arXiv:2604.01235v1 Announce Type: new Abstract: Structured LLM routing is often treated as a prompt-engineering problem. We argue that it is, more fundamentally, a systems-level burden-allocation problem. As large language models (LLMs) become core control components in agentic AI systems, reliable structured routing must balance correctness, latency, and implementation cost under real deployment constraints. We show that this balance is shaped not only by prompts or schemas, but also by how structural work is allocated across the generation stack: […]

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arXiv:2602.18539v1 Announce Type: new Abstract: Digital identity verification often forces a privacy trade-off, where users must disclose sensitive personal data to prove simple eligibility criteria. As blockchain applications integrate with regulated environments, this over-disclosure creates significant risks of data breaches and surveillance. This work proposes a general Selective Disclosure Framework built on Ethereum, designed to decouple attribute verification from identity revelation. By utilizing client-side zk-SNARKs, the framework enables users to prove specific eligibility predicates without revealing underlying identity […]

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arXiv:2603.12300v1 Announce Type: new Abstract: The increasing adoption of server-side component-based web frameworks has introduced new application-layer attack surfaces that remain insufficiently understood at Internet scale. On 3 December 2025, a critical remote code execution vulnerability (CVE-2025-55182) in React Server Components, referred to as React2Shell, was publicly disclosed and subsequently observed being exploited in the wild. Despite its critical severity and a CVSS base score of 10.0, there is limited empirical understanding of how this vulnerability is exploited […]

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There is a growing demand for efficient data removal to comply with regulations like the GDPR and to mitigate the influence of biased or corrupted data. This has motivated the field of machine unlearning, which aims to eliminate the influence of specific data subsets without the cost of full retraining. In this work, we propose a statistical framework for machine unlearning with generic loss functions and establish theoretical guarantees. For squared loss, especially, we develop Unlearning Least Squares […]

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arXiv:2603.28346v1 Announce Type: cross Abstract: Efficient estimation of high-dimensional matrices-including covariance and precision matrices-is a cornerstone of modern multivariate statistics. Most existing studies have focused primarily on the theoretical properties of the estimators (e.g., consistency and sparsity), while largely overlooking the computational challenges inherent in high-dimensional settings. Motivated by recent advances in learning-based optimization method-which integrate data-driven structures with classical optimization algorithms-we explore high-dimensional matrix estimation assisted by machine learning. Specifically, for the optimization problem of high-dimensional matrix […]

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This study proposes the General Bayes framework for policy learning. We consider decision problems in which a decision-maker chooses an action from an action set to maximize its expected welfare. Typical examples include treatment choice and portfolio selection. In such problems, the statistical target is a decision rule, and the prediction of each outcome $Y(a)$ is not necessarily of primary interest. We formulate this policy learning problem by loss-based Bayesian updating. Our main technical device is a squared-loss […]

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arXiv:2603.15806v1 Announce Type: new Abstract: Vertical farming is a controlled-environment agriculture (CEA) approach in which crops are grown in stacked layers under regulated climate and lighting, enabling predictable production but requiring high electricity input. This study quantifies the techno-economic impact of roof-mounted daylighting in a three-tier container vertical farm using a light-pipe (LP) system that delivers sunlight to the upper tier. The optical chain, comprising a straight duct and a tilting aluminum-coated mirror within a rotating dome, was […]

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arXiv:2604.08613v1 Announce Type: new Abstract: In this report, we present our champion solution for the NTIRE 2026 Challenge on Video Saliency Prediction held in conjunction with CVPR 2026. To exploit complementary inductive biases for video saliency, we propose Video Saliency with Adaptive Gated Experts (ViSAGE), a multi-expert ensemble framework. Each specialized decoder performs adaptive gating and modulation to refine spatio-temporal features. The complementary predictions from different experts are then fused at inference. ViSAGE thereby aggregates diverse inductive biases […]

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