Fri 28 Aug

Recent advances in inference-time scaling have significantly improved the reasoning performance of large language models (LLMs). However, these methods typically rely on repeated generation or external verification. To address this limitation, we introduce CritICL, a novel inference-time framework that improves reasoning while maintaining high efficiency. Our key insight is that LLM failure modes exhibit structured patterns across model scales within the same family. Instead of treating failures as undesirable outputs, CritICL leverages them as a source of guidance. Specifically, we utilize fa…

LLM agents increasingly rely on generated interaction data to learn how to interact with external environments. Agentic data generation must maintain consistency among environments, tasks, interactions, and success signals while producing experience that is useful rather than merely abundant. Existing work spans many agent domains, but domain-centered organization and heterogeneous evaluation often obscure common generation mechanisms and conflate candidate construction with verification and selection. This work develops a two-level framework for the field. First, we represent agentic data as …

Reinforcement Learning with Verifiable Rewards (RLVR) significantly improves LLM reasoning but often causes a drop in policy entropy, leading to narrowed reasoning coverage and degraded pass@$k$ for large $k$. While existing methods mitigate this entropy collapse through algorithmic regularizations, cross-model non-parametric perturbation is also neglected. In this work, we propose a simple yet effective approach to preserve the generative diversity of LLMs during RLVR. Instead of relying solely on internal exploration, we force the target model to generate answers based on partial reasoning t…

Greg Kroah-Hartman has announced the release of the 7.2.2 , 7.1.12 , 6.18.48 , 6.12.107 , 6.6.155 , 6.1.186 , 5.15.219 , and 5.10.268 stable kernels. Each of these contains a single fix for a vulnerability ( CVE-2026-80590 ) that allows marking IPv4 or IPv6 fragments as GSO , which can allow an unprivileged user to cause a kernel panic. This vulnerability has been present since Linux 2.6.27. Users are advised to upgrade.

Security updates have been issued by AlmaLinux (assertj-core, golang, httpd, kernel, and libxml2), Debian (chromium and suricata-update), Fedora (rust-h2), Mageia (avahi and python-django), Oracle (kernel and mingw-openssl), SUSE (c-ares-devel, dracut, gh, gstreamer-plugins-bad, java-11-openjdk, liboqs, librest0_7, openssl, openssl-3, pcp, python313-mistune, python36-pip, qt6-svg, rmt-server, rsync, suseconnect-ng, texlive, tor, wicked, and xmlrpc-c), and Ubuntu (linux-azure, linux-azure-4.15, linux-azure-fips, linux-azure-6.8, linux-ibm, linux-ibm-6.8, linux-oracle-6.8, linux-raspi, linux-ras…

Security updates have been issued by AlmaLinux (assertj-core, attr, firefox, go-toolset:rhel8, golang, grafana, gstreamer1-plugins-good, httpd, kernel, mingw-openssl, mod_http2, nginx, nginx:1.24, pam, polkit, and sqlite), Debian (bubblewrap, cockpit, emacs, gimp, libdbi-perl, openjdk-11, openjdk-17, wireshark, and xrdp), Fedora (bluez, curl, emacs, golang, knot, libopenmpt, libsoup3, openbao, openssh, rsync, rust-anstyle-hyperlink, rust-anstyle-progress, rust-cargo, rust-cargo-c, rust-cargo-credential-libsecret, rust-cargo-util, rust-cargo-util-schemas, rust-cargo-util-terminal, rust-crates-i…

Greg Kroah-Hartman has announced the release of the 7.2.1 , 7.1.11 , 6.18.47 , 6.12.106 , 6.6.154 , 6.1.185 , 5.15.218 , and 5.10.267 stable kernels. Each of these has important updates throughout the tree; users are advised to upgrade.

Virtualization can increase CPU utilization by allowing a large number of virtual CPUs to share a smaller number of physical CPUs. The amount of CPU time that is actually available does not change, though, so heavy activity on too many virtual CPUs can lead to contention and significant performance loss. The steal governor patch series from Shrikanth Hegde is an attempt to address that problem with a mechanism that allows virtual machines to voluntarily reduce the number of virtual CPUs they use when contention is high.

Empirical comparisons between systems are a standard form of evidence in computer science research, but few are checked for statistical validity: most are never framed as statistical tests at all. Existing multiple-comparison procedures could control the resulting error, but need inputs (what an analysis examined, and how its observations are arranged) that are not recoverable from a list of p-values. We introduce Tacet, a language in which an analysis declares what it generated, states what it expects to find, and is refused any claim it cannot afford or cannot properly test. Its core calculu…

We study binary consensus in the \emph{stochastic broadcast model}, which assumes $n\geq 2$ processes communicating synchronously by message broadcasts. At each round, every process broadcasts a message to all the other processes. Each broadcast succeeds independently with some probability $p\in[0,1]$. If a broadcast succeeds, all processes receive the message, and if it fails, no process receives the message. The sender does not know whether its broadcast was successful or not. In this model, consensus is not solvable; the objective is to design, for a given number of rounds $r$, consensus al…

Whole-slide images (WSIs) are central to computational pathology but are prohibitively large, making patch-based processing the practical unit for foundation model inference. At scale, however, generating and handling massive numbers of patches on quickly introduces significant I/O and orchestration overhead, often dominating end-to-end performance. We present a decoupled, I/O-aware pipeline for large-scale WSI embedding extraction that decomposes the workflow into three stages: (1) patch generation and staging, (2) embarrassingly parallel embedding inference, and (3) sharded vector database i…

Reinforcement Learning with Verifiable Rewards (RLVR) and other RL-style post-training paradigms have been used for aligning large language models (LLMs) with reasoning standards. The resulting recent Reasoning Language Models (RLMs) such as DeepSeek-R1, o3, and Kimi k1.5 show that such RL-style post-training ("RL-for-LLMs") can substantially improve chain-of-thought reasoning, long-horizon planning, and self-correction. However, the computational footprint of these systems is massive: state-of-the-art RLM training requires millions of GPU-hours and tightly coupled multi-model pipelines that s…

Distributed Quantum Computing (DQC) addresses the physical scaling limitations of monolithic quantum processors by networking modular Quantum Processing Units (QPUs). Efficient execution of quantum algorithms on DQC architectures requires compiling them across QPUs while minimizing inter-QPU communication bottlenecks, primarily through circuit partitioning. However, current evaluations of state-of-the-art partitioning heuristics focus primarily on the total entanglement cost of the partitions, failing to capture the broader structural and temporal overheads introduced by distributed network co…

Byzantine fault-tolerant (BFT) systems are, in principle, an appealing foundation for transactional applications involving mutually distrustful participants. Yet their adoption has been hampered by two persistent stumbling blocks-performance and developer convenience-which are often in tension with one another. Recent systems show promising progress on both fronts by shifting to a client-centric architecture; clients execute transactions locally and concurrently, while the system resolves any data conflicts to maintain database serializability. We argue that, in its current form, this approach…