arXiv:2607.19692v1 Announce Type: new Abstract: Observational causal analyses increasingly pool records across sites, vendors, and collection systems, creating vulnerability to append-only attacks in which plausible records…
arXiv:2607.20119v1 Announce Type: new Abstract: We introduce the Directional Kernel Mean Difference (DKMD), a signed statistic for univariate distribution comparison that preserves the direction of distributional…
arXiv:2607.20084v1 Announce Type: new Abstract: Non--negative matrix factorization (NMF) has become an established dimensionality reduction technique for extracting latent structures from non--negative data and has found…
arXiv:2607.20205v1 Announce Type: new Abstract: Low-rank adaptation (LoRA) has become a widely used parameter-efficient fine-tuning method for large language models. Since different modules and layers may…
arXiv:2406.07746v4 Announce Type: replace Abstract: We propose a computationally efficient algorithm that achieves anytime regret of order $mathcal{O}(sqrt{t})$, with explicit dependence on the system dimensions and…
arXiv:2603.08287v3 Announce Type: replace Abstract: We analyze the Bayesian regret of the Gaussian process posterior sampling reinforcement learning (GP-PSRL) algorithm. Posterior sampling is a heuristic for…
arXiv:2506.06488v3 Announce Type: replace-cross Abstract: A key tool in developing safe AI models is emph{data auditing}, i.e., using statistical tools to determine whether harmful content may…
arXiv:2302.06578v4 Announce Type: replace-cross Abstract: We provide uniform confidence bands for kernel ridge regression (KRR), a widely used nonparametric regression estimator for nonstandard data such as…
arXiv:2601.01699v2 Announce Type: replace-cross Abstract: As cells differentiate, gene-gene associations may change. Because the composition of cell subtypes also shifts with development, it is challenging to…
arXiv:2607.19875v1 Announce Type: new Abstract: Multi-object tracking (MOT) plays a fundamental role in visual perception, where accurate trajectory prediction is essential for reliable target association under…
arXiv:2607.19973v1 Announce Type: cross Abstract: AI researchers describe state-of-the-art models as one thing repeated at scale: the Transformer, wired identically for text, pixels, or speech. Neuroscientists…
arXiv:2602.06595v5 Announce Type: replace Abstract: This paper presents a principled framework for designing energy-aware metaheuristics that operate under fixed energy budgets. We introduce a unified operator-level…
arXiv:2405.12410v2 Announce Type: replace-cross Abstract: Sometimes local search algorithms cannot efficiently find even local peaks. To understand why, I look at the structure of ascents in…
arXiv:2606.17471v3 Announce Type: replace Abstract: Traditional CPU, GPU, and NPU architectures are increasingly limited by the von Neumann bottleneck. While In-Memory Computing (IMC) using ReRAM crossbar…
arXiv:2607.19393v1 Announce Type: new Abstract: While auditing a perturbation-based OOD detector on a document benchmark, we recorded an AUROC of 0.326 -- well below the 0.5…
arXiv:2602.03901v5 Announce Type: replace Abstract: The pursuit of optimal trade-offs in high-dimensional search spaces under stringent computational constraints poses a fundamental challenge for contemporary multi-objective optimization.…
arXiv:2607.19392v1 Announce Type: new Abstract: Arsenic contamination in groundwater presents a longstanding public health crisis in the United States, especially for households depending on private wells.…
arXiv:2507.03853v2 Announce Type: replace Abstract: We introduce OrbitAll, a geometry- and physics-informed deep learning framework that encodes any molecular system with arbitrary charges, spins, and environmental…
arXiv:2607.19391v1 Announce Type: new Abstract: Low-Rank Adaptation is widely used for parameter-efficient fine-tuning, yet existing methods typically assign the same adapter rank to every transformer layer…
arXiv:2607.20239v1 Announce Type: cross Abstract: Bayesian online learning promises uncertainty-aware prediction on data streams, but its performance hinges on inferential choices, including learning rates, prior distributions…
arXiv:2607.19390v1 Announce Type: new Abstract: A recent report finds that orthogonalizing the mLSTM memory matrix at read time (five Newton-Schulz iterations, trained through) substantially improves noisy…
arXiv:2607.19899v1 Announce Type: cross Abstract: Disagreement-triggered escalation can create a structural blind spot in multi-agent arbitration: as base learners improve, they tend to converge, weakening safety…
arXiv:2607.19388v1 Announce Type: new Abstract: This work extends our one-dimensional single-sweep neural-operator studies to two dimensions. We consider one-group transport with isotropic scattering. As in the…
arXiv:2606.20785v2 Announce Type: replace-cross Abstract: Collecting computer use data from human demonstrations is expensive and slow, motivating the need for scalable generation strategies. This requires two…