works on

From the 1 of 11 linked papers with an AI index.

most citedMCPXKIT: The Unified Toolkit for Analyzing Model Context Protocol Security

1 citations · 1 across the 10 of their papers we have counts for

collaborators

11 papers

cs.AI2026

On-Policy Distillation for LLM Safety: A Routing Approach to Template-Robust Realignment

Yongjian Guo, Wanlun Ma, Lingyu Shen +2

The paper introduces Routing-based On-Policy Distillation (ROPD), a method for safely realigning large language models that resists malicious prompt templates while preserving the…

cs.DC2026

JoyNexus: Service-Oriented Multi-Tenant Post-Training for VLA Models

Haoran Sun, Wentao Zhang, Junyang Hua +18

The post-training of Vision-Language-Action (VLA) models is essential due to the diversity of simulators, robot embodiments, and task objectives. Existing compute services, whether…

cs.AI2026

Embodied Operators and Benchmarking: Toward Reusable and Deployable Embodied Intelligence Systems

Junwu Xiong, Jiaxuan Gao, Wei Chai +10

Embodied intelligence systems require not only end-to-end policy models, but also reusable functional modules that transform multimodal observations, robot states, human demonstrat…

cs.RO2026

Building a Scalable, Reproducible, Evaluatable, and Closed-Loop Simulation Environment Foundation for Embodied Intelligence

Junwu Xiong, Yongjian Guo, Mingxi Luo +17

This paper presents a cloud-native simulation infrastructure framework for embodied intelligence that supports large-scale training, standardized evaluation, and simulation-based d…

cs.CR20261 cited

MCPXKIT: The Unified Toolkit for Analyzing Model Context Protocol Security

Yongjian Guo, Puzhuo Liu, Wanlun Ma +5

The Model Context Protocol (MCP) has emerged as a universal standard that enables AI agents to seamlessly connect with external tools, significantly enhancing their functionality.…

cs.LG2026

Missing Old Logits in Asynchronous Agentic RL: Semantic Mismatch and Repair Methods for Off-Policy Correction

Zhong Guan, Yongjian Guo, Haoran Sun +5

Asynchronous reinforcement learning improves rollout throughput for large language model agents by decoupling sample generation from policy optimization, but it also introduces a c…