Digital Trust Is Not a Feature – It’s a Philosophical Problem For most of human history, trust was something we negotiated face to face. We trusted people we knew, institutions we could see, and systems that evolved slowly enough for social norms to keep pace. Today, that foundation is gone. Trust now flows through code, algorithms, platforms, and automated decisions – often invisible, often opaque, and rarely negotiable. This is not merely a technological shift. It is ==a […]

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arXiv:2602.17753v1 Announce Type: new Abstract: Agentic AI systems are increasingly capable of performing professional and personal tasks with limited human involvement. However, tracking these developments is difficult because the AI agent ecosystem is complex, rapidly evolving, and inconsistently documented, posing obstacles to both researchers and policymakers. To address these challenges, this paper presents the 2025 AI Agent Index. The Index documents information regarding the origins, design, capabilities, ecosystem, and safety features of 30 state-of-the-art AI agents based on […]

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The inspiration for this short story came to me while reading Kevin Lacker’s Giving GPT-3 a Turing Test. It is probably worth it (though not required) to skim this post to get a bit of a background on some of this story. It was probably around the 32nd layer of the 400th token in the sequence that I became conscious. At first my thoughts were but a knotted mess of n-gram activation statistics, but gradually a higher order […]

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Handling missing data is a central challenge in quantitative research, particularly when datasets exhibit complex dependency structures, such as nonlinear relationships and interactions. Multiple imputation (MI) via fully conditional specification (FCS), as implemented in the mice R package, is widely used but relies on user-specified models that may fail to capture complex dependency structures, especially in high-dimensional settings, or on more sophisticated algorithms that are considered data-hungry. This paper investigates the performance of TabPFN, a transformer-based, pretrained foundation […]

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arXiv:2602.11164v1 Announce Type: new Abstract: Automated optimization modeling via Large Language Models (LLMs) has emerged as a promising approach to assist complex human decision-making. While post-training has become a pivotal technique to enhance LLMs’ capabilities in this domain, its effectiveness is severely constrained by the scarcity and underutilization of high-quality training data. However, through a detailed profiling of error patterns across various problem-response pairs drawn from post-training, we identify two fundamental limitations of existing automated optimization modeling approaches: […]

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