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cs.AI2026
MS-Eval: Benchmarking Sub-Agent Delegation and Orchestration in MCP-Intensive Working Scenarios
Bowen Ding, Qixin Wang, Lexing Ruan +4
LLM agents are increasingly integrated into real-world works, yet they must handle diverse domain-specific tools and complex multi-sub-task problems that sequential tool invocation…
cs.AI2026
RODS: Reward-Driven Online Data Synthesis for Multi-Turn Tool-Use Agents
Ruishan Fang, Siyuan Lu, Chenyi Zhuang +1
Multi-turn tool-use RL is bottlenecked by the rapid depletion of informative samples in static datasets. We observe that the gradient signal in GRPO concentrates on tasks with the…
cs.AI2026
Do More Agents Help? Evaluating Accuracy and Cost in LLM Multi-Agent Workflows
Yuhang Fu, Ruishan Fang, Jiaqi Shao +5
Choosing whether to replace a single-agent controller requires evidence on both task performance and inference expenditure. We present BenchAgent, a shared evaluation framework, an…