February 2026

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 4.1. A New Formulation To bridge this gap, we present a new […]

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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 4. Differentiable Structural Information In this section, we first establish a new […]

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Deep dive into a product that had the world in its clutches just a week ago, and now that the noise has died out a bit, lets actually explore if it is worth your while – regardless of who you are. A bit of history and setting the stage So far, it seems that the tech world – specifically the field of AI – is making insane progress in whatever it touches. But if you zoom out, most […]

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Most founders have a version of this story. Personally, I’ve watched this happen (and made this mistake myself): the tool looks great in a demo, then headcount grows and reality hits. You pick a modern, impressive-looking tool. The demo is smooth. The UI feels fast. Everyone’s excited. For a while, it works. Then the company grows. You hire more people. Someone leaves. Finance asks for historical data. An auditor wants proof of approvals. A manager needs limited access. […]

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The average human thumb scrolls through 300 feet of content every single day. That is the height of the Statue of Liberty. In this endless vertical freefall, your advertisement is not competing with your business rivals. It is fighting a losing war against dopamine. You have approximately three seconds—less time than it takes to read this sentence—to arrest that motion, hijack attention, and earn a click. Most ad copy fails the “3-Second Test” not because the product is […]

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In February 2026, I took part in a collective investigation based exclusively on open-source intelligence (OSINT) to contextualize vague references contained in public court records released by the United States Department of Justice (DOJ) in connection with the Epstein case. This release — one of the largest ever related to the convicted financier Jeffrey Epstein — made millions of pages publicly available starting in January 2026, under the so-called Epstein Files Transparency Act. What began as collaborative analysis […]

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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 3. Preliminaries and Notations Herein, different from the typical setting of existing […]

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arXiv:2602.08907v2 Announce Type: replace-cross Abstract: We study a setting where the goal is to learn a target function f(x) with respect to a target distribution D(x), but training is done on i.i.d. samples from a different training distribution D'(x), labeled by the true target f(x). Such a distribution shift (here in the form of covariate shift) is usually viewed negatively, as hurting or making learning harder, and the traditional distribution shift literature is mostly concerned with limiting or […]

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arXiv:2602.07488v2 Announce Type: replace-cross Abstract: Despite the fact that experimental neural scaling laws have substantially guided empirical progress in large-scale machine learning, no existing theory can quantitatively predict the exponents of these important laws for any modern LLM trained on any natural language dataset. We provide the first such theory in the case of data-limited scaling laws. We isolate two key statistical properties of language that alone can predict neural scaling exponents: (i) the decay of pairwise token […]

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