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cs.SE2026
The Double Measurement Confound in Agent Benchmarks: De-Scaffolding, Ground-Truth Scoring, and Reliability Beyond the Mean
Yonghong Zhang, Shadi Motaali, Vu Phong Dinh +6
Agent benchmarks are increasingly used to compare large language models (LLMs) and guide deployment decisions, yet benchmark scores are meaningful only if they measure model capabi…
cs.AI2026
Oyster-II: Reinforcement Learning for Constructive Safety Alignment in Large Language Models
Jiyang Guan, Yong Xie, Jun Chen +6
Large language models (LLMs) have demonstrated remarkable capabilities across diverse applications, yet ensuring their simultaneous safety, helpfulness, and trustworthiness remains…