arXiv:2601.21021v1 Announce Type: new Abstract: Surrogate modeling for complex physical systems typically faces a trade-off between data-fitting accuracy and physical consistency. Physics-consistent approaches typically treat physical laws as soft constraints within the loss function, a strategy that frequently fails to guarantee strict adherence to the governing equations, or rely on post-processing corrections that do not intrinsically learn the underlying solution geometry. To address these limitations, we introduce the {Conditional Denoising Model (CDM)}, a generative model designed to learn […]

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arXiv:2603.06672v1 Announce Type: new Abstract: Semantic noise initialization has been reported to improve robustness and controllability in image diffusion models. Whether these gains transfer to text-to-video (T2V) generation remains unclear, since temporal coupling can introduce extra degrees of freedom and instability. We benchmark semantic noise initialization against standard Gaussian noise using a frozen VideoCrafter-style T2V diffusion backbone and VBench on 100 prompts. Using prompt-level paired tests with bootstrap confidence intervals and a sign-flip permutation test, we observe a […]

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arXiv:2601.04288v1 Announce Type: new Abstract: We present a rigorous, human-in-the-loop evaluation framework for assessing the performance of AI agents on the task of Air Traffic Control, grounded in a regulator-certified simulator-based curriculum used for training and testing real-world trainee controllers. By leveraging legally regulated assessments and involving expert human instructors in the evaluation process, our framework enables a more authentic and domain-accurate measurement of AI performance. This work addresses a critical gap in the existing literature: the frequent […]

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arXiv:2509.15197v2 Announce Type: replace-cross Abstract: We consider the problem of recovering the true causal structure among a set of variables, generated by a linear acyclic structural equation model (SEM) with the error terms being independent, not necessarily Gaussian, and having equal variances. It is well-known that the true underlying directed acyclic graph (DAG) encoding the causal structure is uniquely identifiable under this assumption. Interestingly, in this setting, it further holds that the sum of minimum expected squared errors […]

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Einstein argues that religion evolved from primitive fear to moral guidance, but its highest form is “cosmic religious feeling”—an awe at the universe’s sublime order, free from anthropomorphic gods or dogma. This same feeling, he contends, drives genuine scientific inquiry. Rather than opponents, science and religion share a common wellspring: wonder at existence itself.

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The OpenAI Foundation announces plans to invest at least $1 billion in curing diseases, economic opportunity, AI resilience, and community programs.

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arXiv:2602.20191v1 Announce Type: new Abstract: Changing runtime complexity on cloud and edge devices necessitates elastic large language model (LLM) deployment, where an LLM can be inferred with various quantization precisions based on available computational resources. However, it has been observed that the calibration parameters for quantization are typically linked to specific precisions, which presents challenges during elastic-precision calibration and precision switching at runtime. In this work, we attribute the source of varying calibration parameters to the varying token-level […]

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If your PostgreSQL tables are growing into the hundreds of millions of rows and queries are getting sluggish despite good indexes, partitioning might be exactly what you need. This guide covers the fundamentals and walks you through hands-on examples to get you started. What Is Partitioning? Table partitioning is a technique where a single logical table is split into multiple physical sub-tables called partitions. From the application’s perspective, you still query one table. Under the hood, PostgreSQL routes […]

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arXiv:2603.16958v1 Announce Type: new Abstract: Vision-Language Models (VLMs) are increasingly applied to robotic perception and manipulation, yet their ability to infer physical properties required for manipulation remains limited. In particular, estimating the mass of real-world objects is essential for determining appropriate grasp force and ensuring safe interaction. However, current VLMs lack reliable mass reasoning capabilities, and most existing benchmarks do not explicitly evaluate physical quantity estimation under realistic sensing conditions. In this work, we propose PhysQuantAgent, a framework […]

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