Plug-and-Play Dramaturge: A Divide-and-Conquer Approach for Iterative Narrative Script Refinement via Collaborative LLM Agents
- URL: http://arxiv.org/abs/2510.05188v1
- Date: Mon, 06 Oct 2025 05:20:37 GMT
- Title: Plug-and-Play Dramaturge: A Divide-and-Conquer Approach for Iterative Narrative Script Refinement via Collaborative LLM Agents
- Abstract summary: Dramaturge is a task and feature oriented divide-and-conquer approach powered by hierarchical multiple LLM agents.<n>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.<n>Our approach is plug-and-play and can be easily integrated into existing methods to improve the generated scripts.
- Score: 23.1768597825114
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- 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.
Related papers
- Better Call CineCrew: Consistent Ultra-Long Narrative-to-Film Generation [53.27387848819395]
Long-form narrative-to-film generation requires shot-level controllability and cross-clip consistency.<n>We introduce a structured orchestration layer for film-oriented script-to-video generation.
arXiv Detail & Related papers (2026-09-07T16:33:14Z) - Thinking on Shots: Consistent Multi-Shot Video Editing with Agentic Reasoning [56.17582599802973]
We introduce the Multi-Instruction Multi-Shot Long-Video Editing (MMLVE) task, which is structured around three core objectives.<n>To tackle these three unique challenges, we introduce an agentic editing framework that leverages the Large Language Models (LLMs) and Vision-Language Models (VLMs)<n>Extensive experiments demonstrate that our MMLVE-Agent outperforms existing closed-source SOTA approaches.
arXiv Detail & Related papers (2026-08-27T08:48:13Z) - One Sentence, One Drama: Personalized Short-Form Drama Generation via Multi-Agent Systems [17.754369892506542]
One Sentence, One Drama is a hierarchical multi-agent framework that transforms a user's single-sentence idea into a fully produced short drama.<n>Our approach is built upon three key components: (1) a multi-agent debate-based story generation module that enforces short-drama pacing and narrative coherence; (2) a 3D-grounded first-frame generation mechanism that establishes a shared spatial reference for consistent character positioning and scene layout across clips; and (3) multi-stage reviewer loops that perform comprehensive error detection and targeted revision across script, visual, and video generation stages.
arXiv Detail & Related papers (2026-05-21T08:15:46Z) - LiveClawBench: Benchmarking LLM Agents on Complex, Real-World Assistant Tasks [58.3639630490749]
We introduce LiveClawBench, a benchmark to evaluate LLM agents on real-world assistant tasks.<n>Based on an analysis of various realClaw usage cases, we derive a Triple-Axis Complexity Framework.<n>We construct a pilot benchmark with explicit complexity-factor annotations, covering real-world assistant tasks.
arXiv Detail & Related papers (2026-03-20T16:08:21Z) - Hierarchical Long Video Understanding with Audiovisual Entity Cohesion and Agentic Search [61.88597038104749]
We present HAVEN, a unified framework for long-video understanding that enables coherent and comprehensive reasoning.<n>We preserve semantic consistency by integrating entity-level representations across visual and auditory streams.<n>We employ an agentic search mechanism to enable dynamic retrieval and reasoning across these layers.
arXiv Detail & Related papers (2026-01-20T08:23:29Z) - Connecting the Dots: Training-Free Visual Grounding via Agentic Reasoning [63.109585527799005]
GroundingAgent is a visual grounding framework that operates without task-specific fine-tuning.<n>It achieves an average zero-shot grounding accuracy of 65.1 % on widely-used benchmarks.<n>It also offers strong interpretability, transparently illustrating each reasoning step.
arXiv Detail & Related papers (2025-11-24T03:11:08Z) - CML-Bench: A Framework for Evaluating and Enhancing LLM-Powered Movie Scripts Generation [66.24195604846103]
We propose the CML-Bench, featuring quantitative metrics across Dialogue Coherence (DC), Character Consistency (CC), and Plot Reasonableness (PR)<n>CML-Bench effectively assigns high scores to well-crafted, human-written scripts.<n>To further validate our benchmark, we introduce CML-Instruction, a prompting strategy with detailed instructions on character dialogue and event logic.
arXiv Detail & Related papers (2025-10-01T08:03:26Z) - LLM-as-classifier: Semi-Supervised, Iterative Framework for Hierarchical Text Classification using Large Language Models [0.0]
Large Language Models (LLMs) have provided unprecedented capabilities for analyzing unstructured text data.<n>Standard fine-tuning approaches can be resource-intensive and often struggle with the dynamic nature of real-world data distributions.
arXiv Detail & Related papers (2025-08-22T15:47:17Z) - ToolACE-MT: Non-Autoregressive Generation for Agentic Multi-Turn Interaction [84.90394416593624]
Agentic task-solving with Large Language Models (LLMs) requires multi-turn, multi-step interactions.<n>Existing simulation-based data generation methods rely heavily on costly autoregressive interactions between multiple agents.<n>We propose a novel Non-Autoregressive Iterative Generation framework, called ToolACE-MT, for constructing high-quality multi-turn agentic dialogues.
arXiv Detail & Related papers (2025-08-18T07:38:23Z) - CoDial: Interpretable Task-Oriented Dialogue Systems Through Dialogue Flow Alignment [14.098358830189959]
We introduce a novel framework, CoDial, which converts a TOD task schema to programmatic guardrailing code, such as NVIDIA's Colang.<n>CoDial achieves state-of-the-art (SOTA) performance on the widely used STAR dataset and is on par with SOTA on the MultiWOZ dataset.
arXiv Detail & Related papers (2025-06-02T21:12:27Z) - NexusSum: Hierarchical LLM Agents for Long-Form Narrative Summarization [0.0]
We introduce NexusSum, a multi-agent LLM framework for narrative summarization.<n>A narrative-specific preprocessing method standardizes character dialogue and descriptive text into a unified format.<n>Our method establishes a new state-of-the-art in narrative summarization, achieving up to a 30.0% improvement in BERTScore (F1) across books, movies, and TV scripts.
arXiv Detail & Related papers (2025-05-30T13:26:23Z) - Collaborative Temporal Consistency Learning for Point-supervised Natural Language Video Localization [129.43937834515688]
We propose a new COllaborative Temporal consistEncy Learning (COTEL) framework to strengthen the video-language alignment.<n>Specifically, we first design a frame- and a segment-level Temporal Consistency Learning (TCL) module that models semantic alignment across frame saliencies and sentence-moment pairs.
arXiv Detail & Related papers (2025-03-22T05:04:12Z) - SagaLLM: Context Management, Validation, and Transaction Guarantees for Multi-Agent LLM Planning [2.1331883629523634]
SagaLLM is a structured multi-agent architecture designed to address four foundational limitations of current LLM-based planning systems.<n>It bridges this gap by integrating the Saga transactional pattern with persistent memory, automated compensation, and independent validation agents.<n>It achieves significant improvements in consistency, validation accuracy, and adaptive coordination under uncertainty.
arXiv Detail & Related papers (2025-03-15T01:43:03Z) - Meta-Chunking: Learning Text Segmentation and Semantic Completion via Logical Perception [10.614437503578856]
This paper proposes the Meta-Chunking framework, which specifically enhances chunking quality.<n>We design two adaptive chunking techniques based on uncertainty, namely Perplexity Chunking and Margin Sampling Chunking.<n>We establish the global information compensation mechanism, encompassing a two-stage hierarchical summary generation process and a three-stage text chunk rewriting procedure.
arXiv Detail & Related papers (2024-10-16T17:59:32Z) - LLM Blueprint: Enabling Text-to-Image Generation with Complex and
Detailed Prompts [60.54912319612113]
Diffusion-based generative models have significantly advanced text-to-image generation but encounter challenges when processing lengthy and intricate text prompts.
We present a novel approach leveraging Large Language Models (LLMs) to extract critical components from text prompts.
Our evaluation on complex prompts featuring multiple objects demonstrates a substantial improvement in recall compared to baseline diffusion models.
arXiv Detail & Related papers (2023-10-16T17:57:37Z) - Screenplay Summarization Using Latent Narrative Structure [78.45316339164133]
We propose to explicitly incorporate the underlying structure of narratives into general unsupervised and supervised extractive summarization models.
We formalize narrative structure in terms of key narrative events (turning points) and treat it as latent in order to summarize screenplays.
Experimental results on the CSI corpus of TV screenplays, which we augment with scene-level summarization labels, show that latent turning points correlate with important aspects of a CSI episode.
arXiv Detail & Related papers (2020-04-27T11:54:19Z) - A Local-to-Global Approach to Multi-modal Movie Scene Segmentation [95.34033481442353]
We build a large-scale video dataset MovieScenes, which contains 21K annotated scene segments from 150 movies.
We propose a local-to-global scene segmentation framework, which integrates multi-modal information across three levels, i.e. clip, segment, and movie.
Our experiments show that the proposed network is able to segment a movie into scenes with high accuracy, consistently outperforming previous methods.
arXiv Detail & Related papers (2020-04-06T13:58:08Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.