6 citations · 7 across the 7 of their papers we have counts for
Showing cs.AIShow all
3 papers · 1 filter
cs.AI2026
LEMON: Learning Executable Multi-Agent Orchestration via Counterfactual Reinforcement Learning
Xudong Chen, Yixin Liu, Hua Wei +1
Large language models (LLMs) have become a strong foundation for multi-agent systems, but their effectiveness depends heavily on orchestration design. Across different tasks, role…
cs.AI2026
When Simulation Lies: A Sim-to-Real Benchmark and Domain-Randomized RL Recipe for Tool-Use Agents
Xiaolin Zhou, Aojie Yuan, Zheng Luo +12
Tool-use language agents are evaluated on benchmarks that assume clean inputs, unambiguous tool registries, and reliable APIs. Real deployments violate all these assumptions: user…
cs.AI2026
Instructional Agents: Reducing Teaching Faculty Workload through Multi-Agent Instructional Design
Huaiyuan Yao, Wanpeng Xu, Justin Turnau +2
Preparing high-quality instructional materials remains a labor-intensive process that often requires extensive coordination among teaching faculty, instructional designers, and tea…