语义提示：通过空间语义交互实现代理增量叙事优化
Semantic Prompting: Agentic Incremental Narrative Refinement through Spatial Semantic Interaction
AI总结：
本文提出语义提示框架，通过感知语义交互、推理解释意图并执行定位修订，解决现有空间-文本生成中的交互修订不一致、人机意图不一致和定制粒度不足问题，提升空间精细化调整的精度和用户交互效率。
摘要：
交互式空间布局使用户能够整合信息并组织发现以进行认知处理。尽管大型语言模型（LLMs）可以自动从空间布局生成叙述，但基于拼贴和重生成的方法难以支持认知处理过程中固有的增量空间调整。我们识别出现有空间-文本生成的三个关键缺口：交互修订不一致、人机意图不一致和缺乏细粒度定制。为此，我们引入了语义提示框架，该框架通过感知语义交互、推理解释意图并执行目标定位修订来实现空间调整。我们实现了S-PRISM来实现该框架。实证评估表明，S-PRISM有效提高了交互修订细化的精度。一项用户研究（N=14）展示了参与者如何通过交互引导利用S-PRISM进行增量形式化。结果显示，用户重视其高效、适应性强且可信的支持，这有效加强了人机意图的一致性。
英文摘要：
Interactive spatial layouts empower users to synthesize information and organize findings for sensemaking. While Large Language Models (LLMs) can automate narrative generation from spatial layouts, current collage-based and re-generation methods struggle to support the incremental spatial refinements inherent to the sensemaking process. We identify three critical gaps in existing spatial-textual generation: interaction-revision misalignment, human-LLM intent misalignment, and lack of granular customization. To address these, we introduce Semantic Prompting, a framework for spatial refinement that perceives semantic interactions, reasons about refinement intent, and performs targeted positional revisions. We implemented S-PRISM to realize this framework. The empirical evaluation demonstrated that S-PRISM effectively enhanced the precision of interaction-revision refinement. A user study ($N=14$) highlighted how participants leveraged S-PRISM for incremental formalization through interactive steering. Results showed that users valued its efficient, adaptable, and trustworthy support, which effectively strengthens human-LLM intent alignment.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
