technocracy

arXiv:2511.09216v2 Announce Type: replace-cross Abstract: Proteins underpin most biological function, and the ability to design them with tailored structures and properties is central to advances in biotechnology. Diffusion-based generative models have emerged as powerful tools for protein design, but steering them toward proteins with specified properties remains challenging. The Feynman-Kac (FK) framework provides a principled way to guide diffusion models using user-defined rewards. In this paper, we enable FK-based steering of RFdiffusion through the development of guiding potentials […]

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arXiv:2602.23377v1 Announce Type: new Abstract: Current research on visual analytics systems largely follows the research paradigm of interactive system design in the field of Human-Computer Interaction (HCI), and includes key methodologies including design requirement development based on user needs, interactive system design, and system evaluation. However, most studies under this paradigm have a contradiction: there is a significant mismatch between the research methods developed for simple cognitive behaviors (e.g., color perception, the perception of spatial relationship among interactive […]

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We live in what are described as post-capitalist times, where the economic system that promotes the virtues of creating individual wealth has been variously described as broken, defunct, and even failed. It has, according to many, morphed into a system where the oligarchs control the resources required to make immense wealth and leave the rest to fend for themselves and fight over scraps. Some go to the extent of romanticizing the concept of a welfare state, where basic […]

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arXiv:2603.28887v1 Announce Type: new Abstract: Data-driven autonomous driving simulation has long been constrained by its heavy reliance on pre-recorded driving logs or spatial priors, such as HD maps. This fundamental dependency severely limits scalability, restricting open-ended generation capabilities to the finite scale of existing collected datasets. To break this bottleneck, we present OccSim, the first occupancy world model-driven 3D simulator. OccSim obviates the requirement for continuous logs or HD maps; conditioned only on a single initial frame and […]

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The effectiveness of contrastive learning methods has been widely recognized in the field of graph learning, especially in contexts where graph data often lack labels or are difficult to label. However, the application of these methods to node classification tasks still faces a number of challenges. First, existing data enhancement techniques may lead to significant differences from the original view when generating new views, which may weaken the relevance of the view and affect the efficiency of model […]

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In this tutorial, we explore Equinox, a lightweight and elegant neural network library built on JAX, and show how to use it. We begin by understanding how eqx.Module treats models as PyTrees, which makes parameter handling, transformation, and serialization feel simple and explicit. As we move forward, we work through static fields, filtered transformations such as filter_jit and filter_grad, PyTree manipulation utilities, stateful layers such as BatchNorm, and a complete end-to-end training workflow for a toy regression problem. […]

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arXiv:2602.06229v1 Announce Type: new Abstract: The growth of machine learning demands interpretable models for critical applications, yet most high-performing models are “black-box” systems that obscure input-output relationships, while traditional rule-based algorithms like RuleFit suffer from a lack of predictive power and instability despite their simplicity. This motivated our development of Sparse Relaxed Regularized Regression Rule-Fit (SR4-Fit), a novel interpretable classification algorithm that addresses these limitations while maintaining superior classification performance. Using demographic characteristics of U.S. congressional districts from […]

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arXiv:2604.06187v1 Announce Type: new Abstract: Artificial intelligence is increasingly used in hiring, raising concerns about how applicants perceive these systems. While prior work on algorithmic fairness has emphasized technical bias mitigation, little is known about how avatar identity cues influence applicants’ justice attributions in an interview context. We conducted a crowdsourcing study with 215 participants who completed an interview with photorealistic AI avatars varied in phenotypic traits (race and sex), followed by a standardized rejection. Using self-reports, sentiment […]

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