An AI-Based Structured Semantic Control Model for Stable and Coherent Dynamic Interactive Content Generation
Rui Liu

TL;DR
This paper introduces a controllable generation framework that enhances the stability, coherence, and semantic consistency of dynamic interactive content by using structured semantic states and multilevel constraints.
Contribution
It presents a novel structured semantic control model that integrates semantic modeling, control signals, and generation strategies for improved interactive content creation.
Findings
Improves semantic structure and contextual consistency.
Enhances controllable expression in generated content.
Validates effectiveness through sensitivity analyses on a dialogue dataset.
Abstract
This study addresses the challenge that generative models struggle to balance flexibility, stability, and controllability in complex interactive scenarios. It proposes a controllable generation framework for dynamic interactive content construction. The framework builds a structured semantic state space that encodes user input, environmental conditions, and historical context into actionable latent representations and generates directional control vectors to guide the content generation process. It introduces multilevel constraints, including semantic consistency constraints, structural stability constraints, and semantic drift penalties, which help the model maintain clear semantic paths and coherent logic in dynamic environments. These constraints prevent content deviation, unstable tone, or structural breaks. Based on these components, the study designs a systematic controllable…
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
Taxonomy
TopicsMultimodal Machine Learning Applications · Artificial Intelligence in Games · Topic Modeling
