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College of Computer Science and Artificial Intelligence
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PlanRAG transforms exploratory reasoning by leveraging logical query trees, achieving superior performance in complex query resolution.
ADaPT enables a single model to flexibly navigate the efficiency-performance trade-off, achieving significant cost savings without sacrificing reasoning quality.
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.
