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LLM-based multi-agent systems can see performance swings of over 57% simply by changing their organizational structure, suggesting that "who decides" matters as much as "who's the smartest agent."
LLMs exhibit significant geographical performance disparities and task-specific gaps when evaluated on the new GaoYao benchmark, highlighting the need for more nuanced multilingual and multicultural training.
LLM agent performance hinges on maximizing decision-relevant information density within context, not just context length, and GenericAgent proves it.
