Michael Levin argues that life is not built merely from genes and chemistry, but from networks of bioelectric intelligence. Cells remember goals, coordinate across scales, and solve problems like minds. By rewriting these electrical “pattern memories,” medicine could heal cancer, regrow organs, and unlock a future where biology is programmed by meaning, not molecules.

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arXiv:2601.07325v1 Announce Type: new Abstract: The $rho$-posterior framework provides universal Bayesian estimation with explicit contamination rates and optimal convergence guarantees, but has remained computationally difficult due to an optimization over reference distributions that precludes intractable posterior computation. We develop a PAC-Bayesian framework that recovers these theoretical guarantees through temperature-dependent Gibbs posteriors, deriving finite-sample oracle inequalities with explicit rates and introducing tractable variational approximations that inherit the robustness properties of exact $rho$-posteriors. Numerical experiments demonstrate that this approach achieves […]

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We treat AI models like data assets. We version them, we store them in S3, and we cache them. But technically, a PyTorch model (.pt) or a Pickle file (.pkl) is not data. It is a program. And right now, MLOps pipelines are blindly executing these programs with full privileges. I built Veritensor, an open-source security scanner, to solve this. Here is a deep dive into why simple scanning fails and how we implemented a proper defense using Abstract Interpretation. […]

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arXiv:2601.09712v1 Announce Type: new Abstract: Behavioral targeting, or online profiling, is a hotly debated topic. Much of the collection of personal information on the Internet is related to behavioral targeting, although research suggests that most people don’t want to receive behaviorally targeted advertising. The World Wide Web Consortium is discussing a Do Not Track standard, and regulators worldwide are struggling to come up with answers. This article discusses European law and recent policy developments on behavioral targeting.

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arXiv:2601.03309v1 Announce Type: new Abstract: Vision-Language-Action (VLA) models, which integrate pretrained large Vision-Language Models (VLM) into their policy backbone, are gaining significant attention for their promising generalization capabilities. This paper revisits a fundamental yet seldom systematically studied question: how VLM choice and competence translate to downstream VLA policies performance? We introduce VLM4VLA, a minimal adaptation pipeline that converts general-purpose VLMs into VLA policies using only a small set of new learnable parameters for fair and efficient comparison. Despite […]

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arXiv:2601.05376v1 Announce Type: new Abstract: Persona conditioning can be viewed as a behavioral prior for large language models (LLMs) and is often assumed to confer expertise and improve safety in a monotonic manner. However, its effects on high-stakes clinical decision-making remain poorly characterized. We systematically evaluate persona-based control in clinical LLMs, examining how professional roles (e.g., Emergency Department physician, nurse) and interaction styles (bold vs. cautious) influence behavior across models and medical tasks. We assess performance on clinical […]

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As global commerce becomes increasingly decentralized, the ability to communicate clearly across borders and languages has become a prerequisite. But cross-border communication often suffers from language friction, slow replies, unnatural phrasing, and inappropriate tone, all of which can make teams look unprofessional and reduce trust. TranGPT solves exactly this problem. TranGPT exists to remove language frictions and make multilingual communication feel natural, fast, and professional. This social translation app is designed to help professionals communicate effectively and efficiently […]

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