digitado

arXiv:2604.14251v1 Announce Type: new Abstract: Monitoring LLM safety at scale requires balancing cost and accuracy: a cheap latent-space probe can screen every input, but hard cases should be escalated to a more expensive expert. Existing cascades delegate based on probe uncertainty, but uncertainty is a poor proxy for delegation benefit, as it ignores whether the expert would actually correct the error. To address this problem, we introduce Calibrate-Then-Delegate (CTD), a model-cascade approach that provides probabilistic guarantees on the […]

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arXiv:2604.14250v1 Announce Type: new Abstract: An important aspect of crowd monitoring is knowing how many people we are dealing with. Sometimes, knowing the size of a crowd in a single location and at a specific moment is enough. Matters become problematic when counting the same people across dif ferent locations or counting them over longer periods of time. In those cases, we need to identify and later reidentify a person, which immediately leads to privacy concerns. Until recently, […]

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arXiv:2604.14249v1 Announce Type: new Abstract: We introduce Metric-Aware Principal Component Analysis (MAPCA), a unified framework for scale-invariant representation learning based on the generalised eigenproblem max Tr(W^T Sigma W) subject to W^T M W = I, where M is a symmetric positive definite metric matrix. The choice of M determines the representation geometry. The canonical beta-family M(beta) = Sigma^beta, beta in [0,1], provides continuous spectral bias control between standard PCA (beta=0) and output whitening (beta=1), with condition number kappa(beta) […]

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arXiv:2604.14248v1 Announce Type: new Abstract: High-level synthesis (HLS) tools offer limited support for Engineering Change Orders (ECOs), making late-stage design modifications challenging and costly. This paper introduces a graph-based ECO methodology tailored for Google XLS. A Graph Edit Distance (GED) algorithm is used to detect structural differences between original and revised intermediate representations (IRs), which are then transformed into patch operations. A patch application mechanism is developed to enforce XLS IR constraints while preserving semantic correctness, together with […]

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arXiv:2604.14246v1 Announce Type: new Abstract: Sparse Mixture-of-Experts (MoE) models have achieved remarkable scalability, yet they remain vulnerable to hallucinations, particularly when processing long-tail knowledge. We identify that this fragility stems from static Top-$k$ routing: routers tend to favor high-frequency patterns over rare factual associations. Consequently, “specialist experts” possessing critical long-tail knowledge are often assigned low gating scores and remain “dormant” — under-prioritized for specific tokens despite their proven causal importance on other inputs. To address this, we propose […]

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arXiv:2604.14243v1 Announce Type: new Abstract: Real-world decision-making systems operate in environments where state transitions depend not only on the agent’s actions, but also on textbf{exogenous factors outside its control}–competing agents, environmental disturbances, or strategic adversaries–formally, $s_{h+1} = f(s_h, a_h, bar{a}_h)+omega_h$ where $bar{a}_h$ is the adversary/external action, $a_h$ is the agent’s action, and $omega_h$ is an additive noise. Ignoring such factors can yield policies that are optimal in isolation but textbf{fail catastrophically in deployment}, particularly when safety constraints must […]

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arXiv:2604.14242v1 Announce Type: new Abstract: Cloud sovereignty can no longer be defined by data residency or infrastructure location alone. Under conditions of geopolitical disruption, legal exposure, and expanding service boundaries, sovereignty must be understood as enforceable control over how digital services are governed, operated, and recovered. This paper introduces Sovereign 2.0, a control-plane-centric model that extends sovereignty beyond localisation to include governance authority, privileged access, cryptographic trust, data lifecycle control, observability, and incident response across federated environments. We […]

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arXiv:2604.14240v1 Announce Type: new Abstract: The simulation of complex systems increasingly relies on sophisticated but fundamentally opaque computational black-box simulators. Surrogate models play a central role in reducing the computational cost of complex systems simulations across a wide range of scientific and engineering domains. Notwithstanding, they inevitably inherit and often exacerbate this black-box nature, obscuring how input variables drive physical responses. Conversely, Explainable Artificial Intelligence (XAI) offers powerful tools to unpack these models. Yet, XAI methods struggle with […]

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arXiv:2604.14237v1 Announce Type: new Abstract: Transistor topology optimization is a critical step in standard cell design, directly dictating diffusion sharing efficiency and downstream routability. However, identifying optimal topologies remains a persistent bottleneck, as conventional exhaustive search methods become computationally intractable with increasing circuit complexity in advanced nodes. This paper introduces TOPCELL, a novel and scalable framework that reformulates high-dimensional topology exploration as a generative task using Large Language Models (LLMs). We employ Group Relative Policy Optimization (GRPO) to […]

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arXiv:2604.14235v1 Announce Type: new Abstract: Fraud detection on graph data can be viewed as a demanding task that requires distinguishing between different types of nodes. Because graph neural networks (GNNs) are naturally suited for processing information encoded in graph form through their message-passing operations, methods based on GNN models have increasingly attracted attention in the fraud detection domain. However, fraud graphs inherently exhibit relation camouflage, high heterophily, and class imbalance, causing most GNNs to underperform in fraud detection […]

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