Claims Browser

Foundation models for chemistry, agentic workflows, simulation, and automated experimentation are dramatically accelerating the search for new materials

"foundation models for chemistry, agentic workflows, simulation, and automated experimentation are dramatically accelerating the search for new materials and reshaping scientific discovery."

Waves may become a new computational primitive for neural networks

"why waves may become a new computational primitive for neural networks"

Compositional generalization is typically assessed using model accuracy as the primary metric

"Compositional generalization is usually evaluated through model accuracy."

An alternative approach to evaluating compositional generalization examines which structural or lexical identifications enable held-out examples to be admissible based on training structures

"We instead ask which structural or lexical identifications make held-out COGS examples admissible from the structures observed in training."

Across 21 COGS generalization types, admissibility patterns follow distinct identification profiles and residual failures reveal unsupported structural templates

"Across 21 COGS generalization types, admissibility follows distinct identification profiles, while residual failures separate unsupported structural templates."

Data-side diagnostic approaches can characterize what a training corpus licenses under specified identifications without requiring training of a predictive model

"These data-side diagnoses characterize what the training corpus licenses under specified identifications, without training a predictive model."

Traditional credit risk assessment methods fail to differentiate between temporal patterns indicative of credit risk and those reflecting general customer behavior, leading to suboptimal risk predictions

"Specifically, they fail to differentiate between temporal patterns indicative of credit risk and those reflecting general customer behavior or preferences, leading to suboptimal risk predictions."

The DTD-VAE framework achieves performance gains of 3.2%-4.86% in ROC-AUC and 6.41%-9.71% in Accuracy Ratio compared to existing methods on real-world datasets

"Extensive experiments on six real-world datasets demonstrate that the proposed framework consistently outperforms existing methods, achieving performance gains of 3.2%-4.86% in ROC-AUC and 6.41%-9.71% in Accuracy Ratio."

Existing point-cloud reconstruction pipelines typically return a single best-fit structure without uncertainty quantification

"existing point-cloud reconstruction pipelines typically return a single best-fit structure without uncertainty quantification"

A fully Bayesian framework for point-cloud data can accurately reconstruct closed curves with quantified uncertainty

"Numerical experiments, including synthetic examples and real-world LiDAR datasets, show accurate reconstructions and quantified uncertainty over the recovered curves"

Learning cannot be reduced to proper learning by enlarging the hypothesis class, as there exists a learnable multiclass problem that cannot be embedded in any properly learnable class

"we exhibit a learnable multiclass problem that cannot be embedded in any properly learnable class, meaning learning cannot be reduced to proper learning by enlarging the hypothesis class"

Proper learning can require training error, and every properly learnable class admits a proper learner making o(m) errors on samples of size m, but every prescribed sublinear scale is necessary for some properly learnable problem

"proper learning can require training error and characterize this phenomenon precisely: every properly learnable class admits a proper learner making $o(m)$ errors on samples of size $m$, but every prescribed sublinear scale $a_m=o(m)$ is necessary for some properly learnable problem"

Regularization is not a general learner, as there exists a properly learnable class that cannot be learned by any Structural Risk Minimization learner, and a learnable class that cannot be learned by any local regularizer

"regularization is not a general learner: we exhibit a properly learnable class that cannot be learned by any Structural Risk Minimization (SRM) learner, and a learnable class that cannot be learned by any local regularizer"

All known general-purpose multiclass learners rely on intricate orientations of exponentially large one-inclusion structures

"all known general-purpose multiclass learners rely on intricate orientations of exponentially large one-inclusion structures"

Familiar algorithmic principles such as proper learning and regularization remain poorly understood in statistical learning theory

"familiar algorithmic principles such as proper learning and regularization remain poorly understood"

Nvidia is acquiring HuggingFace for $13B, roughly 80x their $150M ARR

"Nvidia is buying HuggingFace for $13B, roughly 80x their $150M ARR, having doubled its customer base in 2026"

HuggingFace doubled its customer base in 2026

"having doubled its customer base in 2026"

GLM-5.3-Flash (Ox Alpha) impressed everyone except GDM vaguepoasters

"GLM-5.3-Flash (aka Ox Alpha) impressing everyone (except GDM vaguepoasters)"