Structured and Natural Responses Co-generation for Conversational Search

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How does AI-generated content affect visibility and discourse in digital platforms? What systematic biases emerge in AI shopping agents' decision-making? How can recommendation systems balance individual and population-level goals? What conversation mechanics enable genuine human-AI communicative participation? How should conversational agents balance proactive goal-pursuit with user autonomy? Do AI explanations represent actual model reasoning or constructed post-hoc narratives? Do language models learn underlying linguistic structure or merely surface patterns? How can parameter sparsity improve interpretability while maintaining model capability? Do language models truly understand their encoded knowledge or merely retrieve patterns? How does verifier integration affect the generation-verification gap? What determines whether visible reasoning traces improve task performance? Can we reliably distinguish AI-generated from human-written text? When and why does aggregating multiple reasoning samples improve performance? Does generative AI replace coordination with individual productivity? Why does separating planning from execution enhance reasoning performance? Does reasoning architecture matter more than model scale?