most citedAgentic Robot: A Brain-Inspired Framework for Vision-Language-Action Models in Embodied Agents

1 citations · 1 across the 1 of their papers we have counts for

collaborators

5 papers

cs.AI2025

MMLU-Reason: Benchmarking Multi-Task Multi-modal Language Understanding and Reasoning

Guiyao Tie, Xueyang Zhou, Tianhe Gu +7

Recent advances in Multi-Modal Large Language Models (MLLMs) have enabled unified processing of language, vision, and structured inputs, opening the door to complex tasks such as l…

cs.RO20251 cited

Agentic Robot: A Brain-Inspired Framework for Vision-Language-Action Models in Embodied Agents

Zhejian Yang, Yongchao Chen, Xueyang Zhou +8

Long-horizon robotic manipulation poses significant challenges for autonomous systems, requiring extended reasoning, precise execution, and robust error recovery across complex seq…

cs.CR2025

BadVLA: Towards Backdoor Attacks on Vision-Language-Action Models via Objective-Decoupled Optimization

Xueyang Zhou, Guiyao Tie, Guowen Zhang +3

Vision-Language-Action (VLA) models have advanced robotic control by enabling end-to-end decision-making directly from multimodal inputs. However, their tightly coupled architectur…

cs.AI2025

SafeAgent: Safeguarding LLM Agents via an Automated Risk Simulator

Xueyang Zhou, Weidong Wang, Lin Lu +7

Large Language Model (LLM)-based agents are increasingly deployed in real-world applications such as "digital assistants, autonomous customer service, and decision-support systems"…

cs.AI2025

Exploring the Necessity of Reasoning in LLM-based Agent Scenarios

Xueyang Zhou, Guiyao Tie, Guowen Zhang +7

The rise of Large Reasoning Models (LRMs) signifies a paradigm shift toward advanced computational reasoning. Yet, this progress disrupts traditional agent frameworks, traditionall…