Tue 1 Sept

Valuable data remains embedded in unstructured sources: web pages, reports, contracts, filings, earnings calls, and PDFs. The big bet in enterprise AI is deploying LLM agents that reason over this data to answer complex questions for every knowledge worker. Agents can do this today, but at prohibitive cost. Each question repeatedly opens large documents to recover scattered evidence, consuming up to a million tokens. However, if the data were already structured, the same question would reduce to a cheap database lookup. For example, on FanOutQA benchmark, reasoning over an ideal pre-structured…

The effect of Large Language Model (LLM) scale on ontology learning (OL) performance remains insufficiently characterized. We present a controlled evaluation of 13 models spanning dense and Mixture-of-Experts variants from the Qwen3.5 and Qwen3.6 lineages, together with proprietary GPT release variants, using the OntoLearner retrieval-augmented generation pipeline. All models are evaluated with the same embedding model, retrieval configuration, prompt templates, decoding settings, datasets, and metrics on term typing, taxonomy discovery, and non-taxonomic relationship extraction across four bi…

Ontology alignment (OA) has evolved through several methodological paradigms, ranging from lexical and structural aligners to knowledge graph embedding (KGE) models and, more recently, Large Language Model (LLM)-based approaches. Although modern OA frameworks provide unified ecosystems for deploying these heterogeneous aligners, mechanisms for systematically reconciling their complementary and sometimes conflicting predictions remain relatively underexplored. We present OntoAligner-Ensemble, a modular and aligner-agnostic framework that combines candidate correspondences through a configurable…

Security updates have been issued by Debian (kernel, libarchive, libdbi-perl, libnet-dns-perl, librabbitmq, roundcube, starlette, and xrdp), Fedora (bluez, postgresql16-anonymizer, pyOpenSSL, python-cryptography, python-pynitrokey, and rust-h2), Gentoo (Chromium, Google Chrome, Microsoft Edge, Opera, Freenet, and Tor), Mageia (golang and python-nltk), Red Hat (nodejs22, nodejs24, nodejs:22, and nodejs:24), SUSE (7zip, apptainer, broot, cadvisor, coredns, coturn, distribution-registry, gh, git-lfs, grafana, gzip, java-25-openjdk, libopenssl-3-devel, libsoup-3_0-0, mozjs102, OpenRGB, openssl-3, …

By the time Linus Torvalds released 7.3-rc1 and closed the merge window for this release, 15,267 non-merge changesets had been pulled into the mainline repository. That is the second-highest commit count for an -rc1 release in the kernel's history; only the 6.7-rc1 release, which included nearly 3,000 commits of bcachefs history, had more. About 13,000 of those commits entered the mainline after the first 7.3 merge-window summary was written so, needless to say, there are a lot of changes to cover.

The Netdev 0x1A conference was held in Rome, Italy from July 13 through July 16. Conference organizer Jami Hadi Salim has let us know that the videos and slides for all sessions are now available. Topics include Linux QUIC, shared memory socket transport, eBPF-based DDoS protection, and more.

Version 4.0 of the OpenShot video editor has been released. OpenShot 4.0 has arrived, bringing some of the biggest creative workflow upgrades in our history. You can now record your screen, webcam, microphone, and system audio directly into a project. You can correct and grade footage with color wheels, curves, LUTs, and professional video scopes. You can also isolate subjects with locally run machine learning models and create everything from animated audio visualizations to cinematic film looks. See the release notes for a full list of changes.

Database Management Systems (DBMSs) support multiple SQL mechanisms for representing intermediate query results, including VIEWs, Common Table Expressions (CTEs), and Temporary Tables (TEMPTs). When these mechanisms are used to represent the same intermediate query result, the corresponding queries are expected to produce consistent results. However, we observe that such queries can return inconsistent results, indicating potential DBMS logic bugs. Existing approaches for detecting DBMS logic bugs have never explored result consistency across such equivalent representations. In this paper, we …

Modern data-intensive applications increasingly require database systems to manage structured records and graph data. This demand gives rise to graph-relational data management, spanning storage, query processing, and optimization across relational and graph data. In response, relational database extensions, multi-model databases, and dedicated graph-relational systems have emerged with diverse architectures. However, evaluation methodologies have not kept pace. Existing relational and graph benchmarks assess the two models largely in isolation, while multi-model benchmarks provide limited cov…

This paper investigates distributed online optimization for multi-agent dynamical systems with constrained inputs and time-varying cost functions. While online convex optimization offers a principal framework for sequential decision-making, existing online learning and optimization algorithms typically require accurate system models, limiting their applicability in practical settings. To overcome this challenge, we propose a distributed bandit online feedback optimization algorithm that relies solely on real-time input-output data. The algorithm employs a smoothing zeroth-order one-point estim…

Unlike the seconds-scale tool-call pauses targeted by prior agent-serving systems, agentic LLM requests can be suspended for minutes or hours while waiting for human approval. We study how suspension and resumption affect GPU serving performance and develop a retention policy that balances active-serving capacity against future recomputation under uncertain approval waits. The central tension is severe because retaining suspended KV preserves fast resume but can consume enough GPU capacity to reduce active-serving goodput by 41%, while evicting it avoids that residency cost at the expense of n…

We present a new distributed algorithm for computing a minimum spanning tree (MST) in the \textsf{CONGEST-KT$_{1}$} model, where messages are limited to $O(\log n)$ bits and each vertex initially knows the identifiers of its neighbors. Our algorithm exposes a two-parameter time--message trade-off: for any $0 \leq λ\leq κ\leq 1/2$, it runs in $\tilde{O}(n^λD_G + n^{1 - κ- λ} + n^{1 - 2κ+ λ} + n^{1/2})$ rounds and uses $\tilde{O}(\min\{m, n^{1 + κ}\})$ messages, where $n$, $m$, and $D_G$ are the number of vertices, edges, and thenetwork diameter, respectively. In particular, setting $(κ, λ) = (1…

Combining event and tracking data is fundamental to modern soccer analytics, yet the two sources are rarely well aligned: event timestamps recorded by human annotators often miss the true moment of the action, distorting the spatiotemporal context that downstream models rely on. Existing synchronization methods depend on noisy human-annotated event locations and fail to detect ball receptions, obscuring when each player gains ball possession. To address these limitations, we propose ELASTIC (Event-Location-AgnoSTIC synchronizer), a framework that infers the start and end timestamps of events s…