2 citations · 3 across the 5 of their papers we have counts for
Showing 2026Show all
2 papers · 1 filter
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.CY2026
Evaluating 21st-Century Competencies in Postsecondary Curricula with Large Language Models: Performance Benchmarking and Reasoning-Based Prompting Strategies
Zhen Xu, Xin Guan, Chenxi Shi +2
The growing emphasis on 21st-century competencies in postsecondary education, intensified by the transformative impact of generative AI, underscores the need to evaluate how these…