This paper proposes an impulsive SIRQ model for the analysis of computer network resilience against malware propagation and distributed denial-of-service (DDoS) attacks. The model extends classical epidemic frameworks by combining continuous-time dynamics of malicious object spreading with discrete control actions corresponding to mass updates, node isolation, and access control policies. A qualitative analysis of the resulting system of impulsive differential equations is performed. The basic reproduction number R0 identified as a threshold parameter characterizing the intensity of attack […]

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arXiv:2601.04218v1 Announce Type: new Abstract: The artificial intelligence value chain is one of the main concepts underpinning the European legislation on the subject, especially the Artificial Intelligence Act. It is an economic concept that has become a legal one. i.e., a concept of legal governance, due to its continued use in policy documents and legal texts. This article (i) analyses its significance and function within the framework of the regulatory strategy established by recent EU programs (the Compass […]

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arXiv:2603.03401v1 Announce Type: new Abstract: This paper proposes a novel parameter selection strategy for kernel-based gradient descent (KGD) algorithms, integrating bias-variance analysis with the splitting method. We introduce the concept of empirical effective dimension to quantify iteration increments in KGD, deriving an adaptive parameter selection strategy that is implementable. Theoretical verifications are provided within the framework of learning theory. Utilizing the recently developed integral operator approach, we rigorously demonstrate that KGD, equipped with the proposed adaptive parameter selection […]

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arXiv:2603.08718v1 Announce Type: new Abstract: Register-Transfer Level (RTL) coding is an iterative, repository-scale process in which Power, Performance, and Area (PPA) emerge from interactions across many files and the downstream toolchain. While large language models (LLMs) have recently been applied to hardware design, most efforts focus on generation or debugging from natural-language prompts, where ambiguity and hallucinations necessitate expert review. A separate line of work begins from formal inputs, yet typically optimizes high-level synthesis or isolated modules and […]

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arXiv:2601.04356v1 Announce Type: new Abstract: Contact-rich manipulation requires reliable estimation of extrinsic contacts-the interactions between a grasped object and its environment which provide essential contextual information for planning, control, and policy learning. However, existing approaches often rely on restrictive assumptions, such as predefined contact types, fixed grasp configurations, or camera calibration, that hinder generalization to novel objects and deployment in unstructured environments. In this paper, we present UNIC, a unified multimodal framework for extrinsic contact estimation that operates […]

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Author(s): Rizwanhoda Originally published on Towards AI. Inside Vector Databases: Engineering High-Dimensional Search for Modern AI Systems The real bottleneck in modern AI systems is not the LLM. Photo by Huzeyfe Turan on UnsplashVector databases serve as specialized infrastructure for managing high-dimensional search within modern AI systems, helping to address challenges in quickly retrieving millions of embeddings with accuracy. The article explores their architecture, benefits over traditional databases, and various applications including semantic search, recommendation systems, and multimodal […]

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The rapid evolution of Large Language Models (LLMs) from static text generators to autonomous agents has revolutionized their ability to perceive, reason, and act within complex environments. However, this transition from single-model inference to System Engineering Security introduces unique structural vulnerabilities—specifically instruction-data conflation, persistent cognitive states, and untrusted coordination—that extend beyond traditional adversarial robustness. To address the fragmented nature of the existing literature, this article presents a comprehensive and systematic survey of the security landscape for LLM-based agents. […]

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Let me start with a scenario you’ve probably lived. It’s 2am. A PagerDuty alert fires. You drag yourself to a laptop, log into Grafana, spend 20 minutes correlating dashboards that were last updated in 2022, and eventually trace the problem to a single upstream service that’s been quietly timing out for the past hour. By the time you push a fix and verify recovery, you’ve been awake for two hours, your on-call rotation is resentful, and your system […]

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Hi everyone, I’m working on a research project where my advisor suggested combining reinforcement learning with a transformer model, and I’m trying to figure out what the best architecture might look like. I unfortunately can’t share too many details about the actual project (sorry!), but I’ll try to explain the technical structure as clearly as possible using simplified examples. Problem setup (simplified example) Imagine we have a sequence where each element is represented by a super-token containing many […]

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arXiv:2602.12458v1 Announce Type: new Abstract: A central challenge in multi-agent reinforcement learning is enabling agents to adapt to previously unseen teammates in a zero-shot fashion. Prior work in zero-shot coordination often follows a two-stage process, first generating a diverse training pool of partner agents, and then training a best-response agent to collaborate effectively with the entire training pool. While many previous works have achieved strong performance by devising better ways to diversify the partner agent pool, there has […]

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