4 citations · 4 across the 10 of their papers we have counts for
10 papers
WeClawArena: An Auditable Sandbox and Benchmark for Cross-User Agents Collaboration and Security in Human-Centered Agent Networks
Prince Zizhuang Wang, Aojie Yuan, Haiyue Zhang +3
Recent advances in persistent personal-agent frameworks are making human-centered agent networks realistic deployment targets: each user can be served by an AI agent that acts on t…
DLLG: Dynamic Logit-Level Gating of LLM Experts
Bingnan Li, Zhaoyang Zhang, Xiaoze Liu +6
Leveraging multiple specialized LLMs can combine complementary strengths, but existing approaches trade adaptability for stability: routing commits prematurely, heuristic ensemblin…
When Simulation Lies: A Sim-to-Real Benchmark and Domain-Randomized RL Recipe for Tool-Use Agents
Xiaolin Zhou, Aojie Yuan, Zheng Luo +12
Tool-use language agents are evaluated on benchmarks that assume clean inputs, unambiguous tool registries, and reliable APIs. Real deployments violate all these assumptions: user…
PRIME: Training Free Proactive Reasoning via Iterative Memory Evolution for User-Centric Agent
Prince Zizhuang Wang, Shuli Jiang
The development of autonomous tool-use agents for complex, long-horizon tasks in collaboration with human users has become the frontier of agentic research. During multi-turn Human…
AgentSocialBench: Evaluating Privacy Risks in Human-Centered Agentic Social Networks
Prince Zizhuang Wang, Shuli Jiang
With the rise of personalized, persistent LLM agent frameworks such as OpenClaw, human-centered agentic social networks in which teams of collaborative AI agents serve individual u…
Asymmetric Actor-Critic for Multi-turn LLM Agents
Shuli Jiang, Zhaoyang Zhang, Yi Zhang +3
Large language models (LLMs) exhibit strong reasoning and conversational abilities, but ensuring reliable behavior in multi-turn interactions remains challenging. In many real-worl…
SLEA-RL: Step-Level Experience Augmented Reinforcement Learning for Multi-Turn Agentic Training
Prince Zizhuang Wang, Shuli Jiang
Large Language Model (LLM) agents have shown strong results on multi-turn tool-use tasks, yet they operate in isolation during training, failing to leverage experiences accumulated…
Reinforcement-aware Knowledge Distillation for LLM Reasoning
Zhaoyang Zhang, Shuli Jiang, Yantao Shen +6
Reinforcement learning (RL) post-training has recently driven major gains in long chain-of-thought reasoning large language models (LLMs), but the high inference cost of such model…
Turning Generative Models Degenerate: The Power of Data Poisoning Attacks
Shuli Jiang, Swanand Ravindra Kadhe, Yi Zhou +3
The increasing use of large language models (LLMs) trained by third parties raises significant security concerns. In particular, malicious actors can introduce backdoors through po…
Forcing Generative Models to Degenerate Ones: The Power of Data Poisoning Attacks
Shuli Jiang, Swanand Ravindra Kadhe, Yi Zhou +2
Growing applications of large language models (LLMs) trained by a third party raise serious concerns on the security vulnerability of LLMs.It has been demonstrated that malicious a…