The most dangerous advice in academia is “just start writing.” It sounds noble. It sounds productive. It is also a lie. Telling a graduate student to “just write” without a structure is like telling a construction crew to “just build” without a blueprint. You might end up with walls, but they won’t hold a roof. You will spend six months pouring concrete only to realize you forgot the plumbing. I see this every year. Brilliant students freeze. They […]

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arXiv:2603.20286v1 Announce Type: new Abstract: Retrieval-Augmented Generation (RAG) enables Large Language Models (LLMs) to extend their existing knowledge by dynamically incorporating external information. However, practical deployment is fundamentally constrained by the LLM’s finite context window, forcing a trade-off between information sufficiency and token consumption. Standard pipelines address this via a retrieve-then-select strategy, typically retaining only the top-k chunks based on relevance. Nevertheless, this approach is suboptimal: it inherently truncates critical bridging evidence located in the long tail of […]

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arXiv:2504.03097v2 Announce Type: replace Abstract: In this paper, we study the problem of multivariate shuffled linear regression, where the correspondence between predictors and responses in a linear model is obfuscated by a latent permutation. Specifically, we investigate the model $Y=tfrac{1}{sqrt{1+sigma^2}}(Pi_* X Q_* + sigma Z)$, where $X$ is an $n*d$ standard Gaussian design matrix, $Z$ is an $n*m$ Gaussian noise matrix, $Pi_*$ is an unknown $n*n$ permutation matrix, and $Q_*$ is an unknown $d*m$ on the Grassmanian manifold […]

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arXiv:2602.15901v1 Announce Type: new Abstract: Autonomous sailboats are well suited for long-duration ocean observation due to their wind-driven endurance. However, their performance is highly anisotropic and strongly influenced by inhomogeneous and time-varying wind and current fields, limiting the effectiveness of existing coverage methods such as boustrophedon sweeping. Planning under these environmental and maneuvering constraints remains underexplored. This paper presents a spatiotemporal coverage path planning framework tailored to autonomous sailboats, combining (1) topology-based morphological constraints in the spatial domain […]

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arXiv:2603.18985v1 Announce Type: new Abstract: Deep learning models have become the dominant approach for multivariate time series anomaly detection (MTSAD), often reporting substantial performance improvements over classical statistical methods. However, these gains are frequently evaluated under heterogeneous thresholding strategies and evaluation protocols, making fair comparisons difficult. This work revisits OmniAnomaly, a widely used stochastic recurrent model for MTSAD, and systematically compares it with a simple linear baseline based on Principal Component Analysis (PCA) on the Server Machine Dataset […]

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arXiv:2602.15832v1 Announce Type: new Abstract: Existing user simulations, where models generate user-like responses in dialogue, often lack verification that sufficient user personas are provided, questioning the validity of the simulations. To address this core concern, this work explores the task of identifying relevant but unknown personas of the simulation target for a given simulation context. We introduce PICQ, a novel dataset of context-aware choice questions, annotated with unknown personas (e.g., ”Is the user price-sensitive?”) that may influence user […]

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arXiv:2509.20993v2 Announce Type: replace-cross Abstract: We consider the problem of estimating inverse temperature parameter $beta$ of an $n$-dimensional truncated Ising model using a single sample. Given a graph $G = (V,E)$ with $n$ vertices, a truncated Ising model is a probability distribution over the $n$-dimensional hypercube ${-1,1}^n$ where each configuration $mathbf{sigma}$ is constrained to lie in a truncation set $S subseteq {-1,1}^n$ and has probability $Pr(mathbf{sigma}) propto exp(betamathbf{sigma}^top Amathbf{sigma})$ with $A$ being the adjacency matrix of $G$. We […]

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arXiv:2602.12534v1 Announce Type: new Abstract: In truncated linear regression, samples $(x,y)$ are shown only when the outcome $y$ falls inside a certain survival set $S^star$ and the goal is to estimate the unknown $d$-dimensional regressor $w^star$. This problem has a long history of study in Statistics and Machine Learning going back to the works of (Galton, 1897; Tobin, 1958) and more recently in, e.g., (Daskalakis et al., 2019; 2021; Lee et al., 2023; 2024). Despite this long history, […]

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In the previous article, I established that the AI art detectors are unreliable. When I started working on AI writing detectors, I expected there to be flawed systems, but what I found was shocking. The first thing I had to do was develop a test for determining the accuracy of a system. Here is the complete methodology I used for testing. Experimental Design: A 5-Variable Gauntlet The testing framework utilized a controlled input sequence designed to map the […]

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