Paper
An AI-Based Structured Semantic Control Model for Stable and Coherent Dynamic Interactive Content Generation
Authors
Rui Liu
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 generation pipeline in which semantic modeling, control signals, and generation strategies work together within one framework. Sensitivity analyses on control vector dimension, hidden layer size, noise intensity, and training sample scale are conducted on a public dialogue dataset to validate the framework. The results show that the approach improves semantic structure, contextual consistency, and controllable expression, providing a structured and effective solution for interactive content generation.
Metadata
Related papers
Vibe Coding XR: Accelerating AI + XR Prototyping with XR Blocks and Gemini
Ruofei Du, Benjamin Hersh, David Li, Nels Numan, Xun Qian, Yanhe Chen, Zhongy... • 2026-03-25
Comparing Developer and LLM Biases in Code Evaluation
Aditya Mittal, Ryan Shar, Zichu Wu, Shyam Agarwal, Tongshuang Wu, Chris Donah... • 2026-03-25
The Stochastic Gap: A Markovian Framework for Pre-Deployment Reliability and Oversight-Cost Auditing in Agentic Artificial Intelligence
Biplab Pal, Santanu Bhattacharya • 2026-03-25
Retrieval Improvements Do Not Guarantee Better Answers: A Study of RAG for AI Policy QA
Saahil Mathur, Ryan David Rittner, Vedant Ajit Thakur, Daniel Stuart Schiff, ... • 2026-03-25
MARCH: Multi-Agent Reinforced Self-Check for LLM Hallucination
Zhuo Li, Yupeng Zhang, Pengyu Cheng, Jiajun Song, Mengyu Zhou, Hao Li, Shujie... • 2026-03-25
Raw Data (Debug)
{
"raw_xml": "<entry>\n <id>http://arxiv.org/abs/2602.22762v1</id>\n <title>An AI-Based Structured Semantic Control Model for Stable and Coherent Dynamic Interactive Content Generation</title>\n <updated>2026-02-26T08:53:35Z</updated>\n <link href='https://arxiv.org/abs/2602.22762v1' rel='alternate' type='text/html'/>\n <link href='https://arxiv.org/pdf/2602.22762v1' rel='related' title='pdf' type='application/pdf'/>\n <summary>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 generation pipeline in which semantic modeling, control signals, and generation strategies work together within one framework. Sensitivity analyses on control vector dimension, hidden layer size, noise intensity, and training sample scale are conducted on a public dialogue dataset to validate the framework. The results show that the approach improves semantic structure, contextual consistency, and controllable expression, providing a structured and effective solution for interactive content generation.</summary>\n <category scheme='http://arxiv.org/schemas/atom' term='cs.HC'/>\n <published>2026-02-26T08:53:35Z</published>\n <arxiv:primary_category term='cs.HC'/>\n <author>\n <name>Rui Liu</name>\n </author>\n </entry>"
}