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Pulse · pasqualepillitteri.it

pasqualepillitteri.it · Oct 28

Anthropic's unreleased AI model builds working exploits for Chrome, Safari, Windows, Linux and Android

Pulse · cnbc.com

cnbc.com · Oct 28

Google alters its cloud playbook, enlisting partners to sell more AI chips

Google started working on in-house silicon in 2013, but didn't begin selling its tensor processing units (TPUs) to outside customers until the second quarter of this year. Now the company is tapping Oracle's cloud as another avenue for selling TPUs as it tries to bolster competition with Nvidia in the AI chip market.

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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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arXiv

Sep 21

SKstars at SHROOM: Visions Agreement-Guided Ensembling of Zero-Shot and LoRA-Adapted Vision--Language Models

This paper describes the SKstars submission to SHROOM-Visions 2026, a shared task on fine-grained hallucination detection in large vision-language model outputs. The task requires systems to identify hallucinated character spans, assign hallucination categories, and provide…

arXiv

Sep 21

H-Spec: Parallel Speculative Decoding Without a Drafter-Side KV Cache

Speculative decoding losslessly accelerates large language model inference by having a lightweight draft model predict future tokens for verification by the target model. Recent block diffusion drafters further reduce drafting latency by predicting multiple tokens in parallel.…

arXiv

Sep 21

LoopCD: Loop-wise Contrastive Decoding for Improving Reasoning in Looped Language Models

Looped Language Models (LoopLMs) perform "latent reasoning" by recursively refining internal latent representations with shared weights, offering a more effective alternative to explicit verbal reasoning. Despite their effectiveness, we find that LoopLMs remain prone to loop…

arXiv

Sep 21

Brain-Inspired Hierarchical Modularity for General Continual Learning

Continual learning, the ability to learn from sequential experience while retaining and adapting prior knowledge, is central to intelligent systems operating in changing environments. However, conventional continual learning is typically studied with offline task-wise training…

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