1 citations · 1 across the 11 of their papers we have counts for
11 papers
CoWeaver: A Bi-directional, Learnable and Explainable Matching Engine for Mixed Human-Agent Science Collaboration
Jiayao Gu, Kexin Chu, Peidong Liu +5
LLM-based agents excel at writing articles, coding and information retrieval. However, they fail to form strong collaborations within the scientific community due to the bidirectio…
MarginGate: Sparse Margin-Triggered Verification for Batch-Invariant LLM Inference
Kexin Chu, Yang Zhou, Wei Zhang
Temperature-zero BF16 LLM inference is often treated as reproducible, yet the same request can emit different tokens when decoded alone or inside a larger batch. Existing fixes use…
Latency-Quality Routing for Functionally Equivalent Tools in LLM Agents
Kexin Chu, Dawei Xiang, Wei Zhang
Tool-augmented LLM agents increasingly access the same tool type through multiple functionally equivalent providers, such as web-search APIs, retrievers, or LLM backends exposed be…
A Systematic Survey of Security Threats and Defenses in LLM-Based AI Agents: A Layered Attack Surface Framework
Kexin Chu
Agentic AI systems introduce a security surface that is qualitatively different from that of stateless LLMs. They persist memory, invoke external tools, coordinate with peer agents…
Dynamic Expert Quantization for Scalable Mixture-of-Experts Inference
Kexin Chu, Dawei Xiang, Zixu Shen +3
Mixture-of-Experts (MoE) has become a practical architecture for scaling LLM capacity while keeping per-token compute modest, but deploying MoE models on a single, memory-limited G…
ExpertFlow: Adaptive Expert Scheduling and Memory Coordination for Efficient MoE Inference
Zixu Shen, Kexin Chu, Yifan Zhang +3
The expansion of large language models is increasingly limited by the constrained memory capacity of modern GPUs. To mitigate this, Mixture-of-Experts (MoE) architectures activate…
PromptSculptor: Multi-Agent Based Text-to-Image Prompt Optimization
Dawei Xiang, Wenyan Xu, Kexin Chu +5
The rapid advancement of generative AI has democratized access to powerful tools such as Text-to-Image models. However, to generate high-quality images, users must still craft deta…
CXLAimPod: CXL Memory is all you need in AI era
Yiwei Yang, Yusheng Zheng, Yiqi Chen +5
The proliferation of data-intensive applications, ranging from large language models to key-value stores, increasingly stresses memory systems with mixed read-write access patterns…
Selective KV-Cache Sharing to Mitigate Timing Side-Channels in LLM Inference
Kexin Chu, Zecheng Lin, Dawei Xiang +7
Global KV-cache sharing is an effective optimization for accelerating large language model (LLM) inference, yet it introduces an API-visible timing side channel that lets adversari…
MVVM: Deploy Your AI Agents-Securely, Efficiently, Everywhere
Yiwei Yang, Aibo Hu, Yusheng Zheng +6
The rise of AI agents powered by Large Language Models (LLMs) presents critical challenges: how to securely execute and migrate these agents across heterogeneous environments while…
CXLMemUring: A Hardware Software Co-design Paradigm for Asynchronous and Flexible Parallel CXL Memory Pool Access
Yiwei Yang, Yusheng Zheng, Kexin Chu +3
CXL-attached memory lets servers add more memory while keeping the standard load/store programming model. The main drawback is latency. CXL memory accesses are too slow for normal…