1 citations · 1 across the 8 of their papers we have counts for
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REAL-Q: E2E LLM Quantization via Dynamic Gradient Descent
Qian Zhang, Yaoming Li, Zhewen Tan +9
Post-training quantization (PTQ) is essential for deploying large language models (LLMs) under strict resource constraints. State-of-the-art PTQ methods quantize each layer with a…
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…
AgentRob: From Virtual Forum Agents to Hijacked Physical Robots
Wenrui Liu, Yaxuan Wang, Xun Zhang +13
Large Language Model (LLM)-powered autonomous agents have demonstrated significant capabilities in virtual environments, yet their integration with the physical world remains narro…
How Few-shot Demonstrations Affect Prompt-based Defenses Against LLM Jailbreak Attacks
Yanshu Wang, Shuaishuai Yang, Jingjing He +1
Large Language Models (LLMs) face increasing threats from jailbreak attacks that bypass safety alignment. While prompt-based defenses such as Role-Oriented Prompts (RoP) and Task-O…