ENPMR-Bench: 情感支持代理的主动记忆检索基准
ENPMR-Bench: Benchmarking Proactive Memory Retrieval for Emotional Support Agents
AI总结：
提出ENPMR-Bench基准，基于马斯洛需求层次评估情感支持代理主动推断用户潜在情感需求并检索适当记忆的能力，实验表明当前检索范式存在显著缺陷。
摘要：
记忆增强的语言代理越来越多地部署在情感支持等情感应用中，在这些应用中，理解和响应用户的潜在情感需求至关重要。然而，现有研究通常将记忆视为事实检索的工具，忽视了其在塑造用户情感体验中的作用。在这项工作中，我们引入了ENPMR-Bench，一个用于评估情感需求感知的主动记忆检索（ENPMR）的基准，这是一种核心能力，使代理能够推断用户的潜在情感需求并主动检索适当的记忆以支持共情交互。基于马斯洛需求层次，ENPMR-Bench包括超过1,800个记忆增强对话，并定义了情感需求与支持性记忆类型之间的结构化映射。实验结果表明，当前的检索范式，包括基于嵌入和LLM驱动的方法，都存在显著缺陷，共情得分明显落后于黄金记忆条件。虽然思维链提示在一定程度上改善了推断的情感需求与检索记忆之间的一致性，但性能差距仍然显著。总之，这些发现揭示了当前代理的关键局限性，并指出了通过需求敏感的记忆检索推进个性化情感支持的方向。
英文摘要：
Memory-augmented language agents are increasingly deployed in affective applications such as emotional support, where understanding and responding to users' latent emotional needs is critical. However, existing research often treats memory as a tool for factual retrieval, overlooking its role in shaping users' emotional experiences. In this work, we introduce ENPMR-Bench, a benchmark for evaluating Emotional Need-aware Proactive Memory Retrieval (ENPMR), a core capability that enables agents to infer users' latent emotional needs and proactively retrieve appropriate memories to support empathetic interaction. Grounded in Maslow's hierarchy of needs, ENPMR-Bench includes over 1,800 memory-augmented dialogues and defines structured mappings between emotional needs and supportive memory types. Experimental results demonstrate that current retrieval paradigms, including both embedding-based and LLM-driven approaches, exhibit substantial deficiencies, with empathy scores significantly lagging behind golden memory conditions. While chain-of-thought prompting improves the alignment between inferred emotional needs and retrieved memories to some extent, a notable performance gap remains. Together, these findings reveal critical limitations in current agents and outline directions for advancing personalized emotional support through need-sensitive memory retrieval.
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。
