OS-level virtualization (e.g., Linux containers) has become a cornerstone of modern cloud systems. While it offers the illusion of isolated kernels for processes, these processes share the same underlying kernel, raising critical concerns around security, fault isolation, and the inability to customize kernels. Existing solutions address the issues by employing virtual machines that isolate kernels. However, these approaches incur significant performance overhead.

Mixed-precision quantization (MPQ) is a vital technique for deep learning model compression. In IQP-based MPQ, the sensitivity matrix capturing cross-layer dependencies is often not positive semi-definite (PSD), and the conventional eigenvalue-clipping correction introduces ordering distortion that can lead to suboptimal bit allocations. In this paper, we propose a novel MPQ framework using Shift-PSD, a structure-preserving PSD correction method combined with block-level sparsification. Our approach provably eliminates ordering distortion while preserving the exact optimal solutions, simultaneously achieving lightweight computation. Specifically, our two-stage framework first sparsifies the sensitivity matrix to remove noisy long-range interactions, and then enforces the PSD property via a spectral shift that adds only a constant offset over the feasible set. Comprehensive experiments demonstrate that the proposed framework achieves superior accuracy with lower computational cost (BitOps) in the highly compressed region.

Mitigating positional bias of language models (LMs) for listwise inputs is a well-known and important problem (e.g., lost-in-the-middle). While zero-shot order-invariant LMs have been proposed to solve this issue, their success on practical listwise problems has been limited. In this work, as a first contribution, we identify and overcome two limitations to make zero-shot invariant LMs more practical: (1) training and inference distribution mismatch arising from modifying positional ID assignments to enforce invariance, and (2) failure to adapt to mixture of order-invariant and sensitive inputs in practical listwise problems. Then, to overcome these issues we propose (1) RoToR, a zero-shot invariant LM for genuinely order-invariant inputs with minimal modifications of positional IDs, and (2) Selective Routing, an adaptive framework that handles both order-invariant and order-sensitive inputs in listwise tasks. On the Lost in the middle (LitM), Knowledge Graph QA (KGQA), and MMLU benchmarks, we show that RoToR with Selective Routing can effectively handle practical listwise input tasks in a zero-shot manner (https://github.com/soyoung97/RoToR)

Kernel concurrency failures are notoriously difficult to identify and diagnose their fundamental reason, the root cause. Kernel concurrency bugs frequently involve challenging patterns such as multi-variable races, data races with asynchronous kernel threads, and pervasive benign races. We perform an in-depth study of real-world kernel concurrency bugs and elicit three requirements: comprehensiveness, pattern-agnostic, and conciseness.

Connectionist Temporal Classification (CTC) model is a very efficient method for modeling sequences, especially for speech data.In order to use the CTC model as an Automatic Speech Recognition (ASR) task, the beam search decoding with an external language model like n-gram LM is necessary to obtain reasonable results.In this paper, we analyze the blank label in CTC beam search deeply and propose a very simple method to reduce the amount of calculation resulting in faster beam search decoding speed.With this method, we can get up to 78% faster decoding speed than ordinary beam search decoding with a very small loss of accuracy in LibriSpeech datasets.We prove this method is effective not only practically by experiments but also theoretically by mathematical reasoning.We also observe that this reduction is more obvious if the accuracy of the model is higher. 1

Binary lifting, which is to translate a binary executable to a high-level intermediate representation, is a primary step in binary analysis. Despite its importance, there are only few existing approaches to testing the correctness of binary lifters. Furthermore, the existing approaches suffer from low test coverage, because they largely depend on random test case generation. In this paper, we present the design and implementation of the first systematic approach to testing binary lifters. We have evaluated the proposed system on 3 state-of-the-art binary lifters, and found 24 previously unknown semantic bugs. Our result demonstrates that writing a precise binary lifter is extremely difficult even for those heavily tested projects.