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cs.LG2026
PACER: Acyclic Causal Discovery from Large-Scale Interventional Data
Ramon Viñas Torné, Sílvia Fàbregas Salazar, Soyon Park +4
Inferring the structure of directed acyclic graphs (DAGs) from data is a central challenge in causal discovery, particularly in modern high-dimensional settings where large-scale i…
cs.CL2026
When Weak LLMs Speak with Confidence, Preference Alignment Gets Stronger
Amirabbas Afzali, Myeongho Jeon, Maria Brbic
Preference alignment is an essential step in adapting large language models (LLMs) to human values, but existing approaches typically depend on costly human annotations or large-sc…