Semantic Prompting: Agentic Incremental Narrative Refinement through Spatial Semantic Interaction

• URL: http://arxiv.org/abs/2604.19971v1
• Date: Tue, 21 Apr 2026 20:28:42 GMT
• Title: Semantic Prompting: Agentic Incremental Narrative Refinement through Spatial Semantic Interaction
• Abstract summary: We introduce Semantic Prompting, a framework for spatial refinement that perceives semantic interactions and performs targeted positional revisions.<n>S-PRISM effectively enhanced the precision of interaction-revision refinement.<n>Results showed that users valued its efficient, adaptable, and trustworthy support, which effectively strengthens human-LLM intent alignment.
• Score: 8.657871568409925
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

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