1 citations · 2 across the 16 of their papers we have counts for
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SEPM: Self-Evolving Procedural Memory Grounded in Task Graphs for Multi-Agent Collaboration
Yi Liu, Junwei Zhao, Li Sun +2
Long-horizon multi-agent collaboration requires memory that preserves both successful actions and the coordination structure underlying them. Existing memory systems support inform…
Feature-Aware Token Attack for Compression-Triggered Stealthy Failures in Large Vision-Language Models
Shilinlu Yan, Bowen Chen, Yuechen Zhang +3
Visual-token compression improves the efficiency of large vision-language models, but can expose failures that full-token evaluation misses. We study adversarial images that preser…
When Reasoning Goes Astray: Attention Dynamics of Uncontrolled Reasoning
Yuanhe Zhang, Ziwei Wang, Jie Ren +6
Large reasoning models (LRMs) improve performance on complex tasks through extended reasoning, yet the same process can degenerate into redundant verification and persistent genera…
Uncovering Uncontrolled Repetition through Residual Stream Dynamics
Yuanhe Zhang, Xinyao Zhou, Haoran Gao +5
Uncontrolled repetition can prolong autoregressive generation in large language models (LLMs) and enable resource consumption attacks. Prior analyses of repetitive generation have…
Concealing LLM-Based Multi-Agent Topology via Phantom Structure Injection
Longzhu He, Zelang Wen, Xinfeng Li +2
Driven by the rapid advancement of large language models (LLMs), LLM-based multi-agent systems (MAS) have emerged as a powerful paradigm for collaborative reasoning over complex ta…
SkillDRE: Dual-Stage Red-Team Evolution of Agent Skills via Pre-Execution and Runtime Feedback
Pengyu Zhu, Jingyi Yang, Yi Liu +2
Agent skills package instructions, executable code, and task-specific resources into reusable artifacts that agents can improve using execution feedback. The same mechanism also en…
From Inaudible Inputs to Model Failures: Low-Frequency Safety Risks in LALMs
Yuanhe Zhang, Weiliu Wang, Jie Ren +7
Large audio-language models (LALMs) have demonstrated strong capabilities in understanding diverse audio inputs. This diversity includes low-frequency signals that are inaudible to…
Structure-Guided Visual Perturbation Neutralization for LVLMs
Yuanhe Zhang, Xueting Wang, YanBin Ren +6
Image inputs enable Large Vision Language Models (LVLMs) to perceive fine-grained visual information, but also introduce a pixel-level attack surface through which adversarial pert…
UniACE: A Unified Framework for Evaluating LLM Agentic Capabilities
Pengyu Zhu, Lijun Li, Yaxing Lyu +8
Agent benchmarks are increasingly used to compare large language models (LLMs) across domains, yet a reported score reflects a complete model--harness--environment configuration ra…
Resource Consumption Threats in Large Language Models
Yuanhe Zhang, Xinyue Wang, Zhican Chen +8
Given limited and costly computational infrastructure, resource efficiency is a key requirement for large language models (LLMs). Efficient LLMs increase service capacity for provi…
From Helpfulness to Toxic Proactivity: Diagnosing Behavioral Misalignment in LLM Agents
Xinyue Wang, Yuanhe Zhang, Zhengshuo Gong +6
The enhanced capabilities of LLM-based agents come with an emergency for model planning and tool-use abilities. Attributing to helpful-harmless trade-off from LLM alignment, agents…
"LLM Agent Performance" Is Not a Single Evaluation Target
Pengyu Zhu, Li Sun, Philip S. Yu +1
LLM agent benchmark scores are shaped not only by the model but also by the agent harness, environment, evaluator, and inference budget. Unified execution controls these non-model…
SEE: Signal Embedding Energy for Quantifying Noise Interference in Large Audio Language Models
Yuanhe Zhang, Jiayu Tian, Yibo Zhang +5
Large Audio Language Models (LALMs) have been widely applied in real-time scenarios, such as in-car assistants and online meeting comprehension. In practice, audio inputs are often…