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cs.CL2026
From Diagnosis to Redesign: Using Quantitative Ethnography to Improve Multi-Agent LLM Reasoning
Vedant Khatri, Anthony Cusimano, Zachari Swiecki +3
Multi-agent large language model (LLM) systems are designed to improve reasoning by decomposing tasks across multiple agents with specialized functions, but the presence of multipl…
cs.CL2025
Bringing Pedagogy into Focus: Evaluating Virtual Teaching Assistants' Question-Answering in Asynchronous Learning Environments
Li Siyan, Zhen Xu, Vethavikashini Chithrra Raghuram +3
Asynchronous learning environments (ALEs) are widely adopted for formal and informal learning, but timely and personalized support is often limited. In this context, Virtual Teachi…