Vertu wants CEOs to run companies from an AI foldable starting at $6,880
Built on top of the open-source Hermes project, Vertu's new foldable combines AI-agent workflows, enterprise integrations, and ultra-premium luxury finishes.
Identifying and Understanding Human Values in Text: A Tailorable LLM-based Architecture
arXiv:2605.27373v1 Announce Type: new
Abstract: As intelligent systems become more autonomous, the scientific community focuses on creating decision-making mechanisms that include ethical and moral considerations, unlike traditional utility-maximisation models. To achieve this, a key aspect is asse...
Soro: A Lightweight Foundation Model and Chatbot for Tajik
arXiv:2605.27379v1 Announce Type: new
Abstract: We present Soro, a family of Tajik-specialized conversational large language models (LLMs) designed for real-world deployment under tight compute and connectivity constraints in Tajikistan. Starting from open-weight Gemma 3 checkpoints, we perform Taj...
DynaSchedBench: Calibrated Dynamic Scheduling Benchmarks and Observability Paradox in LLM-based Scheduling Agents
arXiv:2605.27566v1 Announce Type: new
Abstract: Progress in neural combinatorial optimization for Dynamic Flexible Job Shop Scheduling Problem (DFJSP) is currently hindered by a methodological tension: static benchmarks encourage benchmark overfitting, while uncalibrated generators obscure algorith...
Personalized Observation Normalization for Federated Reinforcement Learning in Simulation Environments with Heterogeneity
arXiv:2605.27385v1 Announce Type: new
Abstract: Federated reinforcement learning (FedRL) enables multiple agents to collaboratively train a global policy without sharing raw data, making it ideal for privacy-sensitive applications. However, FedRL faces challenges in heterogeneous environments where...
IGADA-IoT: IoT Sensor Energy Optimization in Wireless Sensor Networks Driven by Automatic Data Augmentation
arXiv:2605.27397v1 Announce Type: new
Abstract: In wireless sensor networks (WSNs), data augmentation is a novel method to improve sampling-frequency decision performance, thereby enabling energy optimization for IoT (Internet of Things) sensors. However, existing methods rely on a single generator...
A Simple State Space Model Excels at Multivariate Time Series Classification
arXiv:2605.27406v1 Announce Type: new
Abstract: Structured state space models (SSMs) have recently emerged as a promising foundation for sequence modeling, with Mamba-based architectures demonstrating strong performance through input-dependent state transitions, albeit at considerable complexity. H...
$E^3$-Agent: An Executable and Evolving Agent for Resource Management of Edge Generative Inference
arXiv:2605.27428v1 Announce Type: new
Abstract: Edge deployments of generative inference increasingly face two practical realities: per-device per-model performance is often unknown at deployment time, and it is non-stationary due to user-driven semantic events, background load, and device churn. C...
Tackling Multimodal Learning Challenges with Mixture-of-Expert: A Survey
arXiv:2605.27431v1 Announce Type: new
Abstract: Mixture-of-Experts (MoE) presents a naturally compatible and scalable framework for multimodal learning, demonstrating strong adaptability across diverse modalities and tasks. Despite its growing success, a comprehensive and systematic review on the M...
Sakana AI Proposes DiffusionBlocks: a Block-wise Training Framework That Converts Residual Networks into Independently Trainable Denoising Modules
DiffusionBlocks converts residual networks into independently trainable blocks by interpreting layer updates as reverse diffusion denoising steps.
The post Sakana AI Proposes DiffusionBlocks: a Block-wise Training Framework That Converts Residual Networks into Independently Trainable Denoising Modul...
Your SEO strategy is optimized for a search engine that no longer exists.
Google I/O made it official: AI-generated answers are now front and center in search, and most brands have almost no visibility into how AI is describing them to their customers. For anyone who has spent years building a strategy around 10 blue links, the rules just changed in a pretty significant w...
NVIDIA Releases Polar, a Token-Faithful Rollout Framework for GRPO Training Across Codex, Claude Code, and Qwen Code
NVIDIA researchers have introduced Polar, a rollout framework that trains language agents using reinforcement learning without modifying their agent harnesses. Polar places a model API proxy between the harness and the inference server, capturing token-level interactions and reconstructing trainer-r...
Last week, we had our first Infrastructure & Ops superstream of 2026, Platform Engineering in the Age of AI. Our speakers explored a range of topics focused on supporting new AI workloads, each with unique infrastructure needs, unpredictable costs, and novel security concerns. Google Cloud’s Abdel S...
AI Factories: The New Infrastructure of Intelligence
AI factories are token factories, converting power into intelligence in real time. And as agentic AI scales and autonomous, always-on special agents are deployed in the enterprise, performance per watt and cost per token become the economics that matter.
Understanding AI as an extension of human intelligence—not a replacement for it—offers a more grounded path for building trustworthy AI systems.
The post Extending Human Intelligence Through AI appeared first on Microsoft Research.
Learning From Pairwise Preferences: An Introduction to the Bradley Terry Model
How to Turn Simple Head-to-Head Choices Into Probabilistic Rankings
The post Learning From Pairwise Preferences: An Introduction to the Bradley Terry Model appeared first on Towards Data Science.
Startup Battlefield 200 applications close today: Nominate a founder or submit your startup
Today is the final day to apply or nominate a startup for Startup Battlefield 200. Once the clock strikes 11:59 p.m. PT, the window closes on your chance to compete for $100,000 in equity-free funding, gain global visibility, connect directly with investors, and launch on the TechCrunch Disrupt stag...
Most AI Agents Fail in Production Because They’re Built Backwards
Good models don't save bad architecture, and most teams learn that the hard way.
The post Most AI Agents Fail in Production Because They’re Built Backwards appeared first on Towards Data Science.