White House Weighs AI Checks Before Public Release, Silicon Valley Warned
President Donald Trump’s White House is contemplating whether the US government should be allowed to screen the most powerful AI models before they become available to the public, a significant shift from his previously laissez-faire approach to the AI industry. In the most recent story about White ...
The Affiliate Illusion: What AI Buyers Should Learn From the Marketing Machines Behind Today’s “Breakthrough” Tools
An analysis of how affiliate-driven marketing shapes AI product quality, sustainability, and hype—plus what buyers should evaluate before subscribing to AI tools.
A practitioner's argument that meeting summarizers fail in the same way regressions fail when you skip the part where you ask what the data can support.
The post LLM Summarizers Skip the Identification Step appeared first on Towards Data Science.
NVIDIA AI Just Released cuda-oxide: An Experimental Rust-to-CUDA Compiler Backend that Compiles SIMT GPU Kernels Directly to PTX
NVlabs releases cuda-oxide v0.1.0, a custom rustc codegen backend that compiles #[kernel]-annotated Rust functions to PTX through a Rust → Stable MIR → Pliron IR → LLVM IR → PTX pipeline, with single-source host+device compilation from one cargo oxide build command.
The post NVIDIA AI Just Released ...
AI Weekly Issue #491: 100 years from now : The Last Election
This is 100 Years From Now. Once a week we skip a century and try to picture what life actually looks like when the stuff we're building now has had time to settle in. This week: the last vote.
NVIDIA AI Releases Star Elastic: One Checkpoint that Contains 30B, 23B, and 12B Reasoning Models with Zero-Shot Slicing
NVIDIA researchers have introduced Star Elastic, a post-training method that embeds multiple nested reasoning models — at 30B, 23B, and 12B parameter scales — inside a single checkpoint, eliminating the need for separate training runs or stored model weights per variant. Built on the Nemotron Elasti...
From tokenisation to evaluation : how modern language models actually work in practice
The post The Must-Know Topics for an LLM Engineer appeared first on Towards Data Science.
RAG Is Blind to Time — I Built a Temporal Layer to Fix It in Production
Three weeks into testing, a learner told me my AI tutor gave her the wrong answer.
Not obviously wrong — just outdated enough to mislead.
That was the moment I realized something most RAG systems quietly ignore: they have no sense of time. My system retrieved the most similar document, not the most ...
Agent Memory Patterns in Cognitive Science and AI Systems
Memory shapes how humans think and how AI agents act. Without it, an agent only responds to the current input; with it, it can keep context, recall past actions, and reuse useful knowledge. AI memory spans short-term, episodic, semantic, and long-term memory, each with different design trade-offs ar...
Understanding Annotator Safety Policy with Interpretability
arXiv:2605.05329v1 Announce Type: new
Abstract: Safety policies define what constitutes safe and unsafe AI outputs, guiding data annotation and model development. However, annotation disagreement is pervasive and can stem from multiple sources such as operational failures (annotators misunderstand ...
Partial Evidence Bench: Benchmarking Authorization-Limited Evidence in Agentic Systems
arXiv:2605.05379v1 Announce Type: new
Abstract: Enterprise agents increasingly operate inside scoped retrieval systems, delegated workflows, and policy-constrained evidence environments. In these settings, access control can be enforced correctly while the system still produces an answer that appea...
BALAR : A Bayesian Agentic Loop for Active Reasoning
arXiv:2605.05386v1 Announce Type: new
Abstract: Large language models increasingly operate in interactive settings where solving a task requires multiple rounds of information exchange with a user. However, most current systems treat dialogue reactively and lack a principled mechanism to reason abo...
Intelligent CCTV for Urban Design: AI-Based Analysis of Soft Infrastructure at Intersections
arXiv:2605.05402v1 Announce Type: new
Abstract: Artificial intelligence (AI) and computer vision are transforming transportation data collection. This study introduces an AI-enabled analytics framework leveraging existing CCTV infrastructure to evaluate the impact of soft interventions, such as tem...
Meet GitHub Spec-Kit: An Open Source Toolkit for Spec-Driven Development with AI Coding Agents
If you have spent time using AI coding agents — GitHub Copilot, Claude Code, Gemini CLI — you have probably run into this situation: you describe what you want, the agent generates a block of code that looks correct, compiles, and then subtly misses the actual intent. This “vibe-coding” approach can...
Musk v. Altman week 2: OpenAI fires back, and Shivon Zilis reveals that Musk tried to poach Sam Altman
In the second week of the landmark trial between Elon Musk and OpenAI, Musk’s motivations for bringing the suit were under scrutiny. Last week, Musk took the stand, alleging that OpenAI CEO Sam Altman and president Greg Brockman had deceived him into donating $38 million to the company. He claimed t...
OpenAI Adds Chrome Extension to Codex, Letting Its AI Agent Access LinkedIn, Salesforce, Gmail, and Internal Tools via Signed-In Sessions
OpenAI has shipped a Chrome extension for Codex, its AI coding agent, enabling it to complete browser-based tasks directly inside Google Chrome on macOS and Windows — including interacting with signed-in websites, using Chrome DevTools, and running multi-step workflows across browser tabs.
The post ...
Building realistic electric transmission grid dataset at scale: a pipeline from open dataset
Microsoft Research is excited to release an open dataset of approximate transmission topology of the U.S. power grid derived from publicly available data. The ability to study transmission-level power grid behavior is essential for modern power systems research. Analyses of congestion, transmission ...