Plug-and-Play Dramaturge: A Divide-and-Conquer Approach for Iterative Narrative Script Refinement via Collaborative LLM Agents

Researchers propose Dramaturge, a multi-agent LLM system that uses hierarchical divide-and-conquer methodology to iteratively refine narrative scripts. The approach addresses limitations in single-pass LLM generation by coordinating global structural reviews with scene-level refinements across multiple iterations, demonstrating superior output quality compared to baseline methods.

Dramaturge represents a meaningful advancement in applying collaborative AI agents to complex creative tasks that require maintaining coherence across multiple scales of analysis. The research tackles a genuine problem in AI-generated content: single-pass LLM outputs frequently contain structural inconsistencies and local flaws that persist despite the model's training. By decomposing the revision task into hierarchical stages—global review, scene-level analysis, and coordinated refinement—the system mirrors how professional screenwriters actually work, prioritizing high-level narrative architecture before addressing granular details.

This development reflects broader trends in AI agent research toward specialized, multi-step problem-solving frameworks rather than monolithic single-model approaches. The plug-and-play architecture suggests the methodology could generalize beyond screenwriting to other domains requiring iterative refinement of long-form content, including technical documentation, code review, and academic writing. The hierarchical constraint that top-down decisions guide local modifications directly addresses the coherence problem that plagues current LLM systems when editing extended documents.

For the AI development community, this work validates that task-specific agent coordination can significantly improve output quality without requiring larger or more capable base models. The approach suggests efficiency gains through better utilization of existing LLM capabilities rather than scaling. For content creators and studios, the potential integration into production pipelines could reduce manual revision time, though human oversight remains necessary. The research demonstrates that AI's limitations in long-context reasoning may be addressable through architectural innovation rather than waiting for fundamental model improvements.

• →Dramaturge uses hierarchical multi-agent coordination to refine long narratives across global, scene, and sentence levels
• →The divide-and-conquer approach prevents inconsistencies by ensuring high-level structural decisions guide local edits
• →Iterative coarse-to-fine refinement continues until no substantive improvements remain
• →The plug-and-play design enables integration into existing LLM pipelines without architectural changes
• →Results significantly outperform baseline methods on both script-level quality and scene-level detail metrics