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-specific gains. To address this, we propose Curiosity-Driven Questioning (CDQ), a training-free, engine-agnostic method that constructs a compact set of curiosity-driven questions from a topic corpus under black-box LLM access. CDQ scores questions with three corpus-relative indices (GapExposure, DisagrDiversity, ClarityGain) and uses a two-stage scoring with an entropic mirror-ascent reweighting over question meta-policies to concentrate on high-utility question policies. Our results show that prepending CDQ questions improves downstream ideation across single-pass, retrieval-augmented generation, and self-reflection engines with multiple base models under preference-based evaluation. Ablations and a human study further support the complementarity of the indices and the reliability of the evaluation. Our code and dataset are available at: https://github.com/Sikun-Skyler-Guo/CDQ
Sikun Guo, Di Wang, Xiaohan Fan et al.· Proceedings of the 32nd ACM...· 0 citations
Generative retrieval has demonstrated strong performance by directly generating product semantic identifiers (SIDs). Extending this paradigm to image search, however, is nontrivial because real-world query images contain diverse information, including the search target, useful auxiliary evidence, and irrelevant visual content. This requires the model to identify and focus on the search target while selectively utilizing auxiliary evidence. In this paper, we propose \textbf{PailitaoGR}, a \emph{Latent Think-with-Images} method for generative image retrieval, which internalizes target-focused perception and selective auxiliary-evidence utilization into a the generative retrieval model, enabling \textit{Zooming without Cropping} and \textit{Reading without OCR}. Specifically, we design 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. We also design a selective auxiliary-evidence utilization mechanism that identifies and enhances visual tokens of auxiliary evidence, including an auxiliary enhancer and an in-capacity incremental contrastive distillation strategy, enabling the model to exploit auxiliary evidence. We construct training and validation sets sampled from real-world online image-search logs. Experiments show that our method outperforms existing baselines by an average of 13.8\%, validating its effectiveness.
Xiaohan Fan, Yueran Liu, Shengyu Zhou et al.· 0 citations
