争力的前提下显著提升开发者理解度。
- 适合需要解释性优化的流媒体系统开发与调试人员。
过去十年，自适应视频流技术在深度学习推动下取得显著进展。然而，深度学习算法的黑箱特性使开发者难以理解其决策过程，制约了针对特定场景的优化。现有研究通过决策树转换提升了算法可解释性，但可解释性不等于开发者主观可理解性。为此，本文提出首个考虑可理解性的码率自适应算法生成框架 exttt{ComTree}。该框架先生成满足性能要求的完整决策树集合，再利用大语言模型评估各树的开发者可理解性，最终选择最利于人类理解与改进的方案。实验表明， exttt{ComTree} 在保持竞争性性能的同时显著提升可理解性，具备进一步发展潜力。代码已开源：https://github.com/thu-media/ComTree。
原文摘要 · Abstract (English)
Over the past decade, adaptive video streaming technology has witnessed significant advancements, particularly driven by the rapid evolution of deep learning techniques. However, the black-box nature of deep learning algorithms presents challenges for developers in understanding decision-making processes and optimizing for specific application scenarios. Although existing research has enhanced algorithm interpretability through decision tree conversion, interpretability does not directly equate to developers' subjective comprehensibility. To address this challenge, we introduce \texttt{ComTree}, the first bitrate adaptation algorithm generation framework that considers comprehensibility. The framework initially generates the complete set of decision trees that meet performance requirements, then leverages large language models to evaluate these trees for developer comprehensibility, ultimately selecting solutions that best facilitate human understanding and enhancement. Experimental results demonstrate that \texttt{ComTree} significantly improves comprehensibility while maintaining competitive performance, showing potential for further advancement. The source code is available at https://github.com/thu-media/ComTree.
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