activity
20202026
most citedStatic Inference Meets Deep Learning: A Hybrid Type Inference Approach for Python

56 citations · 144 across the 48 of their papers we have counts for

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6 papers · 1 filter

cs.AI2026

Benchmarking Jailbreak Guardrails for Embodied Agents

Xunguang Wang, Qingyue Wang, Yuguang Zhou +3

Embodied agents powered by large language models and vision-language models are increasingly deployed in physical environments, but jailbreak attacks can induce these agents to per…

cs.AI2026

Unlocking Proactivity in Task-Oriented Dialogue

Azure Zhang, Ning Gao, Yuqin Dai +7

Proactive task-oriented dialogue (TOD), such as outbound sales, demands a persuasive agent that actively probes the user's concerns and steers the conversation toward acceptance wi…

cs.AI2026

Beyond Content Safety: Real-Time Monitoring for Reasoning Vulnerabilities in Large Language Models

Xunguang Wang, Yuguang Zhou, Qingyue Wang +5

Large language models increasingly rely on explicit chain-of-thought reasoning to solve complex tasks, yet the safety of the reasoning process itself remains largely unaddressed. E…

cs.AI2026

On Protecting Agentic Systems' Intellectual Property via Watermarking

Liwen Wang, Zongjie Li, Yuchong Xie +4

The evolution of Large Language Models (LLMs) into agentic systems that perform autonomous reasoning and tool use has created significant intellectual property (IP) value. We demon…

cs.AI2025

Measuring and Augmenting Large Language Models for Solving Capture-the-Flag Challenges

Zimo Ji, Daoyuan Wu, Wenyuan Jiang +3

Capture-the-Flag (CTF) competitions are crucial for cybersecurity education and training. As large language models (LLMs) evolve, there is increasing interest in their ability to a…

cs.AI2024★ 1 cited

Testing and Understanding Erroneous Planning in LLM Agents through Synthesized User Inputs

Zhenlan Ji, Daoyuan Wu, Pingchuan Ma +2

Agents based on large language models (LLMs) have demonstrated effectiveness in solving a wide range of tasks by integrating LLMs with key modules such as planning, memory, and too…