Prefill Is the Tax You Keep Paying Twice
Your inference stack is burning money on solved problems. A storage hierarchy would fix the part you’re paying for twice.
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Dynamics of Corruption as a Social Pathogen: A Two-Patch Resource-Competition Model with Endogenous Enforcement and Migration
This paper formulates and analyzes a novel compartmental model to study the spatial dynamics of corruption, framed as a pathogenic social strategy within a biological resource-competition framework. The model incorporates a renewable resource, whose scarcity drives the transmission of a corrupt strategy among a population of cooperators. The population is stratified into Cooperators (S), Corruptors (C), and Immunes/Enforcers (I), interacting within and between two connected patches via migration. The model exhibits a resource-dependent transmission rate and predator-prey dynamics […]
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Rootless Rituals Die Fast in the Age of Vibe Coding
15% of 2026 is gone. here’s what held.
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Photorealistic Images, Token by Token: Inside fal-ai’s Bitdance
Bitdance is a fast, high-res photorealistic image model from fal-ai that generates images token by token using an autoregressive approach.
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Your Terminal Knows When to Pray: A Ramadan Gift for Muslim Developers
How I added Islamic prayer times directly into my Claude Code status line Continue reading on Towards AI »
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Using a Knowledge Graph to Generate Predictive Models for the Oscars
Generated by ChatGPT A layered semantic data foundation for agent-driven forecasting Git Repo: https://github.com/SteveHedden/fckg The full ontology, data pipelines, and modeling code are available in the repository. Using AI to predict the Oscars is easy. Building the infrastructure that lets anyone (human or agent) produce forecasts is harder. This post is not about a single model that forecasts winners. It’s about constructing a reusable semantic data foundation that makes prediction, analysis, and reasoning straightforward. The Oscars are simply a case […]
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I Spent Months Tuning Multi-Agent Systems in Production. Most of the Advice Out There Is Wrong.
Everyone’s debating architecture. Nobody’s talking about what actually matters once you ship it. I need to get something off my chest. I’ve been building a multi-agent orchestration platform — processing real insurance workflows, real claims, real policy renewals — and every week I see another article explaining the difference between single-agent and multi-agent systems. Another tutorial on setting up your first LangChain orchestrator. Another hot take about whether agents are “ready for production.” That ship has sailed. At least for me. The questions I’m dealing […]
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Demystifying Group Relative Policy Optimization: Its Policy Gradient is a U-Statistic
arXiv:2603.01162v2 Announce Type: replace-cross Abstract: Group relative policy optimization (GRPO), a core methodological component of DeepSeekMath and DeepSeek-R1, has emerged as a cornerstone for scaling reasoning capabilities of large language models. Despite its widespread adoption and the proliferation of follow-up works, the theoretical properties of GRPO remain less studied. This paper provides a unified framework to understand GRPO through the lens of classical U-statistics. We demonstrate that the GRPO policy gradient is inherently a U-statistic, allowing us to […]
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Non-Rectangular Average-Reward Robust MDPs: Optimal Policies and Their Transient Values
arXiv:2603.00945v2 Announce Type: replace-cross Abstract: We study non-rectangular robust Markov decision processes under the average-reward criterion, where the ambiguity set couples transition probabilities across states and the adversary commits to a stationary kernel for the entire horizon. We show that any history-dependent policy achieving sublinear expected regret uniformly over the ambiguity set is robust-optimal, and that the robust value admits a minimax representation as the infimum over the ambiguity set of the classical optimal gains, without requiring any […]
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The Implicit Bias of Adam and Muon on Smooth Homogeneous Neural Networks
arXiv:2602.16340v2 Announce Type: replace-cross Abstract: We study the implicit bias of momentum-based optimizers on homogeneous models. We first extend existing results on the implicit bias of steepest descent in homogeneous models to normalized steepest descent with an optional learning rate schedule. We then show that for smooth homogeneous models, momentum steepest descent algorithms like Muon (spectral norm), MomentumGD ($ell_2$ norm), and Signum ($ell_infty$ norm) are approximate steepest descent trajectories under a decaying learning rate schedule, proving that these […]
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