OpenAI Launches the Agents API in Public Beta, Putting the Codex Harness Behind One API Call
OpenAI has released the Agents API in public beta. It gives developers the same harness and infrastructure that run Codex. OpenAI hosts and maintains the harness. Developers run the agent’s compute in an OpenAI-managed sandbox, their own infrastructure, or a partner sandbox. Is it deployable? Yes. I...
Skild AI Taps NVIDIA Physical AI to Teach Robots New Tasks From a Single Video
Manufacturing floors, warehouses and production lines rarely stay fixed — tasks change, layouts shift and new products arrive, and most robots can’t keep up without significant reprogramming. Skild AI’s new S1 robot foundation model helps address this, designed to learn previously unseen, long-horiz...
Feature Engineering in Scikit-Learn: A KDnuggets Cheat Sheet
Once feature engineering lives inside a Pipeline, each step is fitted on training data only, and the model is scored what it actually earned. And that is the idea behind this new cheat sheet.
Physical AI Takes the Wheel: How the World’s Robotaxi Leaders Are Building With NVIDIA Technologies
The global robotaxi market — physical AI’s first commercial breakthrough — is projected to reach $400 billion by 2035, with over 6 million commercial vehicles in operation as driverless fleets are already moving people through some of the world’s busiest and most complex streets. Deploying a driverl...
Reminder: Live Today — Building AI Agents, The Loop
Quick note — The Loop’s first session is today, 4:30 PM PDT, live on Zoom.Free, monthly, and genuinely hands-on: what building an AI agent actually looks like, why the internet is shifting from static information to something built for agents and people, and what that shift means for anyone still th...
Why autonomous agents expose a new explainability problem in fraud detection
The post What SHAP Can't Explain About Agentic AI Fraud appeared first on Towards Data Science.
7 signs your AI infrastructure is still stuck in the HPC era
Your GPU dashboard can look perfectly healthy while doing almost no useful work, and most enterprises are staring at exactly that chart right now. The real bottleneck rarely lives in the silicon. It lives in the storage, pipelines, and scheduler...
d-Matrix Adopts NVIDIA NVLink Fusion for Rack-Scale XPU Deployment
AI inference chipmaker d-Matrix today announced it will use NVIDIA NVLink Fusion to connect its next-generation Raptor XPUs to NVIDIA’s AI infrastructure platform — joining a growing roster of ecosystem partners. By connecting Raptor to NVIDIA NVLink scale-up and Spectrum-X scale-out networking, the...
Optimizing LLM Inference Costs in Multi-Agent Systems with Adaptive Model Routing
Moving from static model assignment to intelligent, task-level LLM selection.
The post Optimizing LLM Inference Costs in Multi-Agent Systems with Adaptive Model Routing appeared first on Towards Data Science.
Who Questions What Works: When Should We Retest Our Assumptions?
A model is only as reliable as the assumptions behind it
The post Who Questions What Works: When Should We Retest Our Assumptions? appeared first on Towards Data Science.
DeepSeek AI Released DeepSeek-V4.1-Flash with 1M Context, FP4 KV Cache, and Cross-Layer Attention Reuse
Long-horizon agents have turned LLM serving into an input-heavy workload. Repeated prefills and million-token contexts leave KV caches that strain HBM, SSD capacity, and bandwidth. DeepSeek AI built its newest release around that exact bottleneck. DeepSeek-V4.1-Flash is a multimodal Mixture-of-Exper...
Bodhan AI Releases Four Indic Models for OCR, Translation and Speech
A Hindi lesson can mix English terms (loan words), scanned tables and handwritten equations. Making that content searchable, translating it and reading it aloud requires several kinds of AI. Bodhan AI and AI4Bharat’s four new models target those jobs across Indian languages. Released in September 2...
AhaBench: Do Agents Learn from Prior Experience? A Benchmark for Long-Horizon Continual Learning
arXiv:2609.05435v1 Announce Type: new
Abstract: Modern language agents are expected to operate over long horizons: they ask follow-up questions, reuse worked examples, handle tool feedback, and adapt to delayed consequences. Most evaluations still reset the agent after a prompt or score only the fi...
Capsule Lens: Locating and Tracking Concept Geometry in Model Representations
arXiv:2609.05575v1 Announce Type: new
Abstract: Understanding how concepts are encoded in the internal representations of machine learning models is a central problem in mechanistic interpretability, essential both for the science of deep learning and for the trustworthy deployment of increasingly ...
HB-PVI: A Hierarchical Bayesian Personalization and Value-of-Information Framework for Complex Activity Recognition
arXiv:2609.05582v1 Announce Type: new
Abstract: Personalization can improve activity-recognition performance, but participant-specific gains are heterogeneous, and every additional calibration label has an acquisition cost. This study presents HB-PVI, a hierarchical Bayesian personalization and val...
Adaptive Entangled Game Modules in Artificial General Intelligence
arXiv:2609.09226v1 Announce Type: new
Abstract: We introduce a probability-wave framework for modeling the collective behavior of interacting adaptive agents, deriving testable eigenmodes through a generalized behavioral intelligence (GBI) nonlocal probability-wave equation. This framework captures...
An Autonomous GeoAI Agent for Arctic Eco-Navigation
arXiv:2609.09374v1 Announce Type: new
Abstract: Arctic maritime navigation is becoming increasingly important as changing sea-ice conditions expand seasonal accessibility while simultaneously introducing substantial operational, environmental, and community risks. Arctic route planning is inherentl...