Multi-Agent Synergy-Driven Iterative Visual Narrative Synthesis

2025-07-17

AI Summary

Existing automated media presentation generation methods often suffer from narrative discontinuity and suboptimal visual layout, failing to meet professional quality standards. To address these issues, we propose RCPS—a reflective, multi-agent framework integrating deep structured narrative planning, adaptive layout generation, and an iterative optimization loop. Furthermore, we introduce PREVAL, a preference-based evaluation system that jointly optimizes content consistency, coherence, and visual design across multiple dimensions. Experimental results demonstrate that RCPS-generated presentations significantly outperform baseline approaches across all quantitative metrics, achieving overall quality comparable to human experts. Crucially, PREVAL’s assessments exhibit strong agreement with human judgments (Spearman’s ρ > 0.92), validating its effectiveness as a reliable, automated quality evaluator.

Technology Category

• Knowledge Representation and Reasoning: Preferences
• Humans and AI: Human-Aware Planning and Behavior Prediction
• Planning, Routing, and Scheduling: Planning with Language Models

Application Category

• User Modeling, Personalization and Recommendation: Personalized content ranking and presentation
• Responsible Web: Human-perceived consequences of algorithmic deployment on the web
• Search and Retrieval-Augmented AI: Web evaluation methodologies and metrics

Abstract

Automated generation of high-quality media presentations is challenging, requiring robust content extraction, narrative planning, visual design, and overall quality optimization. Existing methods often produce presentations with logical inconsistencies and suboptimal layouts, thereby struggling to meet professional standards. To address these challenges, we introduce RCPS (Reflective Coherent Presentation Synthesis), a novel framework integrating three key components: (1) Deep Structured Narrative Planning; (2) Adaptive Layout Generation; (3) an Iterative Optimization Loop. Additionally, we propose PREVAL, a preference-based evaluation framework employing rationale-enhanced multi-dimensional models to assess presentation quality across Content, Coherence, and Design. Experimental results demonstrate that RCPS significantly outperforms baseline methods across all quality dimensions, producing presentations that closely approximate human expert standards. PREVAL shows strong correlation with human judgments, validating it as a reliable automated tool for assessing presentation quality.

Problem

• Automated high-quality media presentation generation challenges
• Logical inconsistencies and suboptimal layouts in presentations
• Lack of reliable automated presentation quality assessment

Innovation

• Deep Structured Narrative Planning
• Adaptive Layout Generation
• Iterative Optimization Loop