activity
20232026
most citedAgentSight: System-Level Observability for AI Agents Using eBPF

5 citations · 6 across the 19 of their papers we have counts for

collaborators

20 papers

cs.AI2026

AgentPProf: Semantic Profiler for Long Horizon AI Agents

Yusheng Zheng, Chaokun Chang, Yu Mao +7

AI agents increasingly orchestrate long-running activities with users, tools, and system resources for days and weeks. To improve agent quality, safety, and cost efficiency, develo…

cs.AR2026

HBFSim: Fast and Faithful Simulation of High-Bandwidth Flash Under Real GPU Execution

Yanpeng Hu, Yiwei Yang, Yuanwu Zhu +3

High-Bandwidth Flash (HBF) places high-capacity NAND beside HBM to relieve the memory-capacity bottleneck of LLM inference, yet its system-level behavior cannot be evaluated before…

cs.DC2026

The Fabric Is the Cluster Driver: Cross-Layer eBPF Policies for GPU-CXL Fabrics

Yiwei Yang, Andi Quinn

We present fabric_ext, an eBPF middleware compiler and runtime for extensible OS policies over GPU--CXL fabrics. fabric_ext lets one policy program execute across GPU hooks, driver…

cs.OS2026

Characterizing and Bridging the Diagnostic Gap in eBPF Verifier Rejections

Yusheng Zheng, Zhengjie Ji, Weichen Tao +5

eBPF lets developers run custom programs inside the Linux kernel, where a verifier proves each program safe. However, when the verifier rejects a program, the unclear error makes r…

cs.OS2026

ActPlane: Programmable OS-Level Policy Enforcement for Agent Harnesses

Yusheng Zheng, Tianyuan Wu, Quanzhi Fu +6

AI agents increasingly run in production through harnesses, the software around the LLM, including an engine that enforces safety and effectiveness policies, e.g., 'run tests befor…

cs.OS2026

Kops: Safely Extending the eBPF Compilation Pipeline with Native Operations

Yusheng Zheng, Zhengjie Ji, Weichen Tao +4

eBPF safely extends OS kernels in domains such as networking, observability, and security. The safety comes from an in-kernel compilation pipeline where a verifier checks every pro…

cs.DC2026

Concordia: JIT-Compiled Persistent-Kernel Checkpointing for Fault-Tolerant LLM Inference

Yuhang Gan, Yiwei Yang, Yuyi Li +6

Long-running LLM agents keep valuable state resident on GPUs: KV caches, request schedulers, communication state, and sometimes online adapters. Losing this state after a GPU or co…

cs.DC2026

GPUOS: A GPU Operating System Primitive for Transparent Operation Fusion

Yiwei Yang, Xiangyu Gao, Yuan Zhou +3

Modern deep learning workloads often consist of many small tensor operations, especially in inference, attention, and micro-batched training. In these settings, kernel launch overh…

cs.OS2026

ReFlux: Reversible Compute Placement for CXL-Enabled Storage

Yanpeng Hu, Yiwei Yang, Yusheng Zheng +4

Static offload to computational storage devices proves brittle because device-side processors throttle under sustained thermal load, while opaque, vendor-specific interfaces inflat…

cs.PF2026

SysOM-AI: Continuous Cross-Layer Performance Diagnosis for Production AI Training

Yusheng Zheng, Wenan Mao, Shuyi Cheng +8

Performance diagnosis in production-scale AI training is challenging because subtle OS-level issues can trigger cascading GPU delays and network slowdowns, degrading training effic…

cs.CR2026

ACRFence: Preventing Semantic Rollback Attacks in Agent Checkpoint-Restore

Yusheng Zheng, Yiwei Yang, Wei Zhang +1

LLM agent frameworks increasingly offer checkpoint-restore for error recovery and exploration, advising developers to make external tool calls safe to retry. This advice assumes th…

cs.OS2026

AgentCgroup: Understanding and Controlling OS Resources of AI Agents

Yusheng Zheng, Jiakun Fan, Quanzhi Fu +3

AI agents are increasingly deployed in multi-tenant cloud environments, where they execute diverse tool calls within sandboxed containers, each call with distinct resource demands…

cs.OS2025

gpu_ext: Extensible OS Policies for GPUs via eBPF

Yusheng Zheng, Tong Yu, Yiwei Yang +8

Performance in modern GPU-centric systems increasingly depends on resource management policies, including memory placement, scheduling, and observability. However, uniform policies…

cs.AI2025

Towards Agentic OS: An LLM Agent Framework for Linux Schedulers

Yusheng Zheng, Yanpeng Hu, Wei Zhang +1

Operating system schedulers suffer from a fundamental semantic gap, where kernel policies fail to understand application-specific needs, leading to suboptimal performance. We intro…

cs.OS2025

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…

cs.OS2025★ 5 cited

AgentSight: System-Level Observability for AI Agents Using eBPF

Yusheng Zheng, Yanpeng Hu, Tong Yu +1

Modern software infrastructure increasingly relies on LLM agents for development and maintenance, such as Claude Code and Gemini-cli. However, these AI agents differ fundamentally…

cs.AR2025

HetGPU: The pursuit of making binary compatibility towards GPUs

Yiwei Yang, Yusheng Zheng, Tong Yu +1

Heterogeneous GPU infrastructures present a binary compatibility challenge: code compiled for one vendor's GPU will not run on another due to divergent instruction sets, execution…

cs.AR2025

Exploiting Control-flow Enforcement Technology for Sound and Precise Static Binary Disassembly

Brian Zhao, Yiwei Yang, Yusheng Zheng +1

Rewriting x86_64 binaries-whether for security hardening, dynamic instrumentation, or performance profiling is notoriously difficult due to variable-length instructions, interleave…

cs.OS2024★ 1 cited

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…

cs.AR2023

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…