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cs.AI2026
ReQuant: Fixed-Grid Discrete Refinement for Post-Training Quantization
Yongge Ma, Guoan Wang, Feiyu Wang +5
Post-training quantization (PTQ) is widely used to reduce the memory and computational cost of large language models. Existing PTQ methods typically obtain an initial quantized mod…
cs.AI2026
Formal Skill: Programmable Runtime Skills for Efficient and Accurate LLM Agents
Xi Zhang, Meijun Gao, Yuntian Zhao +6
Large Language Model (LLM) agents increasingly act inside real workspaces, where tools and skills determine whether model reasoning becomes reliable action. Existing skills remain…