May 2026

My name is Emma and I’m six and a half years old and I like pink and Pokemon and my cat River and I’m going to be swallowed by a hedonium shockwave soon, except you already know that about me because everyone else is too. “Hedonium shockwave” means that everyone is going to be happy forever. Not just all the humans but all the animals and the flowers and the ground and River too. It has already made […]

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This is not a prediction. It is an intellectual exercise. Maybe it is wrong. Maybe it is too early. Maybe it can be discarded as a strange idea. But I think the question is worth asking: if AI-generated text becomes the dominant training substrate, where will the entropy needed for future AI evolution come from? This article continues a line of thought I explored in my previous piece, “More Memory Won’t Fix Your AI Agents”. There, I argued […]

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arXiv:2605.06976v1 Announce Type: new Abstract: Many ranking and agent trace datasets are recorded as linear orders even though their latent structure is only partially ordered. This is especially common in agent and workflow traces, where observed order may reflect arbitrary linearization rather than true prerequisites. We introduce a differentiable relaxation for latent partial-order inference from such traces. Starting from a hard frontier-constrained model of noisy linear extensions, we replace discontinuous product-order precedence and binary frontier feasibility with smooth […]

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arXiv:2605.06959v1 Announce Type: new Abstract: This paper presents a parametric solution to piecewise linear regression through the Adaptive Block Gradient Descent (ABGD) algorithm. The heart of the method is the parametrization of piecewise linear functions as the difference of max-affine (DoMA) functions. A non-asymptotic local convergence analysis for ABGD is provided under sub-Gaussian covariate and noise distributions. To initialize ABGD, we adapt a prior algorithm originally developed for the simpler setting of max-affine functions. When suitably initialized, ABGD […]

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arXiv:2605.06883v1 Announce Type: new Abstract: The Maximum Mean Discrepancy (MMD) is a cornerstone statistic for nonparametric two-sample testing, but its test power is dictated entirely by the chosen kernel. Because any fixed kernel inherently fails to distinguish certain distributions, the kernel must be dynamically optimized. However, data-driven optimization violates the foundational i.i.d. assumption, forcing a strict trade-off in existing frameworks. Ratio criteria ignore this dependence, inducing overfitting and variance collapse on rich kernel classes. Conversely, aggregation methods bypass […]

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arXiv:2605.06873v1 Announce Type: new Abstract: Probabilistic conditioning is concerned with the identification of a distribution of a random variable $X$ given a random variable $Y$. It is a cornerstone of scientific and engineering applications where modeling uncertainty is key. This problem has traditionally been addressed in machine learning by directly learning the conditional distribution of a fixed joint distribution. This paper introduces a novel perspective: we propose to solve the conditioning problem by identifying a single operator that […]

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arXiv:2605.06826v1 Announce Type: new Abstract: We study the spectral properties of sample covariance matrices constructed from pooled sequence representations, where token embeddings are drawn from a fixed two-class Gaussian mixture table and pooled via (fixed) attention weights. Working in the high-dimensional regime $d,V,Ntoinfty$ with $d/Vtodelta$ and $d/Ntogamma$, we derive exact characterizations of the limiting eigenvalue distribution, outlier eigenvalues, and eigenvector alignment with the hidden signal. The bulk spectrum follows a non-Marchenko–Pastur law given by the free multiplicative convolution […]

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I generally work around 9–10 hours a day, but not contiguously. I can usually carve out a dedicated chunk of time in the morning, take lab or project meetings in the afternoon, and block out around 6–8 PM for commute, exercise, socializing, and dinner. I also get more work done in the evening, since my focus is often best then. On weekends, I mostly run errands and try out new food spots, but I also make sure to […]

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NVIDIA AI researchers recently released cuda-oxide, an experimental compiler that allows developers to write CUDA SIMT (Single Instruction, Multiple Threads) GPU kernels in standard Rust code. The project compiles Rust directly to PTX (Parallel Thread Execution) — the assembly-like intermediate representation that CUDA uses to target NVIDIA GPUs — without requiring domain-specific languages, foreign function interface bindings, or C/C++ code. How This Makes a Change Writing GPU kernels today typically means writing C++ and using the CUDA programming […]

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In this tutorial, we explore NadirClaw as an intelligent routing layer that classifies prompts into simple and complex tiers before sending them to the most suitable model. We start by installing the required packages, setting up an optional Gemini API key, and testing the local classifier through the NadirClaw CLI without making any live LLM calls. We then inspect the centroid vectors that power the routing decision, embed our own prompts, visualize how similarity scores separate simple and […]

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