We’re working on a system called Gennie that sits at an interesting intersection of reinforcement learning, human-in-the-loop systems, and noisy real-world environments. The core problem we’re exploring is this: In real-world settings, users issue short, ambiguous, and sometimes incorrect commands (often via voice) under time pressure. The system must decide when to act, when to request confirmation, and when to do nothing, balancing speed and accuracy. The reward signal isn’t immediate and is often delayed or implicit (task […]

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arXiv:2601.14477v1 Announce Type: new Abstract: Until open-world foundation models match the performance of specialized approaches, the effectiveness of deep learning models remains heavily dependent on dataset availability. Training data must align not only with the target object categories but also with the sensor characteristics and modalities. To bridge the gap between available datasets and deployment domains, domain adaptation strategies are widely used. In this work, we propose a novel approach to transferring sensor-specific knowledge from an image dataset […]

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-decently fast on CPU -can run in the cloud -pythin -for marl competitive to train for playing Splendour -not a premade splendour env as I want to learn submitted by /u/Live-Mixture6353 [link] [comments]

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arXiv:2510.25502v4 Announce Type: replace-cross Abstract: Foundation models for zero-shot time series forecasting face challenges in efficient long-horizon prediction and reproducibility, with existing synthetic-only approaches underperforming on challenging benchmarks. This paper presents TempoPFN, a univariate time series foundation model based on linear Recurrent Neural Networks (RNNs) pre-trained exclusively on synthetic data. The model uses a GatedDeltaProduct architecture with state-weaving for fully parallelizable training across sequence lengths, eliminating the need for windowing or summarization techniques while maintaining robust temporal state-tracking. […]

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As engineers, we’re taught to design for reliability. We do design calculations, run simulations, build and test prototypes, and even then we recognize that these are imperfect, so we include safety factors. When it comes to the Industrial Internet of Things (IIoT) though, we rarely give the same level of scrutiny to the components that we rely on. What if we treated our IIoT database the same way we treated the physical things we produce? We build and […]

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arXiv:2602.08980v1 Announce Type: cross Abstract: Neural ordinary differential equations (neural ODEs) can effectively learn dynamical systems from time series data, but their behavior on graph-structured data remains poorly understood, especially when applied to graphs with different size or structure than encountered during training. We study neural ODEs ($mathtt{nODE}$s) with vector fields following the Barab’asi-Barzel form, trained on synthetic data from five common dynamical systems on graphs. Using the $mathbb{S}^1$-model to generate graphs with realistic and tunable structure, we […]

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arXiv:2601.03325v1 Announce Type: new Abstract: Identifiability is central to the interpretability of deep latent variable models, ensuring parameterisations are uniquely determined by the data-generating distribution. However, it remains underexplored for deep regime-switching time series. We develop a general theoretical framework for multi-lag Regime-Switching Models (RSMs), encompassing Markov Switching Models (MSMs) and Switching Dynamical Systems (SDSs). For MSMs, we formulate the model as a temporally structured finite mixture and prove identifiability of both the number of regimes and the […]

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When Netflix launched its ad-supported tier in November 2022, most people shrugged it off as a pricing accommodation for budget-conscious subscribers. It was a bigger deal than it looked. The AVOD vs SVOD debate had been a niche industry conversation up to that point. Netflix, a company that spent a decade telling advertisers they had no place in its product, had just admitted its model needed a second revenue engine. Every major platform followed, and the structural logic […]

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arXiv:2604.18630v1 Announce Type: new Abstract: The choice of visualisation in empirical performance analysis is not a neutral presentation decision but an analytical one: different graphical forms reveal different features of the same dataset, and reliance on any single type systematically conceals what the others expose. This paper presents and argues for a suite of five complementary visualisation tools; tempographs, histograms with spline-smoothed probability density functions, ridgeline plots, stacked bar charts, and combination charts. These are applied to bar-level […]

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arXiv:2601.15385v1 Announce Type: new Abstract: Natural-language-to-visualization (NL2VIS) systems based on large language models (LLMs) have substantially improved the accessibility of data visualization. However, their further adoption is hindered by two coupled challenges: (i) the absence of standardized evaluation metrics makes it difficult to assess progress in the field and compare different approaches; and (ii) natural language descriptions are inherently underspecified, so multiple visualizations may be valid for the same query. To address these issues, we introduce VegaChat, a […]

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