CaRE-KD uses confidence-gated adaptive divergence and batch-level rejection to improve LLM distillation, boosting instruction-following, coding, and math benchmarks over strong baselines.
Agentic AI scientists serve as co-scientists but lack autonomous discovery due to flawed problem selection, missing tacit lab knowledge, compressed diversity, and inadequate benchmarks.