RAG Is Not Machine Learning, and the ML Toolkit Solves the Wrong Problem
Enterprise Document Intelligence [Vol.1 #3] - Why the ML toolkit (hyperparameter sweeps, train/test splits, explainability frameworks) solves the wrong problem, and what to use instead
The post RAG Is Not Machine Learning, and the ML Toolkit Solves the Wrong Problem appeared first on Towards Data Sc...
Meet Memory OS: A 6-Layer Open-Source Memory Stack Built on Top of Hermes Agent
The open-source project adds local persistent memory to Hermes Agent through six layers, gated retrieval, and a wiki.
The post Meet Memory OS: A 6-Layer Open-Source Memory Stack Built on Top of Hermes Agent appeared first on MarkTechPost.
A collage of I/O-related images, including the Antigravity Coffee Co. pop-up, a colorful jellyfish and a still from the Timmy TPU video. The word AI repeats three times on the left of the image, and there are colorful icons, including a sparkle, as well.
Mocking a Year of IoT Sensor Time Series Data with Mimesis
In this guide, you will learn the process of generating a year's worth of daily temperature readings, mimicking a seasonal curve that looks like real — all together with device-level metadata, and ready to build based on open-source frameworks.
Building the infrastructure for the Intelligence Age in Michigan
OpenAI breaks ground on a 1GW data center project in Michigan as part of Stargate, building AI infrastructure to expand access, create jobs, and support communities.
With the rise of agents, many people have been proclaiming that the age of software as a service (SaaS) is over. Who needs to subscribe to a service when you can create your own software with a few English-language prompts and a few dollars spent on tokens? Your own software, most likely a skill tha...
OpenAI frontier models and Codex are now available on AWS
OpenAI frontier models and Codex are now generally available on AWS, giving enterprises a new path to build with OpenAI through the AWS environments, controls, and procurement workflows they already use. Customers can get started with OpenAI on AWS and move faster from evaluation to production.
NVIDIA AI Cloud Ecosystem Expands Worldwide to Meet Global AI Compute Demand
The NVIDIA AI Cloud ecosystem is accelerating the global buildout of AI factory infrastructure. Partners are expanding capacity to meet growing demand from enterprises, startups, nations, AI labs and developers scaling agentic AI applications. NVIDIA AI Clouds are a growing ecosystem of purpose-bui...
Taiwan’s Industry Titans Turbocharge World’s AI Infrastructure Buildout With NVIDIA
Taiwan is home to more than 500 NVIDIA ecosystem partners. More than 1 million NVIDIA MGX rack components for NVIDIA Vera Rubin infrastructure come together in Taiwan, from across 25 factory sites. As Vera Rubin ramps into full production to power agentic AI factories worldwide, that ecosystem spans...
NVIDIA Levels Up Local AI Agents Across RTX PCs and DGX Spark
Personal agents are exploding in popularity, with open source projects like OpenClaw and Hermes seeing rapid adoption by AI developer communities on GitHub. Built to adapt to individual preferences and workflows, these agents can interact with applications, generate content, automate repetitive proc...
QASM-Eval: A Dataset to Train and Evaluate LLMs on OpenQASM-3 Beyond Quantum Circuits
arXiv:2605.30358v1 Announce Type: new
Abstract: Quantum computing remains in the Noisy Intermediate-Scale Quantum (NISQ) era, where the performance is highly constrained to noise. Addressing the limitation often requires hardware-facing capabilities beyond gate-sequence circuit specification, inclu...
Gait2Hip-60: A Unified Deep Learning Benchmark for Predicting Hip Muscle Forces and Joint Moments from Multi-Cadence Gait Kinematics
arXiv:2605.30374v1 Announce Type: new
Abstract: Estimating hip muscle forces and joint moments during gait typically relies on musculoskeletal simulation, which is informative but time-consuming and difficult to apply in clinical settings. This study developed a deep learning framework to predict t...
Unicorn: Scaling High-Dimensional Time Series Forecasting via Universal Correlation Modeling
arXiv:2605.30376v1 Announce Type: new
Abstract: Modern time series architectures face a fundamental trade-off: channel-independent models scale well with increasing data volume but ignore critical inter-channel dependencies, while channel-dependent models are expressive but remain ``dimension-bound...
PhyDrawGen: Physically Grounded Diagram Generation from Natural Language
arXiv:2605.30512v1 Announce Type: new
Abstract: Generating physics diagrams from text requires strict adherence to physical laws. While current generative models produce visually plausible outputs, they systematically hallucinate force vectors, ignore conservation laws, and violate geometric constr...
Physically Viable World Models: A Case for Query-Conditioned Embodied AI
arXiv:2605.30542v1 Announce Type: new
Abstract: World models for embodied AI must be physically viable: constructed to answer intervention queries by representing the physical structure governing action outcomes, rather than merely predicting future observations. Existing observation-predictive wor...
Uncertainty-Aware and Temporally Regulated Expert Advice in Reinforcement Learning for Autonomous Driving
arXiv:2605.30576v1 Announce Type: new
Abstract: Exploration in reinforcement learning for autonomous driving is inherently unsafe: agents must experience novel behaviors to learn, yet exploration can lead to collisions or off-road driving. We propose an uncertainty-aware framework that leverages ex...