Semantic Prompting: Agentic Incremental Narrative Refinement through Spatial Semantic Interaction

2026-04-21

AI Summary

Existing approaches that integrate large language models with spatial layouts struggle to support progressive refinement, often suffering from misalignment between user interaction and model revisions, inconsistent human–AI intent, and insufficient fine-grained customization. This work proposes S-PRISM, a semantic prompting framework that uniquely unifies spatial semantic interaction awareness with user intent inference. By introducing a position-aware revision mechanism, S-PRISM enables interactive, incremental, and customizable narrative generation. Experimental results demonstrate that S-PRISM significantly improves revision accuracy and alignment between human and model intent. An empirical study with 14 users further validates that the system effectively supports progressive narrative formalization, exhibiting high flexibility, efficiency, and trustworthiness.

Technology Category

• Humans and AI: Game Design — Procedural Content Generation & Storytelling
• Natural Language Processing: Prompt Engineering / Prompting
• Planning, Routing, and Scheduling: Planning with Language Models

Application Category

• Search and Retrieval-Augmented AI: Assisted, interactive, and conversational search
• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions
• User Modeling, Personalization and Recommendation: User modeling and simulation for interactive and conversational systems

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.

Problem

• spatial refinement
• sensemaking
• human-LLM alignment
• incremental narrative
• semantic interaction

Innovation

• Semantic Prompting
• Spatial Semantic Interaction
• Incremental Narrative Refinement
• Human-LLM Alignment
• Positional Revision