4 citations · 7 across the 33 of their papers we have counts for
15 papers · 1 filter
Learning What to Retain: Gated-Memory Routing for Efficient Collaboration in Multi-Agent LLM Systems
Rakibul Hasan Rajib, Mengxin Zheng, Qian Lou
Large language model (LLM)-based multi-agent systems tackle complex reasoning by orchestrating how multiple agents are configured and how they collaborate. A central challenge is t…
Conjunctive Poisoning in AI Supply-Chain Applications
Nokimul Hasan Arif, Qian Lou, Mengxin Zheng
Large Language and Vision-Language Models are increasingly deployed through inference pipelines that include prompt wrappers (e.g., templates and post-processing scripts) and confi…
HW-Router: Hardware-Aware Routing for Scalable Multi-LLM Serving
Ahasan Kabir, Jiaqi Xue, Mengxin Zheng +1
Modern large language model (LLM) serving platforms deploy multiple models across different GPUs, requiring routers to direct incoming queries to appropriate LLMs. However, existin…
Learning Latency-Aware Orchestration for Multi-Agent Systems
Xi Shi, Mengxin Zheng, Qian Lou
Multi-agent systems (MAS) coordinate multiple LLM-powered agents through structured workflows, gaining reasoning power but incurring high inference latency from multi-step executio…
CutBackdoor: A Circuit Cut Triggered Backdoor Attack on Variational Quantum Algorithms
Ahatesham Bhuiyan, Hoang Ngo, Cheng Chu +4
Variational Quantum Algorithms (VQAs) are a leading paradigm for near-term quantum computing, combining parameterized quantum circuits with classical optimization across quantum ch…
REALM: A Unified Red-Teaming Benchmark for Physical-World VLMs
Yifei Zhao, Qian Lou, Mengxin Zheng
Vision-language models (VLMs) are increasingly used as perception-reasoning backbones for embodied intelligence in safety-critical physical systems, where perception or reasoning e…
INFRAMIND: Infrastructure-Aware Multi-Agent Orchestration
Ahasan Kabir, Jiaqi Xue, Mengxin Zheng +1
Existing multi-agent LLM orchestration methods, ranging from brute-force ensembles to learned routers, select models and topologies based on task and model features. However, these…
MPC-Patch-Bench: Security-Aware LLM Code Patch for Multi-Party Computation
Yukuan Zhang, Mengxin Zheng, Qian Lou
Repository-level benchmarks for evaluating Large Language Model (LLM) code repair on Secure Multi-Party Computation (MPC) software do not yet exist, and directly transplanting gene…
AgentServeSim: Serving-System Simulation and Policy Search for LLM Agent Programs
Rakibul Hasan Rajib, Mengxin Zheng, Qian Lou
Large language model agents execute programs comprising multiple model turns interleaved with external tool calls. Their job completion time depends on how the serving system retai…
SIF: Semantically In-Distribution Fingerprints for Large Vision-Language Models
Yifei Zhao, Qian Lou, Mengxin Zheng
The public accessibility of large vision-language models (LVLMs) raises serious concerns about unauthorized model reuse and intellectual property infringement. Existing ownership v…
Conjunctive Prompt Attacks in Multi-Agent LLM Systems
Nokimul Hasan Arif, Qian Lou, Mengxin Zheng
Most LLM safety work studies single-agent models, but many real applications rely on multiple interacting agents. In these systems, prompt segmentation and inter-agent routing crea…
SecureRouter: Encrypted Routing for Efficient Secure Inference
Yukuan Zhang, Mengxin Zheng, Qian Lou
Cryptographically secure neural network inference typically relies on secure computing techniques such as Secure Multi-Party Computation (MPC), enabling cloud servers to process cl…
RobPI: Robust Private Inference against Malicious Client
Jiaqi Xue, Mengxin Zheng, Qian Lou
The increased deployment of machine learning inference in various applications has sparked privacy concerns. In response, private inference (PI) protocols have been created to allo…
RPP: A Certified Poisoned-Sample Detection Framework for Backdoor Attacks under Dataset Imbalance
Miao Lin, Feng Yu, Rui Ning +6
Deep neural networks are highly susceptible to backdoor attacks, yet most defense methods to date rely on balanced data, overlooking the pervasive class imbalance in real-world sce…
Learning Latency-Aware Orchestration for Multi-Agent Systems
Xi Shi, Mengxin Zheng, Qian Lou
Multi-agent systems (MAS) coordinate multiple LLM-powered agents through structured workflows, gaining reasoning power but incurring high inference latency from multi-step executio…