arXiv:2601.13519v3 Announce Type: replace Abstract: This paper introduces a new problem-dependent regret measure for online convex optimization with smooth losses. The notion, which we call the $G^star$ regret, depends on the cumulative squared gradient norm evaluated at the decision in hindsight. We show that the $G^star$ regret strictly refines the existing $L^star$ (small loss) regret, and that it can be arbitrarily sharper when the losses have vanishing curvature around the hindsight decision. We establish upper and lower bounds […]

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We address real-time sampling and estimation of autoregressive Markovian sources in dynamic yet structurally similar multi-hop wireless networks. Each node caches samples from others and communicates over wireless collision channels, aiming to minimize time-average estimation error via decentralized policies. Due to the high dimensionality of action spaces and complexity of network topologies, deriving optimal policies analytically is intractable. To address this, we propose a graphical multi-agent reinforcement learning framework for policy optimization. Theoretically, we demonstrate that our proposed […]

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arXiv:2602.11324v1 Announce Type: new Abstract: A key principle in string processing is local consistency: using short contexts to handle matching fragments of a string consistently. String synchronizing sets [Kempa, Kociumaka; STOC 2019] are an influential instantiation of this principle. A $tau$-synchronizing set of a length-$n$ string is a set of $O(n/tau)$ positions, chosen via their length-$2tau$ contexts, such that (outside highly periodic regions) at least one position in every length-$tau$ window is selected. Among their applications are faster […]

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As a senior cloud engineer who has architected solutions across multiple cloud platforms, I’ve learned that choosing the right load balancer can make or break your application’s performance, scalability, and cost efficiency. This guide dives deep into the load balancing offerings from AWS, GCP, and Azure, explaining how they work internally and when to use each type. Understanding Load Balancer Fundamentals Before we dive into specific offerings, let’s understand what happens inside a load balancer. At its core, […]

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arXiv:2601.20918v1 Announce Type: new Abstract: Context: Blockchain and AI are increasingly explored to enhance trustworthiness in software engineering (SE), particularly in supporting software evolution tasks. Method: We conducted a systematic literature review (SLR) using a predefined protocol with clear eligibility criteria to ensure transparency, reproducibility, and minimized bias, synthesizing research on blockchain-enabled trust in AI-driven SE tools and processes. Results: Most studies focus on integrating AI in SE, with only 31% explicitly addressing trustworthiness. Our review highlights six […]

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arXiv:2601.05466v1 Announce Type: new Abstract: Large language models (LLMs) have demonstrated remarkable capabilities across diverse applications, however, they remain critically vulnerable to jailbreak attacks that elicit harmful responses violating human values and safety guidelines. Despite extensive research on defense mechanisms, existing safeguards prove insufficient against sophisticated adversarial strategies. In this work, we propose iMIST (underline{i}nteractive underline{M}ulti-step underline{P}rogreunderline{s}sive underline{T}ool-disguised Jailbreak Attack), a novel adaptive jailbreak method that synergistically exploits vulnerabilities in current defense mechanisms. iMIST disguises malicious queries as […]

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In this article we develop a new method for summarizing a ranking distribution, textit{i.e.} a probability distribution on the symmetric group $mathfrak{S}_n$, beyond the classical theory of consensus and Kemeny medians. Based on the notion of textit{local ranking median}, we introduce the concept of textit{consensus ranking distribution} ($crd$), a sparse mixture model of Dirac masses on $mathfrak{S}_n$, in order to approximate a ranking distribution with small distortion from a mass transportation perspective. We prove that by choosing the […]

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