arXiv subjects

Weichen Tao

Publications and source records attributed to Weichen Tao.

4 recordsLinked to original sources

arXiv2026

Can AI Agents Detect and Repair Artifact Drift in Network Experiments?

In recent years, AI agents have evolved into capable assistants that carry out multi-step tasks in digital environments. The network systems community is beginning to explore these capabilities in operational and experimental settings. However, an agent operating in network systems should not be judged solely by whether it completes the immediate task. The experiment record it modifies must also remain trustworthy. We call this property artifact integrity: the record's claims must remain supported by the available evidence, confined to the scope established by that evidence, and traceable through the artifacts that encode their support. To make this property measurable, we introduce NetArtifactBench, which tests whether AI agents can repair inconsistent records derived from public network-system artifacts while preserving claims that remain supported. The benchmark contains 52 instances with injected inconsistencies ranging from direct contradictions to unstated relations spread across several artifacts. We evaluate 23 agent configurations across three general-purpose AI agent runtimes using deterministic scoring. The average contract pass rate is 65.3 % across 5,980 outputs, but no agent runtime exceeds 30 % when repair requires recovering implicit relations and propagating changes across artifacts. These results reveal a sharp boundary between local correction and complete record-level repair. Therefore, we argue that artifact integrity should become a first-class design and evaluation requirement for AI agents operating on network systems.

cs.NI↗

arXiv2026

Characterizing and Bridging the Diagnostic Gap in eBPF Verifier Rejections

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 repair challenging: the error reports where verification stopped, not where the program lost the proof the verifier required. To quantify this gap, we conduct an empirical study of 235 reproduced rejections, showing that 47% of rejections return only EINVAL, one error string maps to as many as nine distinct root causes, and 10 of the 12 root causes are eBPF-specific. Repair thus requires both domain knowledge and locating where the proof was lost, yet existing tools only help developers read the error. We present bpfix, which reconstructs where the required proof was established and where it was lost from the verifier log, and prints a Rust-like diagnostic. To evaluate bpfix and the ability of LLMs to help repair, we construct a benchmark of 75 LLM repair tasks. Current models achieve 0-37% one-shot success with the raw log, and replacing the log with the bpfix localization improves repair by 11-21pp, suggesting that locating where the proof was lost is key to guiding repair. bpfix is available at https://github.com/eunomia-bpf/bpfix

cs.OS↗

arXiv2026

Kops: Safely Extending the eBPF Compilation Pipeline with Native Operations

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 program, and a kernel just-in-time compiler (JIT) translates the verified bytecode to native code. The kernel keeps the JIT simple to stay trustworthy, translating one bytecode instruction at a time in a single pass. This single-pass design misses optimization opportunities, so eBPF runs up to twice as slow as natively compiled code in our characterization. Adding optimizations to the kernel JIT directly requires upstream acceptance and a long release cycle, enlarges the trusted computing base (TCB), and grows the per-architecture kernel code. To address this, we present Kops, an extension interface that lets userspace compilers and kernel modules introduce new operations without modifying the kernel core, while keeping a minimal trusted computing base (TCB). Each operation has two forms, a proof sequence of vanilla eBPF instructions that the existing verifier checks and a native emit of machine instructions that the JIT compiles. Because the verifier checks the proof sequence, the native emit is the only per-operation addition to the TCB. Hardware idioms are the lowest-hanging fruit for this interface. With Kops, we build EInsn, seven operations such as rotate and conditional select that CPUs execute as single instructions. Lean 4 proofs show that each native emit computes the same result as its proof sequence. On x86-64 and ARM64, EInsn speeds up eBPF microbenchmarks by up to 24% and production applications by up to 12%. The same interface also supports whole-program native replacement, reaching 2.358x at the cost of a larger TCB.

cs.OS↗

arXiv2026

Probabilistic reconstruction of global sea surface temperature using generative diffusion models

Accurate reconstruction of global Sea surface temperature (SST), which dominates the air-sea coupling and global climate variability, underpins climate monitoring and prediction. Existing SST reconstruction products primarily provide one deterministic field derived from heterogeneous satellite data and in situ observations, limiting their ability to represent observation uncertainty and to support probabilistic forecasting. Here, we introduce Satellite and in situ Adaptive Guided Estimation (SAGE), a diffusion-based uncertainty-aware generative framework for probabilistic SST reconstruction. SAGE learns a physically consistent prior from historical SST data and performs observation-conditioned posterior sampling without requiring satellite or in situ data during training, enabling flexible state inference from heterogeneous observations. Through a progressive data-fusion strategy, observations from two FengYun-3D polar-orbiting satellites constrain basin-scale structures, while sparse in situ measurements serve to refine local anomalies and extremes. The resulting ensemble SST fields well capture observational uncertainty and scale-dependent variability. Validation against independent in situ observations shows that SAGE substantially reduces reconstruction errors compared with widely used operational products. When used to initialize forecasting systems, SAGE-generated SST fields substantially reduce 10-day SST forecast errors relative to current operational analyses. At the climate scale, SAGE-driven forecasts of the 2023-2024 El Nino event show added value in capturing its onset and intensity evolution compared to conventional approaches. Our results demonstrate that SAGE represents a step toward a new paradigm for ocean state estimation and climate prediction.

physics.ao-ph↗