2 papers
cs.CL2026
HERO-MoE: Historical Expert Routing with Scale-Preserving Fusion
Junxiang Qiu, Zhengsu Chen, Xinting Hu +6
Mixture-of-Experts (MoE) architectures have become a standard way to scale model capacity while keeping computation sparse, yet routing remains a key determinant of MoE quality and…
cs.CL2026
Confidence Before Answering: A Paradigm Shift for Efficient LLM Uncertainty Estimation
Changcheng Li, Jiancan Wu, Hengheng Zhang +5
Reliable deployment of large language models (LLMs) requires accurate uncertainty estimation. Existing methods are predominantly answer-first, producing confidence only after gener…