Architectural Designer and AI researcher Samuvel Benhursha is formalizing a constraint-first workflow for AI-assisted architectural transformation, validated through peer-reviewed publication, invited teaching, competition recognition, and public exhibitions. Generative AI can now produce architectural imagery in seconds. A few words, a reference image, and an algorithm will deliver a dramatic skyline, a futuristic museum, or a reimagined temple glowing at sunset. The results are often visually striking. But speed has a side effect: it can flatten the layered obligations […]

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arXiv:2602.17681v1 Announce Type: new Abstract: Post-training quantization (PTQ) is a widely used approach for reducing the memory and compute costs of large language models (LLMs). Recent studies have shown that applying invertible transformations to activations can significantly improve quantization robustness by reducing activation outliers; however, existing approaches are largely restricted to rotation or Hadamard-based transformations. Moreover, most studies focused primarily on traditional quantization schemes, whereas modern hardware increasingly supports the microscaling (MX) data format. Attempts to combine both […]

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arXiv:2601.09750v1 Announce Type: new Abstract: Large language models (LLMs) have proven to work well in question-answering scenarios, but real-world applications often require access to tools for live information or actuation. For this, LLMs can be extended with tools, which are often defined in advance, also allowing for some fine-tuning for specific use cases. However, rapidly evolving software landscapes and individual services require the constant development and integration of new tools. Domain- or company-specific tools can greatly elevate the […]

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arXiv:2602.01013v1 Announce Type: new Abstract: The rapid growth of hyperscale data centers driven by Large Language Models and Artificial Intelligence workloads has introduced new challenges for power systems. These facilities experience abrupt power variations during model training and check-point-saving events, causing voltage deviations and frequency disturbances. Moreover, they operate as passive loads that draw power without offering any grid support. This paper presents an integrated architecture that combines Battery Energy Storage Systems (BESSs) within data centers using Grid-Forming […]

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In this paper, we consider the problem of replicable realizable PAC learning. We construct a particularly hard learning problem and show a sample complexity lower bound with a close to $(log|H|)^{3/2}$ dependence on the size of the hypothesis class $H$. Our proof uses several novel techniques and works by defining a particular Cayley graph associated with $H$ and analyzing a suitable random walk on this graph by examining the spectral properties of its adjacency matrix. Furthermore, we show […]

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arXiv:2603.05673v1 Announce Type: new Abstract: The power flow equations are non-linear multivariate equations that describe the relationship between power injections and bus voltages of electric power networks. Given a network topology, we are interested in finding network parameters with many equilibrium points. This corresponds to finding instances of the power flow equations with many real solutions. Current state-of-the art algorithms in computational algebra are not capable of answering this question for networks involving more than a small number […]

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arXiv:2602.20875v1 Announce Type: cross Abstract: We introduce a new method for online parameter estimation in stochastic interacting particle systems, based on continuous observation of a small number of particles from the system. Our method recursively updates the model parameters using a stochastic approximation of the gradient of the asymptotic log likelihood, which is computed using the continuous stream of observations. Under suitable assumptions, we rigorously establish convergence of our method to the stationary points of the asymptotic log-likelihood […]

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Scaling test-time compute via parallel sampling can substantially improve LLM reasoning, but is often limited by Best-of-N selection quality. Generative selection methods, such as GenSelect, address this bottleneck, yet strong selection performance remains largely limited to large models. We show that small reasoning models can acquire strong GenSelect capabilities through targeted reinforcement learning. To this end, we synthesize selection tasks from large-scale math and code instruction datasets by filtering to instances with both correct and incorrect candidate solutions, […]

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Instagram exposes activity but hides sequence. Following lists refresh, reorder, and quietly blur timing. People notice movement yet struggle to place it in time, which turns observation into interpretation. That gap matters because timing changes meaning. A follow from yesterday carries a different weight than one from months ago, but the interface rarely helps you tell the difference. This is where tools focused on structure step in. Instead of adding speculation or interpretation, they reorganize what is already […]

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