Structured and Natural Responses Co-generation for Conversational Search

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How can language models sustain linguistic synchrony and intersubjectivity during dialogue? How should conversational agents balance goal-driven initiative with user control? How should dialogue recommender systems manage conversation history and state? How does AI assistance affect human cognitive development and reasoning autonomy? Which computational strategies best support reasoning in language models? Do language models learn genuine linguistic structure or just surface patterns? Why does verification consistently lag behind AI generation? How do prompt structure and constraints affect model instruction reliability? How do neural networks separate factual knowledge from reasoning abilities? What makes specific clarifying questions more effective than generic ones? What memory architectures best support persistent reasoning across extended interactions? What dimensions of recommendation quality do standard metrics miss? How should memory consolidation strategies shape agent performance over time? How should retrieval systems optimize for multi-step reasoning during inference? What role does compression play in language model capability and generalization? Why do multi-turn conversations degrade AI intent and coherence? Can single-axis benchmarks accurately predict agent deployment success? How does AI-generated content transformation affect public discourse quality?