Structured and Natural Responses Co-generation for Conversational Search

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Question Answering and Search

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What mechanisms preserve shared understanding in evolving conversations? What determines appropriate intervention timing and manner for AI agents? What prevents conversational agents from taking initiative in dialogue? What happens to knowledge when intelligence becomes tokenized like a commodity? Can memory architectures handle ultra-long context better than attention? How can evolutionary algorithms maintain diversity during solution search? Does encoded knowledge in language models actually influence their outputs? How should inference compute be allocated based on problem difficulty? Do reasoning benchmarks predict model performance in long-horizon workflows? How effectively can language models perform reasoning, especially combined with symbolic methods? How does decomposing tasks improve reasoning and prevent failure propagation? How should retrieval systems handle complex multi-step reasoning? What do systematic disagreements between annotators reveal about ground truth? How should agents manage memory granularity to improve long-term performance? Can compression size predict model complexity better than parameter count alone? How should designers communicate what AI systems truly are and can do? How does evaluation scope and dimensionality affect what we measure?