Christoph Riedl, Fernando E. Rosas · 2026 · arXiv
DOI: 10.48550/arXiv.2610.28479 · open-access PDF
जब एक भाषा मॉडल अपनी व्यक्तिगत घटकों के लिए ट्रैक करने योग्य क्षमताओं को विकसित करता है, जब एक मल्टी-एजेंट एआई सिस्टम अपने आप में प्रत्येक एजेंट से अधिक प्रदर्शन करता है, या जब एक मानव-एआई जोड़ी सफल होती है जहां या तो अलग-अलग में विफलता होती है, तो एआई शोधकर्ता इसे "सिनर्जी" या "उपस्थिति" के मामले के रूप में वर्णित करते हैं।
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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