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ComposerX: Multi-Agent Symbolic Music Composition with LLMs
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Music composition represents the creative side of humanity, and itself is a complex task that requires abilities to understand and generate information with long dependency and harmony constraints. While demonstrating impressive capabilities in STEM subjects, current LLMs easily fail in this task, generating ill-written music even when equipped with modern techniques like In-Context-Learning and Chain-of-Thoughts. To further explore and enhance LLMs' potential in music composition by leveraging their reasoning ability and the large knowledge base in music history and theory, we propose ComposerX, an agent-based symbolic music generation framework. We find that applying a multi-agent approach significantly improves the music composition quality of GPT-4. The results demonstrate that ComposerX is capable of producing coherent polyphonic music compositions with captivating melodies, while adhering to user instructions.
Forward citations
Cited by 4 Pith papers
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Text2Score: Generating Sheet Music From Textual Prompts
Text2Score turns text prompts into sheet music by having an LLM produce a bar-wise structural plan and a hierarchical decoder write ABC notation from that plan.
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WeaveMuse: An Open Agentic System for Multimodal Music Understanding and Generation
An open multi-agent system that orchestrates specialized music models for understanding, composition, and synthesis, with local or hosted deployment.
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CoComposer: LLM Multi-agent Collaborative Music Composition
A five-agent LLM system for ABC-notation composition scores modestly higher than ComposerX and a single LLM on an automated aesthetic model, but no error bars or significance tests are reported.
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An Intelligent Fault Self-Healing Mechanism for Cloud AI Systems via Integration of Large Language Models and Deep Reinforcement Learning
An LLM-plus-deep-RL hybrid is proposed for cloud fault self-healing, claiming 37% faster recovery on unknown faults with weak experimental documentation.
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