arXiv:2602.12483v1 Announce Type: new Abstract: Randomized Kaczmarz (RK) is a simple and fast solver for consistent overdetermined systems, but it is known to be fragile under noise. We study overdetermined $mtimes n$ linear systems with a sparse set of corrupted equations, $ {bf A}{bf x}^star = {bf b}, $where only $tilde{bf b} = {bf b} + boldsymbol{varepsilon}$ is observed with $|boldsymbol{varepsilon}|_0 le beta m$. The recently introduced QuantileRK (QRK) algorithm addresses this issue by testing residuals against a […]

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arXiv:2603.25885v1 Announce Type: new Abstract: Superconducting Single Flux Quantum (SFQ) logic offers a promising platform for ultra-low-power, high-frequency computing. However, their pulse-based nature poses challenges for scalable modeling, design, and verification using conventional hardware description languages (HDLs), which are designed for level-based digital logic. Prior efforts have required complex Verilog support modules to enable Standard Delay Format (SDF) compatibility and have provided limited coverage of SFQ cell types. This work presents a Verilog-based modeling framework for SFQ gates […]

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arXiv:2604.05087v1 Announce Type: new Abstract: Document expansion is a classical technique for improving retrieval quality, and is attractive since it shifts computation offline, avoiding additional query-time processing. However, when applied to modern retrievers, it has been shown to degrade performance, often introducing noise that obfuscates the discriminative signal. We recast document expansion as a document optimization problem: a language model or a vision language model is fine-tuned to transform documents into representations that better align with the expected […]

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The preparation of annotated datasets remains a critical bottleneck in the machine learning (ML) pipeline. Existing tools are fragmented across cloud-hosted services, self-hosted web applications, and lightweight desktop tools—none simultaneously ad-dressing diverse annotation modalities, offline-first operation, integrated training, and serverless collaboration. We present Annotix, an open-source, cross-platform desktop application built on a Rust backend (Tauri 2) and React 19 frontend, designed to unify the entire ML data preparation workflow within a single privacy-preserving environ-ment. To evaluate its practical […]

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arXiv:2603.26668v1 Announce Type: new Abstract: As an important paradigm for enhancing the generation quality of Large Language Models (LLMs), retrieval-augmented generation (RAG) faces the two challenges regarding retrieval accuracy and computational efficiency. This paper presents a novel RAG framework called Bridge-RAG. To overcome the accuracy challenge, we introduce the concept of abstract to bridge query entities and document chunks, providing robust semantic understanding. We organize the abstracts into a tree structure and design a multi-level retrieval strategy to […]

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arXiv:2602.00943v1 Announce Type: new Abstract: Cold-start exploration is a core challenge in large-scale recommender systems: new or data-sparse items must receive traffic to estimate value, but over-exploration harms users and wastes impressions. In practice, Thompson Sampling (TS) is often initialized with a uniform Beta(1,1) prior, implicitly assuming a 50% success rate for unseen items. When true base rates are far lower, this optimistic prior systematically over-allocates to weak items. The impact is amplified by batched policy updates and […]

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arXiv:2602.08467v1 Announce Type: cross Abstract: Multivariate time series (MTS) anomaly diagnosis, which encompasses both anomaly detection and localization, is critical for the safety and reliability of complex, large-scale real-world systems. The vast majority of existing anomaly diagnosis methods offer limited theoretical insights, especially for anomaly localization, which is a vital but largely unexplored area. The aim of this contribution is to study the learning process of a Transformer when applied to MTS by revealing connections to statistical time […]

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This paper, which is entirely unconditional, proves a sharpened almost-all theorem with fully explicit effective constants for the restricted weighted Goldbach sum R_{a,q}(N) := sum over p1+p2=N, p1 = a (mod q), of (log p1)(log p2), with q >= 1 and gcd(a,q) = 1, whose expected main term is M_{a,q}(N) = C_2 * S(N) * N / phi(q), where C_2 = 0.6601618… is the twin-prime constant and S(N) is the binary singular series.The results are organised around four […]

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