Christoph Riedl, Fernando E. Rosas · 2026 · arXiv
DOI: 10.48550/arXiv.2610.28479 · open-access PDF
언어 모델이 개별 구성 요소에 따라 추적할 수 없는 기능을 개발할 때, 멀티 에이전트 AI 시스템이 각 에이전트를 독립적으로 극대화할 때, 또는 인간-AI 쌍이 고립적으로 실패할 때, AI 연구자들은 이것을 "시네르기"또는 " Emergence"의 경우로 묘사합니다.그러나 이들은 일반적으로 이론적 프레임워크에 의해 지원되지 않는 비공식적 평가이며, 이러한 주장을 비교할 수 없거나 심지어 의미있는 논쟁을 할 수 없습니다.
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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