Semantic Prompting: Agentic Incremental Narrative Refinement through Spatial Semantic Interaction
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arXiv
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| Autores principales: | , , , , , |
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| Formato: | Preprint |
| Publicado: |
2026
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| _version_ | 1866910156795674624 |
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| author | Tang, Xuxin Tahmid, Ibrahim Krokos, Eric Whitley, Kirsten Wang, Xuan North, Chris |
| author_facet | Tang, Xuxin Tahmid, Ibrahim Krokos, Eric Whitley, Kirsten Wang, Xuan North, Chris |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2604_19971 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Semantic Prompting: Agentic Incremental Narrative Refinement through Spatial Semantic Interaction Tang, Xuxin Tahmid, Ibrahim Krokos, Eric Whitley, Kirsten Wang, Xuan North, Chris Human-Computer Interaction Artificial Intelligence 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. |
| title | Semantic Prompting: Agentic Incremental Narrative Refinement through Spatial Semantic Interaction |
| topic | Human-Computer Interaction Artificial Intelligence |
| url | https://arxiv.org/abs/2604.19971 |