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Emotion-driven feedback can transform multi-turn dialogue systems, boosting empathetic responses and model performance significantly.
Nearly 20% of LLM agent violations occur even after agents acknowledge safety constraints, highlighting a critical gap in execution awareness.
U$^2$Mamba achieves superior salient object detection by integrating contextual information across multiple layers without sacrificing resolution.
Teaching emotional support chatbots specific, executable skills, rather than relying on end-to-end training, leads to more interpretable, controllable, and ultimately more helpful conversations.
Allowing multiple support strategies in a single utterance can dramatically enhance the quality of emotional support conversations, leading to more effective dialogue outcomes.
