arXiv:2603.02607v1 Announce Type: new Abstract: Sparse PCA is one of the most well-studied problems in high-dimensional statistics. In this problem, we are given samples from a distribution with covariance $Sigma$, whose top eigenvector $v in R^d$ is $s$-sparse. Existing sparse PCA algorithms can be broadly categorized into (1) combinatorial algorithms (e.g., diagonal or elementwise covariance thresholding) and (2) SDP-based algorithms. While combinatorial algorithms are much simpler, they are typically only analyzed under the spiked identity model (where $Sigma […]

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arXiv:2602.12350v1 Announce Type: new Abstract: Many natural optimization problems derived from $sf NP$ admit bilevel and multilevel extensions in which decisions are made sequentially by multiple players with conflicting objectives, as in interdiction, adversarial selection, and adjustable robust optimization. Such problems are naturally modeled by alternating quantifiers and, therefore, lie beyond $sf NP$, typically in the polynomial hierarchy or $sf PSPACE$. Despite extensive study of these problem classes, relatively few natural completeness results are known at these higher […]

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arXiv:2602.20163v1 Announce Type: new Abstract: Aphasia is an acquired language disorder caused by injury to the regions of the brain that are responsible for language. Aphasia may impair the use and comprehension of written and spoken language. The Western Aphasia Battery-Revised (WAB-R) is an assessment tool administered by speech-language pathologists (SLPs) to evaluate the aphasia type and severity. Because the WAB-R measures isolated linguistic skills, there has been growing interest in the assessment of discourse production as a […]

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arXiv:2601.12147v1 Announce Type: new Abstract: Segment Anything (SAM) has recently pushed the boundaries of segmentation by demonstrating zero-shot generalization and flexible prompting after training on over one billion masks. Despite this, its mask prediction accuracy often falls short of the precision required in real-world applications. While several refinement modules have been proposed to boost SAM’s segmentation quality, achieving highly accurate object delineation within a single, unified framework remains an open challenge. Furthermore, interactive image matting, which aims to […]

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arXiv:2601.14508v1 Announce Type: new Abstract: Super-time-stepping (STS) methods provide an attractive approach for enabling explicit time integration of parabolic operators, particularly in large-scale, higher-dimensional kinetic simulations where fully implicit schemes are impractical. In this work, we present an explicit STS framework tailored for diffusion operators in gyrokinetic models, motivated by the fact that constructing and storing a Jacobian is often infeasible due to strong nonlocal couplings, high dimensionality, and memory constraints. We investigate the performance of several STS […]

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arXiv:2603.15723v1 Announce Type: new Abstract: Large language models are increasingly deployed in settings where relevant information is embedded within long and noisy contexts. Despite this, robustness to growing context length remains poorly understood across different question answering tasks. In this work, we present a controlled empirical study of context-length robustness in large language models using two widely used benchmarks: SQuAD and HotpotQA. We evaluate model accuracy as a function of total context length by systematically increasing the amount […]

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arXiv:2603.22346v1 Announce Type: new Abstract: We isolate and empirically characterize first-mover bias — a path-dependent concentration of feature importance caused by sequential residual fitting in gradient boosting — as a specific mechanistic cause of the well-known instability of SHAP-based feature rankings under multicollinearity. When correlated features compete for early splits, gradient boosting creates a self-reinforcing advantage for whichever feature is selected first: subsequent trees inherit modified residuals that favor the incumbent, concentrating SHAP importance on an arbitrary feature […]

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The Problem Nobody Names Six months into the medical CRM calendar project, I opened a PR and spent twenty minutes just figuring out which slice owned the appointment state. The code wasn’t broken. It wasn’t even obviously wrong. It had just quietly become something else. We kept calling it “technical debt” — but that wasn’t quite right either. Technical debt implies a conscious trade-off: ship now, fix later. What we had was different. Nobody made a bad call. Every decision […]

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