EMPA：评估基于角色的共情作为过程
EMPA: Evaluating Persona-Aligned Empathy as a Process
AI总结：
本文提出EMPA框架，通过可控心理场景和多智能体沙盒评估持续干预而非孤立回复，以提升对话代理的共情能力。
摘要：
在基于大语言模型的对话代理中评估基于角色的共情仍具挑战性。用户状态是隐含的，反馈稀少且难以现场验证，看似支持性的回复仍可能累积成偏离角色需求的轨迹。我们引入EMPA，一个以过程为导向的框架，将基于角色的支持视为持续干预而非孤立回复。EMPA将真实互动提炼成可控、心理基础的场景，将其与开放的多智能体沙盒结合，暴露战略适应和失败模式，并通过方向对齐、累积影响和稳定性在潜在心理空间中评分轨迹。所得到的信号和指标支持可重复比较和优化长期共情行为，并扩展到由隐含动态和弱、难以验证反馈塑造的其他代理设置。
英文摘要：
Evaluating persona-aligned empathy in LLM-based dialogue agents remains challenging. User states are latent, feedback is sparse and difficult to verify in situ, and seemingly supportive turns can still accumulate into trajectories that drift from persona-specific needs. We introduce EMPA, a process-oriented framework that evaluates persona-aligned support as sustained intervention rather than isolated replies. EMPA distills real interactions into controllable, psychologically grounded scenarios, couples them with an open-ended multi-agent sandbox that exposes strategic adaptation and failure modes, and scores trajectories in a latent psychological space by directional alignment, cumulative impact, and stability. The resulting signals and metrics support reproducible comparison and optimization of long-horizon empathic behavior, and they extend to other agent settings shaped by latent dynamics and weak, hard-to-verify feedback.
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。
