arXiv:2601.17001v1 Announce Type: new Abstract: Twenty-five years ago, Joel Reidenberg argued that technology itself, not just law and regulation, imposes rules on communities in the Information Society. System design choices like network architecture and configurations create regulatory norms he termed “Lex Informatica”-referencing the merchant-driven medieval “Lex Mercatoria” that emerged independent of sovereign control. Today we face different challenges requiring us to revisit Reidenberg’s insights and examine the consequences of that earlier era. While Lex Informatica provided a framework […]

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Federated Learning (FL) enables a distributed client-server architecture where multiple clients collaboratively train a global Machine Learning (ML) model without sharing sensitive local data. However, FL often results in lower accuracy than traditional ML algorithms due to statistical heterogeneity across clients. Prior works attempt to address this by using model updates, such as loss and bias, from client models to select participants that can improve the global model’s accuracy. However, these updates neither accurately represent a client’s heterogeneity […]

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arXiv:2603.01047v1 Announce Type: cross Abstract: Generative Flow Networks (GFlowNets) were developed to learn policies for efficiently sampling combinatorial candidates by interpreting their generative processes as trajectories in directed acyclic graphs. In the value-based training workflow, the objective is to enforce the balance over partial episodes between the flows of the learned policy and the estimated flows of the desired policy, implicitly encouraging policy divergence minimization. The policy-based strategy alternates between estimating the policy divergence and updating the policy, […]

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Hey folks, I’m building a dynamic pricing engine for a multi-store app. We deal with massive seasonality swings (huge peak seasons (spring/fall and on weekends), nearly dead low seasons (winter/summer and at the start of the week) alongside steady YoY growth. We’re using thompson sampling to optimize price ladders for item “clusters” (e.g., all 12oz Celsius cans) within broader categories (e.g., energy drinks). To account for cannibalization, we currently use the total gross profit of the entire category […]

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arXiv:2602.09185v1 Announce Type: new Abstract: AI coding agents are rapidly transforming software engineering by performing tasks such as feature development, debugging, and testing. Despite their growing impact, the research community lacks a comprehensive dataset capturing how these agents are used in real-world projects. To address this gap, we introduce AIDev, a large-scale dataset focused on agent-authored pull requests (Agentic-PRs) in real-world GitHub repositories. AIDev aggregates 932,791 Agentic-PRs produced by five agents: OpenAI Codex, Devin, GitHub Copilot, Cursor, and […]

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Note: This is a short edition. Based on the full paper published December 28, 2025: The Augmented Self: AI Scaffolds, Offloading, and the Drift Toward Dependency A subtle change is underway in how knowledge work begins. More and more, the first coherent version of a thought arrives already shaped—quickly, fluently, and with plausible next steps attached. This can feel like simple convenience. But when the starting point changes, the rest of the workflow changes with it: what gets practiced, what […]

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Introduction When writers think about LaTeX, they think about academic papers. Dense research documents filled with equations, citations, and footnotes. They don’t think about fantasy novels, thriller manuscripts, or literary fiction. That’s a missed opportunity. LaTeX—the typesetting system beloved by mathematicians and scientists—offers creative writers something word processors can’t match: programmable consistency. The same features that let physicists define custom notation for quantum equations let novelists define custom styling for character dialogue, narrative voices, and recurring textual elements. […]

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The rapid evolution of Large Language Models (LLMs) from static text generators to autonomous agents has revolutionized their ability to perceive, reason, and act within complex environments. However, this transition from single-model inference to System Engineering Security introduces unique structural vulnerabilities—specifically instruction-data conflation, persistent cognitive states, and untrusted coordination—that extend beyond traditional adversarial robustness. To address the fragmented nature of the existing literature, this article presents a comprehensive and systematic survey of the security landscape for LLM-based agents. […]

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