Scientific ideation is driven by curiosity: researchers ask questions that expose knowledge gaps, reveal competing hypotheses, and clarify missing evidence relevant to decision making. Yet, most LLM-based ideation systems optimize the idea text while leaving curiosity under-modeled, resulting in brittle, engine-specifi...
Sikun Guo, Di Wang, Xiaohan Fan et al.· Proceedings of the 32nd ACM...· 0 citations
Scientific ideation is driven by curiosity: researchers ask questions that expose knowledge gaps, reveal competing hypotheses, and clarify missing evidence relevant to decision making. Yet, most LLM-based ideation systems optimize the idea text while leaving curiosity under-modeled, resulting in brittle, engine-specifi...
Sikun Guo, Di Wang, Xiaohan Fan et al.· Proceedings of the 32nd ACM...· 0 citations
This paper designs a target-focused perception mechanism that identifies and enhances visual tokens of the search target, consisting of a target Enhancer and a learning strategy based on on-policy distillation and attention guidance loss, enabling the model to focus on search-target regions.
Xiaohan Fan, Yueran Liu, Shengyu Zhou et al.· 0 citations
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