Empathetic response generation is a crucial task for creating more human-like and supportive conversational agents. However, existing methods face a core trade-off between the analytical depth of specialized models and the generative fluency of Large Language Models (LLMs). To address this, we propose TRACE, Task-decomposed Reasoning for Affective Communication and Empathy, a novel framework that models empathy as a structured cognitive process by decomposing the task into a pipeline for analysis and synthesis. By building a comprehensive understanding before generation, TRACE unites deep analysis with expressive generation. Experimental results show that our framework significantly outperforms strong baselines in both automatic and LLM-based evaluations, confirming that our structured decomposition is a promising paradigm for creating more capable and interpretable empathetic agents. Our code is available at https://anonymous.4open.science/r/TRACE-18EF/README.md.

翻译：共情响应生成是构建更具人性化和支持性对话智能体的关键任务。然而，现有方法在专用模型的分析深度与大型语言模型（LLMs）的生成流畅性之间存在核心权衡。为解决这一问题，我们提出TRACE（面向情感交流与共情的任务分解推理），这是一个新颖的框架，通过将任务分解为分析与合成的流程，将共情建模为一个结构化的认知过程。TRACE在生成前构建全面理解，从而将深度分析与表达性生成相结合。实验结果表明，我们的框架在自动评估和基于LLM的评估中均显著优于强基线，证实了我们的结构化分解是构建能力更强、可解释性更高的共情智能体的有前景的范式。我们的代码发布于 https://anonymous.4open.science/r/TRACE-18EF/README.md。