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cs.CL2026
Pruning and Distilling Mixture-of-Experts into Dense Language Models
Junhyuck Kim, Jihun Yun, Haechan Kim +3
Mixture-of-Experts (MoE) is now the dominant architecture for frontier language models, yet it requires all expert parameters to be loaded in memory, making it less preferable for…
cs.AI2026
Towards Faithful Agentic XAI: A Verification Method and an Open-World Benchmark for Better Model Faithfulness
Jaechang Kim, Sunung Mun, Seungjoon Lee +2
Explainable AI (XAI) helps users interpret model behavior and identify potential faults. Agentic XAI systems use Large Language Models (LLMs) to make explanations more accessible t…