MUSE: A Multi-agent Framework for Unconstrained Story Envisioning via Closed-Loop Cognitive Orchestration

2026-02-03

AI Summary

This work addresses narrative defocus in long-form audiovisual story generation caused by semantic drift and character inconsistency by proposing the first multi-agent storytelling framework based on closed-loop cognitive coordination. The approach formulates story generation as a constraint satisfaction problem, employing an iterative planning–execution–verification–correction mechanism that integrates explicit machine-executable controls—such as identity preservation, spatial composition, and temporal continuity—with cross-modal feedback to maintain high-level narrative intent over extended durations. Additionally, the study introduces MUSEBench, a reference-free, open-ended evaluation protocol. Experimental results demonstrate that the proposed method significantly outperforms existing baselines in long-horizon narrative coherence, cross-modal identity consistency, and cinematic visual quality.

Technology Category

• Humans and AI: Game Design — Procedural Content Generation & Storytelling
• Multiagent Systems: Coordination and Collaboration
• Natural Language Processing: Generation

Application Category

• Responsible Web: Machine-in-the-loop, human agency and autonomy
• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions
• Search and Retrieval-Augmented AI: Agentic search

Abstract

Generating long-form audio-visual stories from a short user prompt remains challenging due to an intent-execution gap, where high-level narrative intent must be preserved across coherent, shot-level multimodal generation over long horizons. Existing approaches typically rely on feed-forward pipelines or prompt-only refinement, which often leads to semantic drift and identity inconsistency as sequences grow longer. We address this challenge by formulating storytelling as a closed-loop constraint enforcement problem and propose MUSE, a multi-agent framework that coordinates generation through an iterative plan-execute-verify-revise loop. MUSE translates narrative intent into explicit, machine-executable controls over identity, spatial composition, and temporal continuity, and applies targeted multimodal feedback to correct violations during generation. To evaluate open-ended storytelling without ground-truth references, we introduce MUSEBench, a reference-free evaluation protocol validated by human judgments. Experiments demonstrate that MUSE substantially improves long-horizon narrative coherence, cross-modal identity consistency, and cinematic quality compared with representative baselines.

Problem

• story generation
• narrative coherence
• identity consistency
• multimodal generation
• long-horizon storytelling

Innovation

• multi-agent framework
• closed-loop orchestration
• long-horizon storytelling
• multimodal consistency
• reference-free evaluation