Latte: Towards Agentic Emotional Support Systems

Emotional-support agents are increasingly capable of understanding users and generating helpful responses, yet some support needs arise from practical circumstances that language alone cannot address. We study Agentic Emotional Support (AES), which extends emotional support beyond response generation by enabling agents to understand users' circumstances and take grounded, appropriate actions when helpful. We introduce LATTE, a benchmark of 90 open-ended scenarios in reproducible multi-application Android environments. Each scenario begins with an affective, operationally underspecified utterance and specifies no oracle action or unique terminal state. We evaluate 11 models under Emotional Support Response, Task-oriented Action, and AES using trajectory-level evaluation of Overall Support and Action Quality, together with Grounding and Consent diagnostics. Results show that AES generally improves support over both response-only and task-oriented baselines. More importantly, its benefits depend far more on decision and execution quality than on action frequency: acting more is not inherently better. A bounded Decision-Correction procedure further improves AES for some model families, but not uniformly across backbones. Together, these findings advance emotional-support agents from supportive response toward appropriate action, showing that the key is not merely to act, but to act well.