Quantum key distribution (QKD) networks require relaying when distant key management entities share no direct quantum link. Most relay strategies, however, rely on centralized control or globally maintained routing state. This paper asks whether useful security and efficiency can still be obtained with topology-oblivious stochastic forwarding. It studies the security-overhead trade-off in a model in which fragmented key material is relayed via random-walk variants and reconstructed under privacy amplification. Under a restricted model with at most one compromised […]

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arXiv:2510.10902v2 Announce Type: replace-cross Abstract: Disclosing information via the publication of a machine learning model poses significant privacy risks. However, auditing this disclosure across every datapoint during the training of Large Language Models (LLMs) is computationally prohibitive. In this paper, we present Gradient Uniqueness (GNQ), a principled, attack-agnostic metric derived from an information-theoretic upper bound on the amount of information embedded in a model about individual training points via gradient descent. While naively computing GNQ requires forming and […]

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Engineering teams often think about infrastructure only when something breaks. A remote connection fails. A server becomes unstable. A workstation loses access to a critical system. A field team cannot synchronize data. An industrial environment starts showing communication issues that no one can explain clearly. At that point, infrastructure suddenly becomes visible. But by then, the real problem has usually been present for a long time. In my experience working with networks, servers, secure connectivity, technical support, and […]

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arXiv:2603.05647v1 Announce Type: new Abstract: People experiencing mental health crises frequently turn to open-ended generative AI (GenAI) chatbots such as ChatGPT for support. However, rather than providing immediate assistance, most GenAI chatbots are designed to respond to crisis situations in ways that minimize their developers’ liability, primarily through avoidance (e.g., refusing to engage beyond templated referrals to crisis hotlines). Withholding crisis support in these cases may harm users who have no viable alternatives and reduce their motivation to […]

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arXiv:2505.18535v2 Announce Type: replace-cross Abstract: We study the convergence properties and escape dynamics of Stochastic Gradient Descent (SGD) in one-dimensional landscapes, separately considering infinite- and finite-variance noise. Our main focus is to identify the time scales on which SGD reliably moves from an initial point to the local minimum in the same ”basin”. Under suitable conditions on the noise distribution, we prove that SGD converges to the basin’s minimum unless the initial point lies too close to a […]

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arXiv:2603.16661v2 Announce Type: replace-cross Abstract: Standard masked discrete diffusion models face limitations in reasoning tasks due to their inability to correct their own mistakes on the masking path. Since they rely on a fixed number of denoising steps, they are unable to adjust their computation to the complexity of a given problem. To address these limitations, we introduce a method based on learning a Markov transition kernel that is trained on its own outputs. This design enables tokens […]

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arXiv:2603.05689v1 Announce Type: new Abstract: Large language models (LLMs) have shown remarkable capabilities in natural language processing tasks, yet their application in hardware security verification remains limited due to scarcity of publicly available hardware description language (HDL) datasets. This knowledge gap constrains LLM performance in detecting vulnerabilities within HDL designs. To address this challenge, we propose SecureRAG-RTL, a novel Retrieval-Augmented Generation (RAG)-based approach that significantly enhances LLM-based security verification of hardware designs. Our approach integrates domain-specific retrieval with […]

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