Claims Browser

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"

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 is a natively multimodal model with 1M-token context window, 320B total parameters / 18B active parameters, released under MIT License

"GLM-5.3-Flash as a natively multimodal model with a 1M-token context window, 320B total parameters / 18B active parameters, released under the MIT License"

GLM-5.3-Flash outperforms GLM-5.2 at every effort level and is on par with Claude Opus 4.8 on coding according to Z.ai's internal benchmark

"claiming on its internal benchmark that it outperforms GLM-5.2 at every effort level and is on par with Claude Opus 4.8 on coding"

Open Chinese labs are converging on similar architecture choices around linear attention, sparse attention, residual path design, and Muon

"posts arguing that open Chinese labs are converging on similar architecture choices around linear attention, sparse attention, residual path design, and Muon"

GLM-5.3-Flash achieves 28% accuracy and 28% hallucination rate on knowledge/factual benchmarks

"AA-Omniscience score: +7Accuracy: 28%Hallucination rate: 28%"

GLM-5.3-Flash uses a hybrid attention architecture with 34 KDA layers and 11 MLA/DSA layers

"Kimi Linear-style 3:1 hybrid attention34 KDA layers (Kimi Delta Attention)11 MLA/DSA layersMLA = Multi-head Latent AttentionDSA = DeepSeek Sparse Attention"

GLM-5.3-Flash costs approximately 1/10th of GLM-5.2 while reducing active parameters from 32B to 18B

"compared to GLM-5.2:~1/10 the costactive params 32B → 18Blayers 92 → 45"

GLM-5.3 Flash became the fastest growing model in Cline history, driving 11% of all traffic in less than a week

"Cline said GLM-5.3 Flash was already its fastest growing model in Cline history, driving 11% of all traffic in less than a week"

The GLM-5.3 infrastructure agent co-authored parts of the work by helping with kernels, bottlenecks, and serving stack optimization

"says the GLM-5.3 infrastructure agent co-authored parts of the work by helping with kernels, bottlenecks, and serving stack optimization"

GLM-5.3-Flash achieves an AA Intelligence Index score of 57 at $0.09 cost per task

"Artificial Analysis reports AA Intelligence Index 57 and $0.09 cost/task, plus various benchmark details and pricing."

The subsampled randomized Hadamard transform (SRHT) achieves coordinate-wise accuracy guarantees for overconstrained L2 regression with O(ε^-2 d^(1+Θ(√(log log n/log d)))) rows

"Price, Song and Woodruff initiated the study of this problem and showed that the subsampled randomized Hadamard transform (SRHT) with $O(ε^{-2} d^{1+Θ(\sqrt{\log\log n/\log d})})$ rows achieves this guarantee."