technocracy

arXiv:2603.15705v1 Announce Type: new Abstract: Over the past few years an increasing number of states in the US have adopted new privacy laws. The majority of these laws require compliance with universal opt-out mechanisms (UOOMs), which allow consumers to send legally binding opt-out signals. However, a number of laws generally do not allow UOOMs to be enabled by default. While some laws exempt privacy-protective software from this prohibition, the exemption does not apply to pre-installed software, e.g., a […]

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arXiv:2602.02597v1 Announce Type: new Abstract: Large language models are transforming systems research by automating the discovery of performance-critical algorithms for computer systems. Despite plausible codes generated by LLMs, producing solutions that meet the stringent correctness and performance requirements of systems demands iterative optimization. Test-time reinforcement learning offers high search efficiency but requires parameter updates infeasible under API-only access, while existing training-free evolutionary methods suffer from inefficient context utilization and undirected search. We introduce ContextEvolve, a multi-agent framework that […]

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arXiv:2603.06684v1 Announce Type: new Abstract: Aggregate size and shape are key properties for determining quality of aggregate materials used in road construction and transportation geotechnics applications. The composition and packing, layer stiffness, and load response are all influenced by these morphological characteristics of aggregates. Many aggregate imaging systems developed to date only focus on analyses of individual or manually separated aggregate particles. There is a need to develop a convenient and affordable system for acquiring 3D aggregate information […]

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arXiv:2602.10133v1 Announce Type: new Abstract: Despite the growing capabilities of autonomous agents powered by large language models (LLMs), their adoption in high-stakes domains remains limited. A key barrier is security: the inherently nondeterministic behavior of LLM agents defies static auditing approaches that have historically underpinned software assurance. Existing security methods, such as proxy-level input filtering and model glassboxing, fail to provide sufficient transparency or traceability into agent reasoning, state changes, or environmental interactions. In this work, we introduce […]

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New York, United States, April 21st, 2026/CyberNewswire/–BreachLock, a global leader in offensive security, today announced it has been named a representative vendor in the 2026 Gartner Market Guide for Adversarial Exposure Validation. This recognition marks the first time BreachLock has been identified in the Adversarial Exposure Validation (AEV) category since launching its agentic AI-powered Adversarial Exposure Validation platform in 2025. Not only has the company gained recognition in the AEV market quickly, but BreachLock has also emerged as the only vendor […]

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arXiv:2602.20282v1 Announce Type: new Abstract: In this work, we discuss low-parametric approaches for approximating SimRank matrices, which estimate the similarity between pairs of nodes in a graph. Although SimRank matrices and their computation require a significant amount of memory, common approaches mostly address the problem of algorithmic complexity. We propose two major formats for the economical embedding of target data. The first approach adopts a non-symmetric form that can be computed using a specialized alternating optimization algorithm. The […]

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arXiv:2408.12175v3 Announce Type: replace-cross Abstract: Aleatoric (data) and epistemic (knowledge) uncertainty are textbook components of Uncertainty Quantification. Jointly estimating these components has been shown to be problematic and non-trivial. As a result, there are multiple ways to disentangle these uncertainties, but current methods to evaluate them are insufficient. We propose that aleatoric and epistemic uncertainty estimates should be orthogonally disentangled – meaning that each uncertainty is not affected by the other – a necessary condition that is often […]

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In recent years, a variety of powerful agentic workflows have been applied to solve a wide range of human problems. However, existing workflow orchestration still faces key challenges, including high manual cost, reliance on specific operators/large language models (LLMs), and sparse reward signals. To address these challenges, we propose FlowSteer, an end-to-end reinforcement learning framework that takes a lightweight policy model as the agent and an executable canvas environment, automating workflow orchestration through multi-turn interaction. In this process, […]

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