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Pulse · msn.com

msn.com · Oct 28

Record 115 State AI Laws in 2026: Illinois Sets Frontier Audit Mandate, Congress Still Silent

State AI laws in 2026 reached 115 across 30 states, a nearly 58 percent surge from last year, as state legislatures raced to fill a federal governance vacuum. Illinois became the first US state to mandate annual independent third-party audits of the largest AI developers,

Pulse · livemint.com

livemint.com · Oct 28

OpenAI turns to Google's AI chips amid race to challenge Nvidia

OpenAI plans to buy AI chips from Google as it expands computing capacity, according to a person familiar with the discussions. The potential purchase complements plans to use in-house chips later this year and reflects efforts across the industry to develop alternatives to Nvidia processors.

Pulse · msn.com

msn.com · Oct 28

OpenAI and Anthropic AI models claim to refute computing hypothesis

OpenAI and Anthropic assert that their AI models have posed a challenge to a traditional view concerning the limits of computing. If proven true, the findings can reshape complexity theory. However, neither of the firms has succeeded in creating faster software or in cracking current encryption.

Pulse · note.com

note.com · Oct 28

Who Verifies the 'Reliability' of AI? How OpenAI's Collaboration with Academic Institutions is Changing the Future of the AI Industry

Do you have a basis for 'trusting' AI?'This AI is safe,' 'This AI is accurate.'In most cases, it is the companies that created the AI themselves saying these things.OpenAI has announced a formal colla

Pulse · msn.com

msn.com · Oct 28

OpenAI plans to buy Google's AI chips, The Information reports

Oct 27 (Reuters) - OpenAI plans to buy Google's AI chips, The Information reported on Tuesday, citing a person familiar with the matter, as technology companies ramp up spending on data centers and semiconductors to meet surging demand for AI computing.

Pulse

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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arXiv

Sep 20

Comparative Study of Quantum and Classical Machine Learning Models in Binary Classification

A potential path forward is Quantum Machine Learning (QML), which aims to leverage quantum computing in conjunction with classical machine learning to enhance computing efficiency and the expressiveness of models. In this paper, two different quantum classifiers - Variational…

arXiv

Sep 20

Can't Find Waldo: Evaluating VLMs' Sensitivity to Image Resolution and Detail Level

Visual Language Models (VLMs) have achieved remarkable success across diverse tasks, yet they struggle with high-resolution inputs where critical information resides in small regions or detailed, cluttered scenes. While several approaches address this limitation, a systematic…

arXiv

Sep 20

RPMem: Learning Long-Term Recurrent Parametric Memory Across Sessions for LLM Agents

Long-running LLM agents require memory that persists and evolves across sessions. Text-based memory retrieves and reconstructs past interactions at every query, making long-horizon performance increasingly dependent on retrieval quality and contextual reasoning as histories…

arXiv

Sep 20

Propose, Verify, Commit: Evidence-Grounded Memory for Long-Horizon Multi-Actor Conversations

Memory enables language model agents to use information beyond their context windows, but maintaining and accessing this information reliably remains difficult. Long-horizon conversational memory is especially challenging in multi-actor settings, where relevant evidence is…

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