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.
PySpark Optimization: 12 Proven Techniques to Speed Up Your Spark Jobs
Modern data pipelines handle massive volumes of structured and unstructured data every day. As datasets grow, poorly optimized Spark jobs become slower, more expensive, and harder to scale. Common issues include long execution times, excessive shuffling, memory bottlenecks, and inefficient joins. Ef...
The Statistics of Token Selection: Logits, Temperature, and Top-P Walkthrough
When large language models, or LLMs for short, produce outputs, several criteria are at stake, including not only overall response relevance but also coherence and creativity.
The following article originally appeared on Addy Osmani’s blog and is being reposted here with the author’s permission. The default behavior of any AI coding agent is to take the shortest path to “done.” Ask for a feature and it writes the feature. It doesn’t ask whether you have a spec, write a te...