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cs.AI2026
SkillsBench: Benchmarking How Well Agent Skills Work Across Diverse Tasks
Xiangyi Li, Yimin Liu, Wenbo Chen +75
Agent Skills are structured packages of procedural knowledge that augment large language model (LLM) agents at inference time. Despite rapid adoption, there is no standard way to m…
cs.AI2026
MARS: Margin-Adversarial Risk-controlled Stopping for Parallel LLM Test-time Scaling
Wenbo Chen, Puheng Li, Mengyang Liu +2
Parallel test-time scaling samples many reasoning traces and majority-votes their answers, improving LLM accuracy but requiring traces to run to completion, incurring substantial c…
cs.AI2026
ClawsBench: Evaluating Capability and Safety of LLM Productivity Agents in Simulated Workspaces
Xiangyi Li, Kyoung Whan Choe, Yimin Liu +12
Large language model (LLM) agents are increasingly deployed to automate productivity tasks (e.g., email, scheduling, document management), but evaluating them on live services is r…