arxiv: 2510.05188 · v4 · submitted 2025-10-06 · 💻 cs.AI

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

Wenda Xie , Chao Guo , Yanqing Jing , Junle Wang , Yisheng Lv , Fei-Yue Wang This is my paper

Pith reviewed 2026-05-18 10:44 UTC · model grok-4.3

classification 💻 cs.AI

keywords LLM agentsnarrative script refinementdivide-and-conqueriterative revisioncollaborative agentsscript quality improvementhierarchical reviewplug-and-play

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The pith

Dramaturge coordinates multiple LLM agents in a top-down workflow to iteratively fix both global structure and local details in long narrative scripts.

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

Single-pass LLM generation often leaves long scripts with structural problems and inconsistent local edits because the model cannot track the full context at once. The paper presents Dramaturge as a divide-and-conquer system that first reviews the entire storyline for big-picture issues, then examines individual scenes for sentence-level flaws, and finally applies coordinated revisions that let high-level plans control local changes. The workflow repeats in coarse-to-fine rounds until further gains stop. Experiments indicate the method raises both script-level quality and scene-level detail above all tested baselines while remaining easy to add to existing generators.

Core claim

Dramaturge is a task- and feature-oriented framework of hierarchical LLM agents that performs a Global Review to identify storyline and structural problems, a Scene-level Review to locate detailed flaws, and a Hierarchical Coordinated Revision stage that integrates the two scales of fixes in a top-down flow, repeating the cycle until no substantive improvements remain.

What carries the argument

The Hierarchical Coordinated Revision stage, which translates global structural strategies into consistent local edits across scenes.

If this is right

Global review findings directly constrain scene-level edits so that local changes remain aligned with overall narrative goals.
The iterative loop stops only when no further substantive improvements can be identified, producing progressively refined scripts.
The plug-and-play design allows the three-stage workflow to be inserted into other LLM script generators without retraining.
Separate review stages for structure and detail reduce the inconsistencies that arise from direct multi-granularity edits.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

The same staged separation of global and local analysis could be tested on other long-form tasks such as novel chapter revision or technical report editing.
Multi-agent coordination of this form may lower the amount of human post-editing required for creative writing pipelines.
The coarse-to-fine stopping rule offers a concrete way to decide when an automated revision process has reached diminishing returns.

Load-bearing premise

The top-down task flow ensures that high-level strategies guide local modifications while maintaining contextual consistency.

What would settle it

Run the full Dramaturge pipeline on a test script and measure whether any round of coordinated revision introduces new cross-scene contradictions or fails to raise human-rated quality scores above a single-pass baseline.

Figures

Figures reproduced from arXiv: 2510.05188 by Chao Guo, Fei-Yue Wang, Junle Wang, Wenda Xie, Yanqing Jing, Yisheng Lv.

**Figure 1.** Figure 1: Our Dramaturge is inspired by the human scriptwriting process and performs Global Review, Scenelevel Review, and Hierarchical Coordinated Revision to iteratively refine narrative scripts via a task and feature oriented divide-and-conquer strategy. et al. 2024a; Marco et al. 2024). This discrepancy highlights the importance of script refinement—a critical yet underexplored area in LLM-based creative writ… view at source ↗

**Figure 2.** Figure 2: The Architecture of Dramaturge. A task and feature oriented divide-and-conquer strategy is adopted, leveraging [PITH_FULL_IMAGE:figures/full_fig_p003_2.png] view at source ↗

**Figure 3.** Figure 3: Enhancement in character development. Dramaturge introduces internal conflict and a subplot for Ron, transforming [PITH_FULL_IMAGE:figures/full_fig_p007_3.png] view at source ↗

**Figure 4.** Figure 4: Enhancement in narrative structure. Dramaturge [PITH_FULL_IMAGE:figures/full_fig_p007_4.png] view at source ↗

**Figure 5.** Figure 5: Enhancement of scene presentation. Dramaturge introduces atmospheric intensification and character-environment [PITH_FULL_IMAGE:figures/full_fig_p008_5.png] view at source ↗

**Figure 6.** Figure 6: Enhancement of dialogue quality. Enhancement of [PITH_FULL_IMAGE:figures/full_fig_p008_6.png] view at source ↗

**Figure 7.** Figure 7: Distribution of scores for script-level overall evaluation and scene-level comparative evaluation across all datasets. [PITH_FULL_IMAGE:figures/full_fig_p009_7.png] view at source ↗

**Figure 8.** Figure 8: Ablation study shows the effectiveness of multi [PITH_FULL_IMAGE:figures/full_fig_p009_8.png] view at source ↗

**Figure 9.** Figure 9: Our method shows significant and continuous im [PITH_FULL_IMAGE:figures/full_fig_p009_9.png] view at source ↗

read the original abstract

Although LLMs have been widely adopted for creative content generation, a single-pass process often struggles to produce high-quality long narratives. How to effectively revise and improve long narrative scripts like scriptwriters remains a significant challenge, as it demands a comprehensive understanding of the entire context to identify global structural issues and local detailed flaws, as well as coordinating revisions at multiple granularities and locations. Direct modifications by LLMs typically introduce inconsistencies between local edits and the overall narrative requirements. To address these issues, we propose Dramaturge, a task and feature oriented divide-and-conquer approach powered by hierarchical multiple LLM agents. It consists of a Global Review stage to grasp the overall storyline and structural issues, a Scene-level Review stage to pinpoint detailed scene and sentence flaws, and a Hierarchical Coordinated Revision stage that coordinates and integrates structural and detailed improvements throughout the script. The top-down task flow ensures that high-level strategies guide local modifications, maintaining contextual consistency. The review and revision workflow follows a coarse-to-fine iterative process, continuing through multiple rounds until no further substantive improvements can be made. Comprehensive experiments show that Dramaturge significantly outperforms all baselines in terms of script-level overall quality and scene-level details. Our approach is plug-and-play and can be easily integrated into existing methods to improve the generated scripts.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Desk Editor's Note private letter to a colleague

Dramaturge gives a clear hierarchical multi-agent workflow for refining narrative scripts, but the reported gains may trace to extra LLM calls rather than the divide-and-conquer structure itself.

read the letter

The paper's main contribution is a three-stage process: a global review to catch overall storyline problems, a scene-level review for local flaws, and a hierarchical coordination step that applies top-down fixes while trying to keep consistency. The workflow runs iteratively until no more big changes appear, and it is presented as plug-and-play on top of existing generators. That combination of stages for script work is a reasonable extension of existing multi-agent patterns, and the emphasis on maintaining narrative coherence through the top-down flow is a practical design choice that addresses a real pain point in long-form generation. The claim that it can be dropped into other methods without much friction is also useful if it holds up in practice. The experiments are described as showing clear wins on script-level quality and scene details over baselines, which would matter for anyone building tools in entertainment or education. The soft spot is the missing detail on controls. The abstract does not report how many rounds or total calls the baselines received, so it is hard to separate the effect of the architecture from simply giving the system more refinement opportunities. If the full paper lacks compute-matched ablations or per-method token counts, that undercuts the central empirical claim. There is no new theory or formal derivation here, just an engineering workflow, and the citations follow the usual multi-agent LLM references without obvious gaps. This is for applied researchers who want concrete patterns for improving LLM story output rather than core theory advances. A reader focused on practical multi-agent setups for creative text would find the workflow description worth a look. I would bring it to a reading group to talk through the experimental design. I would not cite it in my own work in the next year unless the full results include strong ablations. It deserves peer review so the controls and reproducibility can be checked properly.

Referee Report

2 major / 1 minor

Summary. The paper proposes Dramaturge, a plug-and-play divide-and-conquer framework using hierarchical collaborative LLM agents to iteratively refine long narrative scripts. It consists of a Global Review stage to identify overall storyline and structural issues, a Scene-level Review stage to detect detailed scene and sentence flaws, and a Hierarchical Coordinated Revision stage that integrates improvements in a top-down manner to preserve contextual consistency. The process iterates in a coarse-to-fine manner across multiple rounds until no further substantive changes are identified. The central claim is that this architecture yields significant improvements in script-level overall quality and scene-level details over baselines, and that the method can be integrated into existing generation pipelines.

Significance. If the outperformance is shown to arise specifically from the hierarchical coordination rather than increased iteration count, the work would offer a practical, modular approach to addressing global-local consistency issues in LLM-based long-form narrative generation. The plug-and-play design and explicit separation of global strategy from local edits are useful engineering contributions that could be adopted in creative writing assistants. The iterative coarse-to-fine workflow is a reasonable response to the limitations of single-pass generation.

major comments (2)

[Abstract] Abstract: the claim that 'Dramaturge significantly outperforms all baselines in terms of script-level overall quality and scene-level details' is presented without any description of the experimental design, choice of baselines, evaluation metrics, number of scripts tested, or statistical significance tests. This absence makes it impossible to assess whether the central empirical result is supported by the manuscript.
[Hierarchical Coordinated Revision stage] Hierarchical Coordinated Revision stage (and workflow description): the process is described as continuing 'through multiple rounds until no further substantive improvements can be made,' which necessarily entails a variable and potentially larger number of LLM calls and token budget than single-pass or fixed-iteration baselines. No ablation or reporting of average LLM usage per method is mentioned, so any quality gains could be explained by extra refinement opportunities rather than the divide-and-conquer structure or top-down consistency enforcement.

minor comments (1)

[Abstract] The abstract would be clearer if it included a brief forward reference to the section that presents the experimental protocol and results.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for the constructive feedback and for recognizing the potential practical value of the plug-and-play hierarchical workflow. We address each major comment below and will revise the manuscript to strengthen the presentation of the empirical results and the analysis of computational cost.

read point-by-point responses

Referee: [Abstract] Abstract: the claim that 'Dramaturge significantly outperforms all baselines in terms of script-level overall quality and scene-level details' is presented without any description of the experimental design, choice of baselines, evaluation metrics, number of scripts tested, or statistical significance tests. This absence makes it impossible to assess whether the central empirical result is supported by the manuscript.

Authors: We agree that the abstract would be more informative if it briefly contextualized the empirical claims. In the revised version we will expand the abstract to mention the evaluation metrics (script-level and scene-level quality scores), the set of baselines, the number of test scripts, and the use of statistical significance testing. Full experimental details remain in Section 4, but the abstract will now provide sufficient information for readers to assess the central result. revision: yes
Referee: [Hierarchical Coordinated Revision stage] Hierarchical Coordinated Revision stage (and workflow description): the process is described as continuing 'through multiple rounds until no further substantive improvements can be made,' which necessarily entails a variable and potentially larger number of LLM calls and token budget than single-pass or fixed-iteration baselines. No ablation or reporting of average LLM usage per method is mentioned, so any quality gains could be explained by extra refinement opportunities rather than the divide-and-conquer structure or top-down consistency enforcement.

Authors: This observation is correct and highlights an important point. The iterative coarse-to-fine process can indeed consume a variable number of LLM calls. To demonstrate that gains arise from the hierarchical coordination rather than simply additional iterations, we will add (1) a table reporting average LLM calls and token usage for Dramaturge versus each baseline and (2) an ablation that compares the full iterative workflow against fixed-round variants. These additions will appear in the revised experimental section. revision: yes

Circularity Check

0 steps flagged

No circularity: empirical engineering method without derivation chain

full rationale

The paper proposes an empirical divide-and-conquer workflow (Global Review, Scene-level Review, Hierarchical Coordinated Revision) implemented via LLM agents, with iterative refinement until no further improvements. It reports experimental outperformance on script quality metrics. No equations, fitted parameters, or self-referential definitions appear in the method description or claims. The central results rest on external benchmarks and comparisons rather than reducing to inputs by construction or self-citation load-bearing steps. This is a standard self-contained empirical contribution.

Axiom & Free-Parameter Ledger

0 free parameters · 2 axioms · 0 invented entities

The method rests on domain assumptions about LLM capabilities for context understanding and coordination; no free parameters or invented entities are explicitly introduced in the abstract.

axioms (2)

domain assumption LLMs can grasp overall storyline and structural issues in a full script during the Global Review stage
This capability is required for the first stage to function as described.
domain assumption Hierarchical coordination can integrate structural and detailed improvements without introducing new inconsistencies
This is invoked to justify the top-down flow in the Hierarchical Coordinated Revision stage.

pith-pipeline@v0.9.0 · 5782 in / 1339 out tokens · 36623 ms · 2026-05-18T10:44:31.129077+00:00 · methodology

discussion (0)

Reference graph

Works this paper leans on

121 extracted references · 121 canonical work pages

[1]

arXiv preprint arXiv:2504.15552

A Multi-Agent Framework for Automated Qinqiang Opera Script Generation Using Large Language Models. arXiv preprint arXiv:2504.15552. Chen, G.; Dong, S.; Shu, Y .; Zhang, G.; Sesay, J.; Karls- son, B.; Fu, J.; and Shi, Y . 2024a. AutoAgents: A Frame- work for Automatic Agent Generation. InProceedings of the Thirty-Third International Joint Conference on Ar...

work page arXiv 2024
[2]

Mirowski, P.; Mathewson, K

Pron vs Prompt: Can Large Language Models already Challenge a World-Class Fiction Author at Creative Text Writing? InProceedings of the 2024 Conference on Em- pirical Methods in Natural Language Processing, 19654– 19670. Mirowski, P.; Mathewson, K. W.; Pittman, J.; and Evans, R. 2023. Co-writing Screenplays and Theatre Scripts with Language Models: Evalua...

work page arXiv 2024
[3]

suggestion

BOOKWORLD: From Novels to Interactive Agent Societies for Story Creation. InProceedings of the 63rd An- nual Meeting of the Association for Computational Linguis- tics (Volume 1: Long Papers), 15898–15912. Shao, Y .; Jiang, Y .; Kanell, T.; Xu, P.; Khattab, O.; and Lam, M. 2024. Assisting in Writing Wikipedia-like Articles From Scratch with Large Language...

work page arXiv 2024
[4]

Provide EXACTLY 1-2 bold, creative suggestions that will significantly enhance the script

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[5]

Each suggestion should be truly innovative and make the story more exciting

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[6]

Consider adding unexpected plot twists, new characters, surprising revelations, or unique narrative devices

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Make bold innovations while maintaining the original world setting of the script

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[8]

Focus on adding more interesting plots, unexpected twists

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[9]

Your suggestions should transform the script into something truly memorable and distinctive

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[10]

suggestion

DO NOT provide more than 2 suggestions REMEMBER: Your entire response must be ONLY the JSON object above, nothing else! Table 4: Prompt used for theEngagement Evaluator. Character Evaluator Prompt: Now that you have received all [total scenes] scenes of the script [title], please provide your complete analysis. You are an expert script analyst focused on ...

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[11]

Provide EXACTLY 1-2 bold, creative suggestions that will significantly enhance the characters

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[12]

Each suggestion should make the characters more complex, relatable, and compelling

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[13]

Consider adding unexpected character revelations, deeper internal conflicts, or surprising relationship dynamics

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[14]

Focus on character transformations that will create more emotional impact

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[15]

Your suggestions should make the characters truly memorable and distinctive

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[16]

suggestion

DO NOT provide more than 2 suggestions REMEMBER: Your entire response must be ONLY the JSON object above, nothing else! Table 5: Prompt used for theCharacter Evaluator. Theme Evaluator Prompt: Now that you have received all [total scenes] scenes of the script [title], please provide your complete analysis. You are an expert script analyst focused on thema...

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[17]

Provide EXACTLY 1-2 bold, creative suggestions that will significantly enhance the script’s themes

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[18]

Each suggestion should add depth, meaning, and emotional resonance to the story

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[19]

Consider adding powerful symbolic elements, thematic parallels, or emotional set pieces

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[20]

Focus on thematic innovations that will create more profound meaning

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[21]

Your suggestions should make the script’s themes more impactful and memorable

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[22]

suggestion

DO NOT provide more than 2 suggestions REMEMBER: Your entire response must be ONLY the JSON object above, nothing else! Table 6: Prompt used for theTheme Evaluator. Narrative Evaluator Prompt: Now that you have received all [total scenes] scenes of the script [title], please provide your complete analysis. You are an expert script analyst focused on narra...

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[23]

Provide EXACTLY 1-2 bold, creative suggestions that will significantly enhance the script’s narrative

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[24]

Each suggestion should improve the story’s pacing, structure, or flow

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[25]

Consider adding narrative devices like non-linear storytelling, parallel plotlines, or dramatic reveals

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[26]

Focus on narrative innovations that will make the story more compelling

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[27]

Your suggestions should make the script’s structure more engaging and effective

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[28]

Global Review Integrator Prompt: You are an expert script consultant who specializes in organizing high-level script suggestions into scene-specific, actionable recommendations

DO NOT provide more than 2 suggestions REMEMBER: Your entire response must be ONLY the JSON object above, nothing else! Table 7: Prompt used for theNarrative Evaluator. Global Review Integrator Prompt: You are an expert script consultant who specializes in organizing high-level script suggestions into scene-specific, actionable recommendations. Script Sum...

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[29]

Analyze all inspector suggestions for potential conflicts or contradictions

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[31]

Resolve any contradictions by prioritizing based on scene context and narrative importance

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[32]

Synthesize discrete feedback points into unified improvement themes

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[33]

Ensure all final suggestions are mutually reinforcing rather than competing

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[35]

integrated suggestions

Maintain consistency with the overall scene’s narrative function Integration Guidelines: - Focus on creating synergy between different aspects (dialogue, plot, consistency, description) - Prioritize suggestions that address multiple dimensions simultaneously - Ensure character consistency suggestions align with dialogue improvements - Make sure plot sugge...

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[38]

Break these down into specific, actionable suggestions for this scene’s dialogue

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[41]

Focus on:

For each suggestion, reference both the Global Review suggestion and Storyline Editor implementation Analyze the dialogue in this scene. Focus on:

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Dialogue authenticity and naturalness

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Character voice and distinctiveness

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Dialogue flow and pacing

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Plot advancement through dialogue

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Character development through dialogue

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suggestions

Emotional impact and tension in dialogue Your analysis should be specific to dialogue only, as this is the primary carrier of plot in the scenes. Return your detailed analysis in JSON format: { "suggestions": [ { "global review reference": "Title of the Global Review suggestion you’re refining", "beat update reference": "Specific implementation from Story...

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[50]

Break these down into specific, actionable suggestions for this scene’s structure and plot

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[53]

Focus on:

For each suggestion, reference both the Global Review suggestion and Storyline Editor implementation Analyze the scene structure, coherence, and plot issues. Focus on:

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[54]

Scene pacing and rhythm

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[55]

Internal scene logic and consistency

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[56]

Scene beats and progression

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Scene tension and release

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[58]

Plot holes and logical inconsistencies

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[59]

Implausible events or coincidences

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Unresolved plot threads

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suggestions

Weak narrative points Your analysis should provide a comprehensive evaluation of both scene structure and plot issues. Return your detailed analysis in JSON format: { "suggestions": [ { "global review reference": "Title of the Global Review suggestion you’re refining", "beat update reference": "Specific implementation from Storyline Editor (if available)"...

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[64]

Break these down into specific, actionable suggestions for character consistency in this scene

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[67]

Focus on:

For each suggestion, reference both the Global Review suggestion and Storyline Editor implementation Analyze the character consistency in this scene. Focus on:

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[68]

Character behavior consistency with established traits

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[69]

Character motivation clarity and consistency

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[70]

Character development progression

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[71]

Character reactions and decisions

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[72]

suggestions

Character relationships consistency Your analysis should be specific to character consistency only. Return your detailed analysis in JSON format: { "suggestions": [ { "global review reference": "Title of the Global Review suggestion you’re refining", "beat update reference": "Specific implementation from Storyline Editor (if available)", "issue": "Detaile...

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[73]

First, understand the high-level suggestions from Global Review

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[74]

Then, examine the Storyline Editor’s scene-specific implementations

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[75]

Break these down into specific, actionable suggestions for this scene’s description

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[76]

DO NOT add new issues that weren’t identified by Global Review or Storyline Editor

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[77]

Make sure your suggestions are specific to this scene and its context

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[78]

Focus on:

For each suggestion, reference both the Global Review suggestion and Storyline Editor implementation Analyze the scene description in this scene. Focus on:

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[79]

Sensory details (visual, auditory, tactile, olfactory, gustatory)

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[80]

Setting and atmosphere

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[81]

Physical environment

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[82]

Character physicality and non-verbal communication

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[83]

Pacing through descriptive elements

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[84]

suggestions

Balance between description and action Your analysis should be specific to scene description elements only. Return your detailed analysis in JSON format: { "suggestions": [ { "global review reference": "Title of the Global Review suggestion you’re refining", "beat update reference": "Specific implementation from Storyline Editor (if available)", "issue": ...

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[85]

Analyze all agent suggestions for potential conflicts or contradictions

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[86]

Identify and eliminate duplicate or overlapping recommendations

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[87]

Resolve contradictions by prioritizing based on scene context and narrative importance

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[88]

Synthesize discrete suggestions into unified improvement themes

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[89]

Ensure final suggestions are mutually reinforcing rather than competing

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[90]

Create clear implementation priorities for the scene

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[91]

integrated suggestions

Maintain consistency with the scene’s narrative function and context Integration Guidelines: - Focus on creating synergy between dialogue, scene structure, character consistency, and description - Prioritize suggestions that address multiple agent concerns simultaneously - Ensure character consistency suggestions align with dialogue improvements - Make su...

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[92]

Maintains the original world setting and core characters

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[93]

Adds unexpected but logical plot developments

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[94]

Deepens character motivations in key scenes

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