Rethinking Conversational Agents in the Era of LLMs: Proactivity, Non-collaborativity, and Beyond

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Conversational Agents

as LLMs are trained to follow users’ instructions, LLM-augmented conversational systems typically overlook the design of an essential property in intelligent conversations, i.e., goal awareness. In this tutorial, we will introduce the recent advances on the design of agent’s awareness of goals in a wide range of conversational systems, including proactive, non-collaborative, and multi-goal conversational systems.

Derived from the definition of proactivity in organizational behaviors [23] and its dictionary definitions, conversational agents’ proactivity can be defined as the capability to create or control the conversation by taking the initiative and anticipating impacts on themselves or human users.

Proactive ODD systems can consciously change topics [49] and lead directions [45, 48] for improving user engagement in the conversation. We will present the existing methods for topic shifting and planning in open-domain dialogues, including keyword-based discourse-level topic planning [45], graph-based topic planning [38, 52], and learning from interactions with users [28].

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How does explicit structure improve LLM reasoning on complex tasks? What conversation mechanics enable genuine human-AI communicative participation? How should conversational agents balance proactive goal-pursuit with user autonomy? How does offloading cognition to AI assistance affect learning retention? How should control and autonomy be allocated in human-AI collaboration? Does preference-optimized training sacrifice accuracy and calibration for perceived quality? Do AI explanations represent actual model reasoning or constructed post-hoc narratives? Why doesn't LLM knowledge translate to improved reasoning outputs? How should AI ethics integrate consciousness, values, and transparent disclosure? How do dialogue structure and state management affect multi-turn reasoning quality? How does transparency of agent reasoning affect effective oversight? What makes context and memory handling unique in persistent AI systems? What constraints limit conversational AI's effectiveness in therapeutic contexts? Should GUI agents use structured UI perception instead of raw pixels? What reasoning limitations and biases do language models systematically exhibit?