arXiv:2505.16953v3 Announce Type: replace-cross Abstract: Multimodal learning is of continued interest in artificial intelligence-based applications, motivated by the potential information gain from combining different data modalities. However, modalities observed in the source environment may differ from the modalities observed in the target environment due to multiple factors, including cost, hardware failure, or the perceived textit{informativeness} of a given modality. This change in missingness patterns between the source and target environment has not been carefully studied. Na{“i}ve estimation of […]

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arXiv:2603.19648v1 Announce Type: cross Abstract: Stochastic approximation (SA) is a fundamental iterative framework with broad applications in reinforcement learning and optimization. Classical analyses typically rely on martingale difference or Markov noise with bounded second moments, but many practical settings, including finance and communications, frequently encounter heavy-tailed and long-range dependent (LRD) noise. In this work, we study SA for finding the root of a strongly monotone operator under these non-classical noise models. We establish the first finite-time moment bounds […]

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arXiv:2602.15975v1 Announce Type: new Abstract: As cyber threats grow in complexity and scale, many security incidents remain poorly managed due to the lack of proper training among C-level executives. Thus, there is a need for targeted cybersecurity education to enhance executive decision-making and crisis response. Traditional training methods, such as cyber wargames and Tabletop Exercises (TTX), aim to develop abilities to face critical incidents, however, they often lack the interactive and dynamic elements required to prepare individuals for […]

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arXiv:2602.04037v1 Announce Type: new Abstract: Learning domain adaptive policies that can generalize to unseen transition dynamics, remains a fundamental challenge in learning-based control. Substantial progress has been made through domain representation learning to capture domain-specific information, thus enabling domain-aware decision making. We analyze the process of learning domain representations through dynamical prediction and find that selecting contexts adjacent to the current step causes the learned representations to entangle static domain information with varying dynamical properties. Such mixture can […]

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I got interested in ASP.NET Core 3.0 since the first preview and followed it very closely. I started using it since preview2 and will now soon go to production with it (preview9). I compiled this list to have all the new features, improvements and breaking changes that happened during this time for easy access and accessibility.

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arXiv:2602.18447v1 Announce Type: new Abstract: Chain-of-Thought reasoning significantly improves the performance of large language models on complex tasks, but incurs high inference latency due to long generation traces. Step-level speculative reasoning aims to mitigate this cost, yet existing approaches face a long-standing trade-off among accuracy, inference speed, and resource efficiency. We propose ConfSpec, a confidence-gated cascaded verification framework that resolves this trade-off. Our key insight is an asymmetry between generation and verification: while generating a correct reasoning step […]

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Mechanistic models encode scientific knowledge about dynamical systems and are widely used in downstream scientific and policy applications. Recent work has explored LLM-based agentic frameworks to automatically construct mechanistic models from data; however, existing problem settings substantially oversimplify real-world conditions, leaving it unclear whether LLM-generated mechanistic models are reliable in practice. To address this gap, we introduce the Neural-Integrated Mechanistic Modeling (NIMM) evaluation framework, which evaluates LLM-generated mechanistic models under realistic settings with partial observations and diversified task […]

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arXiv:2603.24785v1 Announce Type: new Abstract: Precision agriculture promises higher yields and sustainability, but adoption is slowed by the high cost of cyber-physical systems (CPS) and the lack of systematic design methods. We present a cost-aware design space exploration (DSE) framework for multimodal drone-rover platforms to integrate budget, energy, sensing, payload, computation, and communication constraints. Using integer linear programming (ILP) with SAT-based verification, our approach trades off among cost, coverage, and payload while ensuring constraint compliance and a multitude […]

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