2 papers
cs.AI2026
CausalVerify: End-to-End Verification of Causal Analyses by Language Models
Yonghong Zhang, Ricardo Correia, Isabel M. Parra +1
Language models increasingly perform empirical analyses end to end, yet existing evaluations assess the written explanation or whether generated code executes, not whether the exec…
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…