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Related Experiment Video

Updated: Jul 1, 2026

Virtual Agent for Real-Time Motivational Interviewing by Integrating Adaptive Nonverbal Behavior and Language Models
07:14

Virtual Agent for Real-Time Motivational Interviewing by Integrating Adaptive Nonverbal Behavior and Language Models

Published on: December 23, 2025

Interactive recommendation algorithm based on intelligent assistance for project application.

Changcheng Shao1, Qianyu Zou2, Zhouqiang Qiu3

  • 1School of Management, Guangdong University of Technology, Guangzhou, 510520, Guangdong, China.

Scientific Reports
|June 29, 2026
PubMed
Summary
This summary is machine-generated.

This study introduces an intelligent assistant for project applications, integrating Large Language Models (LLMs) and knowledge graphs to improve guideline understanding and material preparation. The model enhances efficiency and accuracy in project application management.

Keywords:
Intelligent project applicationInteractive recommendationKnowledge graphLarge language modelReinforcement learning

Related Experiment Videos

Last Updated: Jul 1, 2026

Virtual Agent for Real-Time Motivational Interviewing by Integrating Adaptive Nonverbal Behavior and Language Models
07:14

Virtual Agent for Real-Time Motivational Interviewing by Integrating Adaptive Nonverbal Behavior and Language Models

Published on: December 23, 2025

Area of Science:

  • Artificial Intelligence
  • Information Science
  • Computer Science

Background:

  • Project application management faces challenges with guideline interpretation, material standardization, and review efficiency.
  • Existing AI assistants often suffer from knowledge gaps or lack interactive capabilities.

Purpose of the Study:

  • To develop a novel Knowledge Graph-Enhanced Interactive Recommendation Intelligent Assistant Model for Project Applications.
  • To address limitations of current LLM-based and KG/RL-based systems in domain knowledge and interaction.

Main Methods:

  • Integration of a domain-specific project application knowledge graph (PAG-KG) to mitigate LLM hallucinations.
  • An LLM decision brain with plan-first execution and Actor-Critic reflection for natural language interaction and task orchestration.
  • A knowledge graph-enhanced Dueling Deep Q-Network (KG-DQN) for optimizing long-term decision-making strategies.

Main Results:

  • The proposed model significantly outperforms baseline models in guideline understanding, material preparation completeness, and formal review accuracy.
  • Demonstrated excellent adaptability in complex multi-turn interactions and strong generalization capabilities.
  • Substantially reduced application time and manual intervention, enhancing management efficiency.

Conclusions:

  • The three-in-one integration offers full-process intelligent assistance for project applications, a novel achievement.
  • The model effectively enhances the efficiency and intelligence of project application management.
  • Presents a robust solution for complex, multi-turn interactive decision-making scenarios in specialized domains.