arXiv:2601.10814v1 Announce Type: new Abstract: Localization and mapping are core perceptual capabilities for underwater robots. Stereo cameras provide a low-cost means of directly estimating metric depth to support these tasks. However, despite recent advances in stereo depth estimation on land, computing depth from image pairs in underwater scenes remains challenging. In underwater environments, images are degraded by light attenuation, visual artifacts, and dynamic lighting conditions. Furthermore, real-world underwater scenes frequently lack rich texture useful for stereo depth estimation […]

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I cooked up a new fast geometric regression algorithm and show that it is a suitable replacement for MLPs. Check out the paper: https://doi.org/10.5281/zenodo.18673034 Whats inside? New research indicates that many representations within LLMs create geometric structures to model language. ( https://arxiv.org/abs/2601.04480 , https://arxiv.org/abs/2510.26745 ) MLPs store geometric representations in highly inefficient ways, so I say it is time to look for new methods that encode regressions directly in geometry. Enter K-Splanifolds, a fast high dimensional spline manifold […]

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arXiv:2602.05029v1 Announce Type: new Abstract: Operating effectively in novel real-world environments requires robotic systems to estimate and interact with previously unseen objects. Current state-of-the-art models address this challenge by using large amounts of training data and test-time samples to build black-box scene representations. In this work, we introduce a differentiable neuro-graphics model that combines neural foundation models with physics-based differentiable rendering to perform zero-shot scene reconstruction and robot grasping without relying on any additional 3D data or test-time […]

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We study uniform computability properties of PAC learning using Weihrauch complexity. We focus on closed concept classes, which are either represented by positive, by negative or by full information. Among other results, we prove that proper PAC learning from positive information is equivalent to the limit operation on Baire space, whereas improper PAC learning from positive information is closely related to Weak Kőnig’s Lemma and even equivalent to it, when we have some negative information about the admissible […]

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arXiv:2602.10228v1 Announce Type: new Abstract: Differential privacy (DP) provides a mathematical guarantee limiting what an adversary can learn about any individual from released data. However, achieving this protection typically requires adding noise, and noise can accumulate when many statistics are measured. Existing DP synthetic data methods treat all features symmetrically, spreading noise uniformly even when the data will serve a specific prediction task. We develop a prediction-centric approach operating in three regimes depending on available structural knowledge. In […]

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arXiv:2602.17774v1 Announce Type: new Abstract: We present a comprehensive characterization study of open-source message-oriented middleware (MOM) systems. We followed a rigorous methodology to select and study ten popular and diverse MOM systems. For each system, we examine 42 features with a total of 134 different options. We found that MOM systems have evolved to provide a framework for modern cloud applications through high flexibility and configurability and by offering core building blocks for complex applications including transaction support, […]

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arXiv:2602.22340v1 Announce Type: new Abstract: Non-Display Smart Glasses hold the potential to support everyday activities by combining continuous environmental sensing with voice-only interaction powered by large language models (LLMs). Understanding how conversational successes and breakdowns arise in everyday contexts can better inform the design of future voice-only interfaces. To investigate this, we conducted a month-long collaborative autoethnography (n=2) to identify patterns of successes and breakdowns when using such devices. We then compare these patterns with prior findings on […]

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arXiv:2601.19922v1 Announce Type: new Abstract: Supportive conversation depends on skills that go beyond language fluency, including reading emotions, adjusting tone, and navigating moments of resistance, frustration, or distress. Despite rapid progress in language models, we still lack a clear way to understand how their abilities in these interpersonal domains compare to those of humans. We introduce HEART, the first-ever framework that directly compares humans and LLMs on the same multi-turn emotional-support conversations. For each dialogue history, we pair […]

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