Every story across every category, newest first. Each card links to the original publisher; daily-brief posts open as editorial pages.
arXiv:2606.30958v1 Announce Type: cross Abstract: With the emergence of various tensor data, tensor completion from partial measurements has attracted widespread attention in data science and signal…
arXiv:2606.31963v1 Announce Type: cross Abstract: Modern LLM workflows move coordinate-indexed objects across checkpoints: steering vectors, sparse autoencoders, top-$k$ neuron sets, attribution lists, and merge alignments. This…
arXiv:2606.15837v2 Announce Type: replace-cross Abstract: Deep neural networks (DNNs) frequently fail to generalize to out-of-distribution (OOD) medical images because of variations in scanners and acquisition protocols.…
arXiv:2605.09235v2 Announce Type: replace-cross Abstract: One-step generative modeling has emerged as a leading approach for amortizing the inference cost of diffusion and flow-matching models. Among distillation-free…
arXiv:2606.30831v1 Announce Type: cross Abstract: We study the squared singular value spectrum of a product of non-square random matrices, a setting that also corresponds to the…
arXiv:2602.20062v2 Announce Type: replace-cross Abstract: Pretraining and fine-tuning are central stages in modern machine learning systems. In practice, feature learning plays an important role across both…
arXiv:2602.13184v2 Announce Type: replace-cross Abstract: Unbinned likelihood fits maximize the information extracted from experimental data, yet their application in realistic high-dimensional analyses has been fundamentally bottlenecked…
arXiv:2606.30930v1 Announce Type: new Abstract: Modern deep learning has been shown to operate at the edge of stability, routinely using learning rates far larger than those…
arXiv:2606.31063v1 Announce Type: new Abstract: Gaussian process inference is often limited by cubic computational costs, a challenge that becomes more pronounced in spatio-temporal settings where posterior…
arXiv:2606.31253v1 Announce Type: new Abstract: The classical $k$-means clustering, based on distances computed from all data features, cannot be directly applied to incomplete data with missing…
arXiv:2606.30648v1 Announce Type: cross Abstract: Causal mediation analysis decomposes a treatment effect into indirect pathways through mediators and direct pathways not operating through them. Modern biomedical…
arXiv:2606.31915v1 Announce Type: new Abstract: While conformal prediction provides a general framework for uncertainty quantification in predictive inference, its application is often limited by computational cost.…
arXiv:2606.30702v1 Announce Type: cross Abstract: Structured tabular data dominates clinical medicine, yet existing benchmarks fail to reflect real-world properties like complex survey sampling, demographic oversampling, and…
arXiv:2606.30659v1 Announce Type: cross Abstract: Small area estimation borrows strength across domains to repair the poor precision of direct survey estimators. Two philosophies dominate the area-level…
arXiv:2606.09052v3 Announce Type: replace-cross Abstract: Self-evolution offers a scalable path to stronger reasoning: a pretrained language model improves itself with only minimal external supervision. Yet existing…
arXiv:2606.30889v1 Announce Type: new Abstract: Alternating recurrent events -- event-times of a specific nature that trigger a secondary refractory period -- occur in a wide-range of…
arXiv:2507.14661v2 Announce Type: replace Abstract: Semi-supervised domain adaptation (SSDA) seeks to achieve accurate predictions in a target domain with limited labeled target data by exploiting abundant…
arXiv:2606.30668v1 Announce Type: new Abstract: What happens when LLM agents operate with no context outside a turn, minimal prompting, and simple tools? Inspired by swarm engineering,…
arXiv:2606.30669v1 Announce Type: new Abstract: Backpropagation-trained dense neural networks are powerful function approximators, but they couple learning across many parameters and can overwrite previous associations when…
arXiv:2606.30676v1 Announce Type: new Abstract: Deploying spiking neural networks (SNNs) on neuromorphic hardware demands aggressive synaptic pruning while preserving temporal computation integrity. Existing strategies either neglect…
arXiv:2606.31584v1 Announce Type: new Abstract: This paper evaluates the Metabolic Multi-Agent Optimizer (MMAO) under a stricter empirical protocol rather than reintroducing the framework itself. The study…
arXiv:2606.31789v1 Announce Type: new Abstract: Anomaly detection in multivariate time series remains a critical challenge in large-scale distributed systems, where related entities may exhibit transferable precursor…
arXiv:2606.31990v1 Announce Type: new Abstract: We analyze the effect of optimizing the initial population of genetic programming (GP) for symbolic regression (SR) on the accuracy and…
arXiv:2606.31700v1 Announce Type: cross Abstract: Biological neural circuits obey Dale's principle: each neuron's synapses are uniformly excitatory or inhibitory. Artificial networks that respect this constraint must…