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Pulse · finance.biggo.com

finance.biggo.com · Oct 29

Nvidia Releases Open-Weight 'Nemotron 3.5' Speech Recognition AI Covering 40 Languages

Nvidia has released "Nemotron 3.5 Transcribe" as an open-weight model that converts speech in up to 40 languages into text and supports translation…

Pulse · finance.yahoo.com

finance.yahoo.com · Oct 28

Jamie Dimon Warns AI Agents Could Be ‘Treacherous’ for Banks, but Researcher Says There May Be Another Reason to Worry

JPMorgan Chase & Co. CEO Jamie Dimon has warned that artificial intelligence agents accessing customer accounts pose serious security risks, but AI researcher Andrew Yoon argued banks may have another reason to resist the technology: the potential threat to their...

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Top picks this weekarXiv

arXiv

Oct 27

Honeycomb-Surfels: Compact Hexagonal Primitives for Efficient Scene Rendering and Reconstruction

Novel view synthesis and 3D reconstruction have advanced rapidly with radiance fields and splatting, yet their memory and computational demands limit their use in resource-constrained settings. We introduce Hexels: novel planar hexagonal surface elements with a first-order spatial color field for efficient 3D scene and 2D image representation. Hexels exploit locally planar regions with smoothly varying appearance using fewer, larger primitives. Their hexagonal support provides an area- and boundary-efficient domain, while the local color field captures appearance variation within each primitive. Across 34 scenes, Hexels improve PSNR over memory-matched 2DGS by 4.53-7.38 dB across datasets while requiring lower training VRAM. We further ablate Hexels by showing that per primitive, Hexels outperform 2DGS by upto 4.74 dB while using lower training VRAM and having 2.31 times faster FPS. Even in unconstrained settings, Hexels offers complementary quality-memory trade-offs. For efficient 3D surface reconstruction, Hexels reduce error by 28.9% compared with memory-matched 2DGS across 15 DTU scans. On 6 Tanks and Temples surface scenes, Hexels achieves the highest mean F1 among compared…

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Hugging Face

Sep 29

The Turbo Brilliance system (27 reasoning, 27 instruct and 9 xhigh modes) vastly augments the models core performance and includes interactive help system directly connected to the model.

VLM4Cluster: Benchmarking Deep Clustering In the Era of Vision-Language Pre-training

Vision-language pre-training has reshaped image clustering, giving rise to language-assisted image clustering (LaIC), which leverages textual semantics to complement visual representations. Despite the rapid proliferation of LaIC methods, it remains unclear how much LaIC has…

arXiv

Sep 29

Where Predictive Supervision Goes Shapes What VLA Policies Learn

Future prediction is increasingly used to improve vision-language-action (VLA) policies, based on the premise that anticipating scene evolution encourages representations useful for control. However, forecast quality alone does not establish that a policy has learned a better…

arXiv

Sep 29

OCA: ODE-Driven Cross-Attention for Image-to-Point-Cloud Registration

Cross-attention is a crucial component in learning-based image-to-point-cloud (I2P) registration. Although existing cross-attention mechanisms have achieved promising progress, attention ambiguity remains a fundamental challenge that hinders the learning of discriminative 2D-3D…

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