Christoph Riedl, Fernando E. Rosas · 2026 · arXiv
DOI: 10.48550/arXiv.2610.28479 · open-access PDF
Wenn ein Sprachmodell Fähigkeiten entwickelt, die nicht auf seine einzelnen Komponenten zurückverfolgt werden können, wenn ein Multi-Agent-AI-System jeden einzelnen Agenten allein übertrifft, oder wenn ein Mensch-AI-Paar erfolgreich ist, wo entweder in Isolation versagt, beschreiben KI-Forscher es als einen Fall von "Synergie" oder "Emergence".
Machine translation (Meta M2M100); the English original follows.
When a language model develops capabilities not traceable to its individual components, when a multi-agent AI system outperforms each agent on its own, or when a human-AI pair succeeds where either fails in isolation, AI researchers describe it as a case of ``synergy'' or ``emergence''. However, these are usually informal assessments not supported by a theoretical framework, making these claims impossible to compare or even meaningfully dispute. Here we argue that \emph{information decomposition can serve as a unifying formal framework to operationalize assessments about synergy in AI systems}
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