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.
@article{arxiv.2509.21849,
title = {Following the TRACE: A Structured Path to Empathetic Response Generation with Multi-Agent Models},
author = {Ziqi Liu and Ziyang Zhou and Yilin Li and Haiyang Zhang and Yangbin Chen},
journal= {arXiv preprint arXiv:2509.21849},
year = {2026}
}