arXiv subjects

Wenan Mao

Publications and source records attributed to Wenan Mao.

3 recordsLinked to original sources

arXiv2026

ActPlane: Programmable OS-Level Policy Enforcement for Agent Harnesses

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 before committing.' Enforcing these policies requires bridging a semantic gap: policy intent is expressed in underspecified natural language, while enforcement must act on concrete system actions, e.g., which test to run. Many policies also define event ordering or data flow actions. Yet existing approaches fall short. Tool-call guardrails miss system actions that bypass the tool layer, while OS sandboxes control resource access instead of actions, returning opaque errors that confuse the agent. Our key insight is that policy context lives within the agent closest to the task, while enforcement must happen at the OS to cover all execution paths. We introduce ActPlane, a policy engine that lets agents declare policies and enforces them in the OS kernel with semantic feedback and isolation. ActPlane uses a simple information-flow control (IFC) DSL to support cross-event policies. We implement ActPlane with eBPF and evaluate it on policies from the empirical study, coding-task benchmarks, and safety benchmarks. ActPlane improves policy compliance, including on indirect execution paths that tool-call interception cannot observe, with 1.9%-8.4% overhead. ActPlane is at https://github.com/eunomia-bpf/ActPlane

cs.OS↗

arXiv2026

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

Performance diagnosis in production-scale AI training is challenging because subtle OS-level issues can trigger cascading GPU delays and network slowdowns, degrading training efficiency across thousands of GPUs. Existing profiling tools are limited to single system layers, incur prohibitive overhead (10--30%), or lack continuous deployment capabilities, resulting in manual analyses spanning days. We argue that continuous, cross-layer observability enabled by OS-level instrumentation and layered differential diagnosis is necessary to address this gap. We introduce SysOM-AI, a production observability system that continuously integrates CPU stack profiling, GPU kernel tracing, and NCCL event instrumentation via adaptive hybrid stack unwinding and eBPF-based tracing, incurring less than 0.4% overhead. Deployed at Alibaba across over 80,000 GPUs for more than one year, SysOM-AI helped diagnose 94 confirmed production issues, reducing median diagnosis time from days to approximately 10 minutes.

cs.PF↗

arXiv2025

gpu_ext: Extensible OS Policies for GPUs via eBPF

Performance in modern GPU-centric systems increasingly depends on resource management policies, including memory placement, scheduling, and observability. However, uniform policies typically yield suboptimal performance across diverse workloads. Existing approaches present a tradeoff: user-space runtimes provide programmability and flexibility but lack cross-tenant visibility and fine-grained control of hardware resources; meanwhile, modifications to the OS kernel introduce significant complexity and safety risks. To address this, we argue that the GPU driver and device layer should provide an extensible OS interface for policy enforcement. While the emerging eBPF technology shows potential, directly applying existing host-side eBPF is insufficient because they lack visibility and control into critical device-side events, and directly embedding policy code into GPU kernels could compromise safety and efficiency. We propose gpu_ext, an eBPF-based runtime that treats the GPU driver and device as a programmable OS subsystem. gpu_ext extends GPU drivers by exposing safe programmable hooks and introduces a device-side eBPF runtime capable of executing verified policy logic within GPU kernels, enabling coherent and transparent policies. Evaluation across realistic workloads including inference, training, and vector search demonstrates that gpu_ext improves throughput by up to 4.8x and reduces tail latency by up to 2x, incurring low overhead, without modifying or restarting applications

cs.OS↗