arXiv:2605.27240v1 Announce Type: new Abstract: 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
The increasing deployment of memory-augmented language agents in emotional support applications necessitates more nuanced evaluation benchmarks that move beyond factual retrieval.
This benchmark highlights the critical need for AI agents to understand and proactively address latent emotional needs, pushing the frontier of human-AI interaction beyond simple task completion.
AI development for emotional support will now have a specific framework to measure and improve the proactive emotional intelligence of agents, moving from reactive to anticipatory responses.
- · AI developers focused on emotional intelligence
- · Therapeutic AI companies
- · Users of emotional support agents
- · AI models lacking emotional intelligence
- · Developers focused solely on factual retrieval
Emotional support agents become more effective at building rapport and trust with users.
Increased adoption of AI in mental health and well-being applications, requiring new regulatory frameworks.
Societal reliance on AI for emotional processing, potentially altering human empathy and interpersonal skill development.
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