OpenAI for Singapore launches a multi-year AI partnership to expand deployment, build local talent, and support businesses and public services with AI.
The following article originally appeared on the Elevate newsletter and is being reposted here with the author’s permission. Peek under the hood of most “production agents” shipping today and you won’t find intelligence. You’ll find custom plumbing, fragile session logic, shared service accounts, an...
NVIDIA AI Releases Nemotron-Labs-Diffusion: A Tri-Mode Language Model with 6× Tokens Per Forward Over Qwen3-8B
NVIDIA researchers have released Nemotron-Labs-Diffusion, a language model family that unifies three decoding modes in one architecture. The model supports autoregressive (AR) decoding, diffusion-based parallel decoding, and self-speculation decoding. It is available in 3B, 8B, and 14B parameter siz...
Alibaba Qwen Team Introduces Qwen3.5-LiveTranslate-Flash: Real-Time Multimodal Interpretation Across 60 Languages at 2.8-Second Latency
Alibaba's Qwen team has released Qwen3.5-LiveTranslate-Flash, a real-time multimodal translation model that processes audio and video simultaneously. The model covers 60 input languages and produces speech output in 29 languages at 2.8 seconds of latency. Key additions over the previous Qwen3 versio...
Gemini 3.5 Flash: frontier intelligence with speed
Google Gemini’s next-generation family offering: Gemini 3.5 is here! Gemini 3.5 Flash combines frontier intelligence with real-world action and supports high-speed agentic workflows, coding, and multimodal reasoning while maintaining the low latency expected from the Flash series. With Gemini 3.5 P...
Google Introduces Gemini 3.5 Flash at I/O 2026: A Faster and Cheaper Model for AI Agents and Coding
Google's Gemini 3.5 Flash beats its own flagship on coding and agentic benchmarks while running four times faster and at half the cost.
The post Google Introduces Gemini 3.5 Flash at I/O 2026: A Faster and Cheaper Model for AI Agents and Coding appeared first on MarkTechPost.
Dimensional Balance Improves Large Scale Spatiotemporal Prediction Performance
arXiv:2605.18793v1 Announce Type: new
Abstract: Accurate spatiotemporal pattern analysis is critical in fields such as urban traffic, meteorology, and public health monitoring. However, existing methods face performance bottlenecks, typically yielding only incremental gains and often exhibiting lim...
Robust Basis Spline Decoupling for the Compression of Transformer Models
arXiv:2605.18794v1 Announce Type: new
Abstract: Decoupling is a powerful modeling paradigm for representing multivariate functions as compositions of linear transformations and univariate nonlinear functions. A single-layer decoupling can be viewed as a fully connected neural network with a single ...
HELLoRA: Hot Experts Layer-Level Low-Rank Adaptation for Mixture-of-Experts Models
arXiv:2605.18795v1 Announce Type: new
Abstract: Low-Rank Adaptation (LoRA) dominates parameter-efficient fine-tuning of large language models, yet most variants target dense architectures. Mixture-of-Experts (MoE) models scale parameters at near-constant per-token compute, and their sparse activati...
UCCI: Calibrated Uncertainty for Cost-Optimal LLM Cascade Routing
arXiv:2605.18796v1 Announce Type: new
Abstract: LLM cascades and model routing promise lower inference cost by sending easy queries to a small model and escalating hard ones to a large model, but most deployed routers use uncalibrated confidence scores and require per-workload threshold tuning. We ...
Simply Stabilizing the Loop via Fully Looped Transformer
arXiv:2605.18797v1 Announce Type: new
Abstract: Scaling model performance typically requires increasing model size. Looped Transformer offers a compelling alternative by iteratively reusing the same Transformer blocks, trading additional computation for improved performance without increasing param...
Position: Let's Develop Data Probes to Fundamentally Understand How Data Affects LLM Performance
arXiv:2605.18801v1 Announce Type: new
Abstract: Data is fundamental to large language models (LLMs). However, understanding of what makes certain data useful for different stages of an LLM workflow, including training, tuning, alignment, in-context learning, etc., and why, remains an open question....
Operationalizing Document AI: A Microservice Architecture for OCR and LLM Pipelines in Production
arXiv:2605.18818v1 Announce Type: new
Abstract: Academic research tends to focus on new models for document understanding creating a wide gap in the literature between model definition and running models at production scale. To close that gap, we present a microservice architecture that encapsulate...
Evaluating the Utility of Personal Health Records in Personalized Health AI
arXiv:2605.18937v1 Announce Type: new
Abstract: Patient-managed Personal Health Records (PHRs) promises to empower patients to better understand their health; but information in the record is complex, potentially hindering insights. In this study, we assess the potential of large language models (L...
Learn-by-Wire Training Control Governance: Bounded Autonomous Training Under Stress for Stability and Efficiency
arXiv:2605.19008v1 Announce Type: new
Abstract: Modern language-model training is increasingly exposed to instability, degraded runs, and wasted compute, especially under aggressive learning-rate, scale, and runtime-stress conditions. This paper introduces Learn-by-Wire Guard (LBW-Guard), a bounded...
AgentNLQ: A General-Purpose Agent for Natural Language to SQL
arXiv:2605.19010v1 Announce Type: new
Abstract: Natural language to SQL (NL2SQL) conversion is an important problem for researchers and enterprises due to the ubiquitous importance of relational databases in broad-ranging practical problems. Despite the rapid advancements in the capabilities of LLM...
The next phase of OpenAI’s Education for Countries
OpenAI advances Education for Countries, expanding AI adoption in schools with new partnerships, teacher training, and tools to improve global learning outcomes.
An OpenAI model has disproved a central conjecture in discrete geometry
An OpenAI model solved the 80-year-old unit distance problem, disproving a major conjecture in discrete geometry and marking a milestone in AI-driven mathematics.
Google Launches Antigravity 2.0 at I/O 2026: A Standalone Agent-First Platform with CLI, SDK, Managed Execution, and Enterprise Support
Google used its I/O 2026 developer keynote to ship a meaningful architectural shift in how it packages AI-assisted development. The company announced Google Antigravity 2.0 — a standalone desktop application built entirely around agent orchestration alongside an Antigravity CLI, an Antigravity SDK, ...
NVIDIA and Google Cloud Empower the Next Wave of AI Builders
At this year’s Google I/O conference, NVIDIA and Google Cloud are accelerating the work of more than 100,000 developers in the companies’ joint developer community, which provides curated learning paths, hands-on labs and events that help them build using the full-stack NVIDIA AI platform on Google ...
Listen to the session or watch below Elon Musk lost his suit against OpenAI, in which he alleged CEO Sam Altman and President Greg Brockman had deceived him over the company’s non-profit status. Watch as AI reporter and attorney Michelle Kim, who covered the trial for MIT Technology Review, joins in...