February 2026

The primary claim I make in Why Authorization is the Hard Problem in Agentic AI is that static authorization models are insufficient for systems that plan, act, and replan over time. In agentic systems, authorization cannot be a one-time gate checked before execution begins. It must be evaluated as part of the agent’s control loop. In this post, I’ll walk through a concrete demo that shows what this looks like in practice. Using OpenClaw and Cedar, we modify the agent […]

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Every day, billions of users scroll through personalized feeds on TikTok, Instagram, and YouTube, each experiencing a uniquely tailored stream of content. Behind this seemingly effortless personalization lies one of the most challenging problems in modern machine learning: how do you build recommendation systems that adapt to user interests in real-time, learning from every interaction within the same scrolling session? The engineering required to make this work, processing billions of events per second, updating models every few hours, […]

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Brazil has recently tokenized R$5.4 5.4billion of real-world assets, more than any other Latin American country. Surprisingly, only three blockchains cover 93 per cent of the latter market. According to the RWA Monitor data published in January 2026, the XDC Network has R$2.68, the XRP Ledger has R$1.7, and Polygon has R$506 million. Why did Brazilian giant institutions—Banco Itaú, VERT Capital, and Mercado Bitcoin—choose these three networks? Each blockchain solved a unique problem. Why Brazil Became Latin America’s […]

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Federated learning on connected electric vehicles (BEVs) faces severe instability due to intermittent connectivity, time-varying client participation, and pronounced client-to-client variation induced by diverse operating conditions. Conventional FedAvg and many advanced methods can suffer from excessive drift and degraded convergence under these realistic constraints. This work introduces Fractional-Order Roughness-Informed Federated Averaging (FO-RI-FedAvg), a lightweight and modular extension of FedAvg that improves stability through two complementary client-side mechanisms: (i) adaptive roughness-informed proximal regularization, which dynamically tunes the pull toward […]

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The literature indicates that the qubit requirements for factoring RSA-2048 remain on the order of 1 million, under commonly assumed architectures and error-correction models, leaving a substantial gap between current resource estimates and near-term practical feasibility. Reducing this requirement to the low thousands qubit regime therefore remains an important open research objective. This work proposes a hybrid classical-quantum algorithm using a classical modular exponentiation subroutine with a Quantum Number Theoretic Transform (QNTT) circuit, to increase the speed and […]

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Table of Links Abstract and 1. Introduction Related Work Preliminaries and Notations Differentiable Structural Information 4.1. A New Formulation 4.2. Properties 4.3. Differentiability & Deep Graph Clustering LSEnet 5.1. Embedding Leaf Nodes 5.2. Learning Parent Nodes 5.3. Hyperbolic Partitioning Tree Experiments 6.1. Graph Clustering 6.2. Discussion on Structural Entropy Conclusion, Broader Impact, and References Appendix A. Proofs B. Hyperbolic Space C. Technical Details D. Additional Results 2. Related Work Deep Graph Clustering. Deep models have achieved state-of-the-art results […]

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This study develops a Constraint-Driven Model of Intelligence to explain the emergence of structured meaning in complex systems, reconciling probability and cybernetics. Building on Émile Borel’s Infinite Monkey Theorem, which illustrates the theoretical inevitability of order through unbounded stochastic processes, and Gregory Bateson’s principle of negative explanation, which frames structure as the consequence of systematically eliminated alternatives, the analysis formalizes how constraints break ergodicity and generate asymmetry. Shannon’s entropy quantifies the informational effects of constraints, while Simon’s bounded […]

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