arXiv:2601.16994v1 Announce Type: new Abstract: This data paper describes and publicly releases this dataset (v1.0.0), published on Zenodo under DOI 10.5281/zenodo.18189192. Motivated by the need to increase the temporal granularity of originally monthly data to enable more effective training of AI models for epidemiological forecasting, the dataset harmonizes municipal-level dengue hospitalization time series across Brazil and disaggregates them to weekly resolution (epidemiological weeks) through an interpolation protocol with a correction step that preserves monthly totals. The statistical and […]

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arXiv:2601.20985v1 Announce Type: new Abstract: Optimal control of complex environments with robotic systems faces two complementary and intertwined challenges: efficient organization of sensory state information and far-sighted action planning. Because the reinforcement learning framework addresses only the latter, it tends to deliver sample-inefficient solutions. Active inference is the state-of-the-art process theory that explains how biological brains handle this dual problem. However, its applications to artificial intelligence have thus far been limited to extensions of existing model-based approaches. We […]

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arXiv:2602.06197v1 Announce Type: new Abstract: Product designers often begin their design process with handcrafted personas. While personas are intended to ground design decisions in consumer preferences, they often fall short in practice by remaining abstract, expensive to produce, and difficult to translate into actionable design features. As a result, personas risk serving as static reference points rather than tools that actively shape design outcomes. To address these challenges, we built Personagram, an interactive system powered by multimodal large […]

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arXiv:2510.16974v3 Announce Type: replace-cross Abstract: In the social sciences, small- to medium-scale datasets are common, and linear regression is canonical. In privacy-aware settings, much work has focused on differentially private (DP) linear regression, but mostly on point estimation with limited attention to uncertainty quantification. Meanwhile, synthetic data generation (SDG) is increasingly important for reproducibility studies, yet current DP linear regression methods do not readily support it. Mainstream DP-SDG approaches either are tailored to discrete or discretized data, making […]

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The Most Popular But Flawed OpenClaw Gets a High Security Replacement in OpenFang On November 25th, 2025, a developer named Peter Steinberger pushed an open-source project called Clawdbot to GitHub. By mid-March, it had spawned over twenty alternatives, triggered a Mac mini shortage in several U.S. cities, and earned its current name — OpenClaw — after two rapid rebrands driven by trademark disputes. That was not a product launch. :::info That was a detonation. ::: And yet, within […]

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arXiv:2601.03396v1 Announce Type: new Abstract: Procedural content generation has enabled vast virtual worlds through levels, maps, and quests, but large-scale character generation remains underexplored. We identify two alignment-induced biases in existing methods: a positive moral bias, where characters uniformly adopt agreeable stances (e.g. always saying lying is bad), and a helpful assistant bias, where characters invariably answer questions directly (e.g. never refusing or deflecting). While such tendencies suit instruction-following systems, they suppress dramatic tension and yield predictable characters, […]

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Learning data-efficient object dynamics models for robotic manipulation remains challenging, especially for deformable objects. A popular approach is to model objects as sets of 3D particles and learn their motion using graph neural networks. In practice, this is not enough to maintain physical feasibility over long horizons and may require large amounts of interaction data to learn. We introduce PIEGraph, a novel approach to combining analytical physics and data-driven models to capture object dynamics for both rigid and […]

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Im super late to the party but I had completely forgotten about RL environments for agents since I first explored it due to mechanize.work. Now I see that meta has also been quite active in that area. I’m a mere layman in RL so could someone tell me how big of a deal this actually is and if it is an unsolved problem or just an implementation/business problem? submitted by /u/lokeye-ai [link] [comments]

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arXiv:2601.12120v1 Announce Type: cross Abstract: Instrumental variable based estimation of a causal effect has emerged as a standard approach to mitigate confounding bias in the social sciences and epidemiology, where conducting randomized experiments can be too costly or impossible. However, justifying the validity of the instrument often poses a significant challenge. In this work, we highlight a problem generally neglected in arguments for instrumental variable validity: the presence of an ”aggregate treatment variable”, where the treatment (e.g., education, […]

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