Every story across every category, newest first. Each card links to the original publisher; daily-brief posts open as editorial pages.
arXiv:2608.13418v1 Announce Type: new Abstract: Given a dataset where a portion of the samples are contaminated, our goal is to recover the underlying clean population distribution.…
arXiv:2608.13201v1 Announce Type: new Abstract: We develop the statistical and algorithmic theory of inverse optimal transport (IOT) under the feature-parameterized cost C_theta(i,j) = -theta^T phi(i,j). The…
arXiv:2604.24196v4 Announce Type: replace Abstract: A drifting model is a one-step generator trained by moving each sample along a field of kernel-weighted attraction toward data samples…
arXiv:2608.13514v1 Announce Type: new Abstract: We revisit the problem of learning predictors robust to adversarial examples at test-time. We prove that VC classes are adversarially robustly…
arXiv:2604.06116v2 Announce Type: replace-cross Abstract: Financial statement auditors use a risk-based approach to evidence collection to obtain reasonable assurance. When an initial sample does not support…
arXiv:2312.12323v2 Announce Type: replace-cross Abstract: We study the annealed complexity of Gaussian random homogeneous polynomials on the $(N-1)$-dimensional unit sphere in the presence of deterministic perturbations…
arXiv:2608.12443v1 Announce Type: new Abstract: Neural combinatorial optimization (NCO) relies on parallel solution sampling for training, yet existing methods fail to fully exploit the rich information…
arXiv:2606.29018v2 Announce Type: replace-cross Abstract: We show that net demand for liquidity by algo strategies is identifiable from its trade and price history alone, with no…
arXiv:2408.06401v3 Announce Type: replace Abstract: We study nonconvex optimization in high dimensions through Langevin dynamics, focusing on the multi-spiked tensor PCA problem. In this tensor estimation…
arXiv:2608.13549v1 Announce Type: cross Abstract: The per-instance Jaccard score, or intersection over union (IoU), is standard in multi-label classification and binary segmentation. With $s$ labels, its…
arXiv:2608.12772v1 Announce Type: new Abstract: Evolutionary multitasking allows several related problems to be solved in a single run of an algorithm. In this paper, we investigate…
arXiv:2608.12388v1 Announce Type: cross Abstract: The emergence of orientation selectivity in the primary visual cortex (V1) remains a central question in computational neuroscience. Shirazi's Bayes-Markov model…
arXiv:2608.12679v1 Announce Type: cross Abstract: Large Language Models (LLMs) are increasingly deployed in discovery domains such as math and science. The usual approach is to present…
arXiv:2508.17464v2 Announce Type: replace-cross Abstract: Brain-body co-optimization remains a challenging problem. To understand and overcome its challenges, we exhaustively map a morphology-fitness landscape: we train controllers…
arXiv:2605.08756v2 Announce Type: replace-cross Abstract: Automatic heuristic design (AHD) has emerged as a promising paradigm for solving NP-hard combinatorial optimization problems (COPs). Recent works show that…
arXiv:2606.21253v2 Announce Type: replace-cross Abstract: Continual learning that is gradient-free, local, online, and append-only is attractive for edge and streaming deployment, but its value is usually…
arXiv:2607.10923v4 Announce Type: replace Abstract: Large language models exhibit remarkable emergent behaviors, yet the physical mechanism governing their collective dynamics remains poorly understood. Cognitive Field Theory…
arXiv:2608.12564v1 Announce Type: new Abstract: Automating empirical research is a long-standing direction of AI. Recent automatic research (AutoResearch) agents bring this goal within reach, as modern…
arXiv:2605.01928v2 Announce Type: replace Abstract: We optimize losses that jump: spiking thresholds, quantized layers, and discrete routing put jumps in the forward pass, where backpropagation does…
arXiv:2608.12535v1 Announce Type: new Abstract: Deep learning models are widely used for time series analysis in domains such as healthcare, finance, energy systems, and environmental monitoring.…
arXiv:2510.15076v2 Announce Type: replace Abstract: The $ell_p$-norm objectives for correlation clustering present a fundamental trade-off between minimizing total disagreements (the $ell_1$-norm) and ensuring fairness to individual…
arXiv:2608.12514v1 Announce Type: new Abstract: Deep graph neural networks(GNNs) suffer from oversmoothing- a progressive collapse of node representation towards a low information subspace as network depth…
arXiv:2608.13510v1 Announce Type: cross Abstract: Machine learning procedures are commonly evaluated in terms of predictive accuracy and computational efficiency. However, their achievable performance is fundamentally constrained…
arXiv:2608.12489v1 Announce Type: new Abstract: Organizations decide whom to treat under a budget and want to know what a targeting rule would have earned before deploying…