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Home/Authors/Tao Yu

Tao Yu

6 indexed papers

Recent (6 mo)
6
With code
0
Influential cites
0
Benchmarked
0

Publications per year

6
26

Top categories

AI×6NLP×4ML×4Crypto×3Vision×2Robotics×1Stats ML×1

Frequent co-authors

Dongrui Liu4×
Leitao Yuan4×
Jing Shao4×
Tianyi Zhou3×
Xia Hu3×
Qinghua Mao3×

Research Timeline

2026
Frequency-Domain Regularized Adversarial Alignment for Transferable Attacks against Closed-Source MLLMs

The paper proposes FRA-Attack, a frequency-domain regularization method, to significantly improve the transferability of adversarial attacks against closed-source Multimodal Large Language Models (MLLMs).

Qwen-VLA: Unifying Vision-Language-Action Modeling across Tasks, Environments, and Robot Embodiments

Qwen-VLA introduces a unified embodied foundation model that extends vision-language understanding to continuous action generation, enabling robust, multi-task generalization across diverse robotic tasks and embodiments.

AgentDoG 1.5: A Lightweight and Scalable Alignment Framework for AI Agent Safety and Security

The paper introduces AgentDoG 1.5, a lightweight and scalable alignment framework that significantly improves AI agent safety and security for complex, open-world agentic scenarios.

AgentDoG 1.5: A Lightweight and Scalable Alignment Framework for AI Agent Safety and Security

The paper introduces AgentDoG 1.5, a lightweight and scalable alignment framework that significantly improves AI agent safety and security for complex open-world agent deployments.

COLLEAGUE.SKILL: Automated AI Skill Generation via Expert Knowledge Distillation

COLLEAGUE.SKILL introduces an automated system that distills heterogeneous traces of human expertise and role-specific knowledge into portable, inspectable, and usable AI skill packages.

AnyEdit++: Adaptive Long-Form Knowledge Editing via Bayesian Surprise

AnyEdit++ introduces a structure-aware framework that uses Bayesian Surprise to adaptively segment long-form knowledge, significantly improving the coherence and accuracy of knowledge editing in LLMs.

Highlighted terms show continued research focus across papers

Papers

cs.AIRecentMay 31, 2026

AnyEdit++: Adaptive Long-Form Knowledge Editing via Bayesian Surprise

Bowen Tian, Caixue He, Jiemin Wu, Jingying Wang +3 more

AnyEdit++ introduces a structure-aware framework that uses Bayesian Surprise to adaptively segment long-form knowledge, significantly improving the coherence and accuracy of knowledge editing in LLMs.

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cs.AIcs.CLcs.LGRecentMay 29, 2026

COLLEAGUE.SKILL: Automated AI Skill Generation via Expert Knowledge Distillation

Tianyi Zhou, Dongrui Liu, Leitao Yuan, Jing Shao +1 more

COLLEAGUE.SKILL introduces an automated system that distills heterogeneous traces of human expertise and role-specific knowledge into portable, inspectable, and usable AI skill packages.

View →
cs.ROcs.AIcs.CLRecentMay 28, 2026

Qwen-VLA: Unifying Vision-Language-Action Modeling across Tasks, Environments, and Robot Embodiments

Qiuyue Wang, Mingsheng Li, Jian Guan, Jinhui Ye +36 more

Qwen-VLA introduces a unified embodied foundation model that extends vision-language understanding to continuous action generation, enabling robust, multi-task generalization across diverse robotic ta…

View →
cs.AIcs.CLcs.CRRecentMay 28, 2026

AgentDoG 1.5: A Lightweight and Scalable Alignment Framework for AI Agent Safety and Security

Dongrui Liu, Yu Li, Zhonghao Yang, Peng Wang +46 more

The paper introduces AgentDoG 1.5, a lightweight and scalable alignment framework that significantly improves AI agent safety and security for complex, open-world agentic scenarios.

View →
cs.AIcs.CLcs.CRRecentMay 28, 2026

AgentDoG 1.5: A Lightweight and Scalable Alignment Framework for AI Agent Safety and Security

Dongrui Liu, Yu Li, Zhonghao Yang, Peng Wang +46 more

The paper introduces AgentDoG 1.5, a lightweight and scalable alignment framework that significantly improves AI agent safety and security for complex open-world agent deployments.

View →
cs.CRcs.AIcs.LGRecentMay 20, 2026

Frequency-Domain Regularized Adversarial Alignment for Transferable Attacks against Closed-Source MLLMs

Leitao Yuan, Qinghua Mao, Daizong Liu, Kun Wang +4 more

The paper proposes FRA-Attack, a frequency-domain regularization method, to significantly improve the transferability of adversarial attacks against closed-source Multimodal Large Language Models (MLL…

View →