# MusicSwarm: Biologically Inspired Intelligence for Music Composition

arXiv: https://arxiv.org/abs/2509.11973 · HTML: https://mlsanity.com/paper/2509.11973/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 19 of its 88 references that are in the map.
- First posted on arXiv 15 Sep 2025.

## Builds on (most cited first, 19 of 19)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Training language models to follow instructions with human feedback](https://mlsanity.com/paper/2203.02155/) (2022)
- [ReAct: Synergizing Reasoning and Acting in Language Models](https://mlsanity.com/paper/2210.03629/) (2022)
- [Judging LLM-as-a-Judge with MT-Bench and Chatbot Arena](https://mlsanity.com/paper/2306.05685/) (2023)
- [Reflexion: Language Agents with Verbal Reinforcement Learning](https://mlsanity.com/paper/2303.11366/) (2023)
- [Voyager: An Open-Ended Embodied Agent with Large Language Models](https://mlsanity.com/paper/2305.16291/) (2023)
- [Fine-Tuning Language Models from Human Preferences](https://mlsanity.com/paper/1909.08593/) (2019)
- [Quiet-STaR: Language Models Can Teach Themselves to Think Before Speaking](https://mlsanity.com/paper/2403.09629/) (2024)
- [Multiagent Cooperation and Competition with Deep Reinforcement Learning](https://mlsanity.com/paper/1511.08779/) (2015)
- [Ripser: efficient computation of Vietoris-Rips persistence barcodes](https://mlsanity.com/paper/1908.02518/) (2019)
- [Emergent Communication through Negotiation](https://mlsanity.com/paper/1804.03980/) (2018)
- [A Comprehensive Survey on Deep Music Generation: Multi-level Representations, Algorithms, Evaluations, and Future Directions](https://mlsanity.com/paper/2011.06801/) (2020)
- [LakhNES: Improving multi-instrumental music generation with cross-domain pre-training](https://mlsanity.com/paper/1907.04868/) (2019)
- [MechGPT, a language-based strategy for mechanics and materials modeling that connects knowledge across scales, disciplines and modalities](https://mlsanity.com/paper/2310.10445/) (2023)
- [Music Generation by Deep Learning - Challenges and Directions](https://mlsanity.com/paper/1712.04371/) (2017)
- [Sparks: Multi-Agent Artificial Intelligence Model Discovers Protein Design Principles](https://mlsanity.com/paper/2504.19017/) (2025)
- [ForceGen: End-to-end de novo protein generation based on nonlinear mechanical unfolding responses using a protein language diffusion model](https://mlsanity.com/paper/2310.10605/) (2023)
- [On the Complex Network Structure of Musical Pieces: Analysis of Some Use Cases from Different Music Genres](https://mlsanity.com/paper/1709.09708/) (2017)
- [Autonomous Inorganic Materials Discovery via Multi-Agent Physics-Aware Scientific Reasoning](https://mlsanity.com/paper/2508.02956/) (2025)

## Built on by (most cited first, 1 of 1)

- [Selective Imperfection as a Generative Framework for Analysis, Creativity and Discovery](https://mlsanity.com/paper/2601.00863/) (2025)

## Abstract (arXiv)

We show that coherent, long-form musical composition can emerge from a decentralized swarm of identical, frozen foundation models that coordinate via stigmergic, peer-to-peer signals, without any weight updates. We compare a centralized multi-agent system with a global critic to a fully decentralized swarm in which bar-wise agents sense and deposit harmonic, rhythmic, and structural cues, adapt short-term memory, and reach consensus. Across symbolic, audio, and graph-theoretic analyses, the swarm yields superior quality while delivering greater diversity and structural variety and leads across creativity metrics. The dynamics contract toward a stable configuration of complementary roles, and self-similarity networks reveal a small-world architecture with efficient long-range connectivity and specialized bridging motifs, clarifying how local novelties consolidate into global musical form. By shifting specialization from parameter updates to interaction rules, shared memory, and dynamic consensus, MusicSwarm provides a compute- and data-efficient route to long-horizon creative structure that is immediately transferable beyond music to collaborative writing, design, and scientific discovery.