Wei Zhang

Yusheng Zheng, Zhengjie Ji, Weichen Tao et al.

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.

Qingyang Wang, Xingang Liu, Changwei Yao et al.

Imitation learning enables robots to acquire complex manipulation skills from demonstrations, but its effectiveness is limited by the cost of collecting high-quality data. Trajectory-level data augmentation methods alleviate this challenge by recombining expert demonstrations under varied initial states. However, such methods typically insert interpolations or other non-expert transition segments between disjoint parts, and such non-expert segments could reduce the quality of the generated data. This paper introduces Minimizing Interpolation (MinInter), an effective trajectory selection method that, for each sampled initial configuration, chooses the source demonstration requiring the least interpolation to form a complete trajectory. By explicitly minimizing interpolations during data generation, MinInter produces higher-quality synthetic demonstrations while remaining compatible with existing data generation frameworks. Experiments on 12 manipulation tasks with 26 variants from the MimicGen benchmark show that MinInter consistently improves both data generation success rates and policy success rates, with the largest gains on contact-rich, long-horizon and high-variance settings. Compared to the recent SkillGen framework, MinInter achieves higher policy success rates despite its conceptual simplicity, underscoring the value of interpolation minimization for data augmentation.

William Guey, Pierrick Bougault, Wei Zhang et al.

Who sets the rules for artificial intelligence, and on what terms, has become a defining question of global governance. For several years that contest ran through principles and ethics codes; it now runs through institutions. China's proposed World Artificial Intelligence Cooperation Organization (WAICO) is the most consequential recent entrant and the least examined. We place WAICO within the emerging regime complex for AI and argue that its importance lies not in any single commitment but in the position it is designed to hold. Coding a cross-section of fifteen international AI governance instruments and institutions on how they admit members, how they are organized, and what they prioritize, we find that WAICO's proposed design joins three features that no constituted multilateral body currently combines: membership open to any sovereign state, no values or regime-type test for entry, and an agenda built around development and the global capability divide. The incumbent Western-led bodies gate membership by shared values and concentrate on rights and safety; the universal United Nations bodies are open but anchored in human rights; a development-first agenda is otherwise carried by the regional strategies of the Global South. Among constituted institutions, the only occupant of WAICO's intended position is China's own 2023 precursor initiative. We read this as the formation of a second, still-proposed pole in global AI governance, organized around sovereignty and development rather than rights and safety, and argue that WAICO would be the first standing organization built to anchor it. We report the full coding, state testable expectations against which the claim can be judged as the organization takes shape, and release the dataset for replication.