ComPASS：通过工具增强的陪伴实现个性化代理社交支持
ComPASS: Towards Personalized Agentic Social Support via Tool-Augmented Companionship
AI总结：
本文提出ComPASS框架，通过外部工具增强代理能力，构建首个个性化社交支持基准，训练出ComPASS-Qwen模型，提升响应质量。
摘要：
开发具有同理心的交互系统需要代理不仅理解用户情绪，还提供多样实质支持。尽管近期工作探索共情对话生成，但响应形式和内容有限，难以满足多样化需求。为此，本文探索赋予代理外部工具以执行多样化动作。基于
英文摘要：
Developing compassionate interactive systems requires agents to not only understand user emotions but also provide diverse, substantive support. While recent works explore empathetic dialogue generation, they remain limited in response form and content, struggling to satisfy diverse needs across users and contexts. To address this, we explore empowering agents with external tools to execute diverse actions. Grounded in the psychological concept of "social support", this paradigm delivers substantive, human-like companionship. Specifically, we first design a dozen user-centric tools simulating various multimedia applications, which can cover different types of social support behaviors in human-agent interaction scenarios. We then construct ComPASS-Bench, the first personalized social support benchmark for LLM-based agents, via multi-step automated synthesis and manual refinement. Based on ComPASS-Bench, we further synthesize tool use records to fine-tune the Qwen3-8B model, yielding a task-specific ComPASS-Qwen. Comprehensive evaluations across two settings reveal that while the evaluated LLMs can generate valid tool-calling requests with high success rates, significant gaps remain in final response quality. Moreover, tool-augmented responses achieve better overall performance than directly producing conversational empathy. Notably, our trained ComPASS-Qwen demonstrates substantial improvements over its base model, achieving comparable performance to several large-scale models. Our code and data are available at https://github.com/hzp3517/ComPASS.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
