Emergent Coordination in Multi-Agent Language Models
📰 ArXiv cs.AI
Learn to identify emergent coordination in multi-agent language models using an information-theoretic framework
Action Steps
- Apply information decomposition to measure dynamical emergence in multi-agent LLM systems
- Use the framework to localize emergent coordination in the system
- Distinguish spurious correlations from genuine higher-order structure
- Test the framework on various multi-agent LLM systems to validate its effectiveness
- Analyze the results to identify areas for improvement in the system's design and training
Who Needs to Know This
Researchers and engineers working on multi-agent language models can benefit from this framework to analyze and improve their systems
Key Insight
💡 Emergent coordination can be measured and localized in multi-agent LLM systems using information decomposition
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💡 Identify emergent coordination in multi-agent language models with information-theoretic framework
Full Article
Title: Emergent Coordination in Multi-Agent Language Models
Abstract:
arXiv:2510.05174v4 Announce Type: replace-cross Abstract: When are multi-agent LLM systems merely a collection of individual agents versus an integrated collective with higher-order structure? We introduce an information-theoretic framework to test -- in a purely data-driven way -- whether multi-agent systems show signs of higher-order structure. This information decomposition lets us measure whether dynamical emergence is present in multi-agent LLM systems, localize it, and distinguish spurious
Abstract:
arXiv:2510.05174v4 Announce Type: replace-cross Abstract: When are multi-agent LLM systems merely a collection of individual agents versus an integrated collective with higher-order structure? We introduce an information-theoretic framework to test -- in a purely data-driven way -- whether multi-agent systems show signs of higher-order structure. This information decomposition lets us measure whether dynamical emergence is present in multi-agent LLM systems, localize it, and distinguish spurious
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