HPE AI Factory With NVIDIA Expands for the Era of Agents
Enterprises are moving agentic AI from proof of concept to production — and the next generation of AI factories are built for the era of agents. At HPE Discover Las Vegas, running through Thursday, June 18, NVIDIA and HPE are expanding the HPE AI Factory with NVIDIA, including NVIDIA Vera CPU and NV...
Fastest, Largest, Strongest: NVIDIA Blackwell Sweeps MLPerf Training 6.0
Every breakthrough AI model starts the same way: with a training run. The infrastructure running those training jobs shapes everything: how fast teams can iterate, what scale of model they can build and whether those jobs complete reliably. As models grow in size, complexity and intelligence, the d...
Tired of your monthly API bill? Follow this tested guide to set up a high-performance local LLM on your Mac Mini without the headaches.
The post Run a Local LLM with OpenClaw on Your Mac Mini appeared first on Towards Data Science.
LLM Fallbacks Break Agent Pipelines — I Built the Missing Recovery Layer
LLM rate limits don't just interrupt agent pipelines—they can silently corrupt structured outputs when fallback models receive incompatible payloads. I built a recovery layer that classifies failures, adapts payloads across model tiers, preserves execution state, and maintains schema integrity durin...
Building an End-to-End Sentiment Analysis Pipeline with Scikit-LLM
Traditional machine learning pipelines for predictive tasks like text classification usually rely on extracting structured, numerical features from raw text — for instance, TF-IDF frequencies or token embeddings — to feed into classical models such as logistic regression, ensembles, or support vecto...
Many AI agent systems become economically unsustainable long before they become technically impressive. Teams usually focus on model choice, prompt design, tool calling, and orchestration. Those things matter, but they are only part of the system setup. The deeper issue is that coding agents, such a...
Hermes Agent Adds Asynchronous Subagents, So Delegated Work No Longer Blocks the Parent Chat
We look at Hermes Agent's new asynchronous subagents from Nous Research. The delegate tool can now spawn background agents that no longer block the parent chat. We walk through the async_delegation toolset tracked in issue #5586, covering how to spawn, check, steer, and collect tasks. We also cover ...
Want to get a data center online quickly? Give it some flex.
At the end of a tense and scoreless first half of a soccer match between the English men’s team and rival Germany, millions of Brits let out a collective sigh and did what they so often do in moments of stress: They made tea. That wave of electric kettles clicking on, however, caused a different…
Meet Atoms: A Vibe Coding Tool That Uses AI Agents to Build, Deploy, and Market Your App (No Code)
The concept of vibe coding is interesting; you don’t need to be a developer or software engineer to build your own applications. You can describe your idea to an AI in plain language, and it will build, edit, and refine your applications so you don’t have to write code line by line. It sounds simple...
Google Cloud Introduces Open Knowledge Format (OKF): A Vendor-Neutral Markdown Spec for Giving AI Agents Curated Context
We break down Google Cloud's new Open Knowledge Format (OKF), an open spec that formalizes the LLM-wiki pattern. We explain how a bundle works: a directory of markdown files with YAML frontmatter, where each concept needs only a type field. We cover the three design principles, the reference tools G...
Autoregressive Models: Predicting the Future Using the Past
Autoregressive models are one of the most important ideas in time series forecasting and sequence modeling. The name may sound technical at first, but the concept is surprisingly intuitive. An autoregressive model predicts the next value by looking at previous values. That is the core idea. For exam...
QPILOTS: Efficient Test-Time Q-Steering for Flow Policies
arXiv:2606.14801v1 Announce Type: new
Abstract: Flow-matching and diffusion policies are expressive action generators, but optimizing them with temporal-difference reinforcement learning (RL) remains difficult. Effective policy extraction requires exploiting the critic's action gradient, yet direct...
GRASP: Gradient-Aligned Sequential Parameter Transfer for Memory-Efficient Multi-Source Learning
arXiv:2606.14900v1 Announce Type: new
Abstract: Multi-source transfer learning faces a fundamental scalability bottleneck: existing approaches require either loading all K source models into memory simultaneously during parameter fusion, requiring O(K) memory, or deploying all models at inference t...
Policy Regret for Embedding Model Routing: Contextual Bandits with Low-Rank Experts
arXiv:2606.14929v1 Announce Type: new
Abstract: Modern recommendation systems increasingly rely on dynamically routing diverse queries to multiple embedding models. Despite its practical significance, this problem remains poorly understood under realistic conditions like adversarial queries, bandit...
A Definition of Good Explanations and the Challenges Explaining LLM Outputs
arXiv:2606.14838v1 Announce Type: new
Abstract: How to define a good explanation is a long-standing philosophical debate which has found recent renewed interest in the context of AI outputs. Explainability is crucial for AI adoption in many contexts, but in order to produce good explanations of AI ...
Dr-DCI: Scaling Direct Corpus Interaction via Dynamic Workspace Expansion
arXiv:2606.14885v1 Announce Type: new
Abstract: Agentic search over large corpora relies on retriever-mediated interfaces (e.g., BM25 or ColBERT) for scalable candidate discovery. While effective at ranking relevant documents, these interfaces expose evidence only as ranked results or bounded docum...
arXiv:2606.14892v1 Announce Type: new
Abstract: An artificial intelligence must have a model of its environment that is causal, supporting reasoning about interventions and counterfactuals, and also combinatorial, supporting generalization to unseen combinations of objects. In this work, we formall...
Trust Between AI Agents: Measuring Formation, Breakage, and Recovery, with Implications for Governing Multi-Agent Systems
arXiv:2606.14923v1 Announce Type: new
Abstract: As language-model agents increasingly work in teams, each agent must decide how much to trust its teammates. Yet we lack a standard way to measure trust between AI agents. We propose a behavioral measure based on costly verification. In a cooperative ...
PrologMCP: A Standardized Prolog Tool Interface for LLM Agents
arXiv:2606.14935v1 Announce Type: new
Abstract: Frontier reasoning-tuned language models still fail on deductive tasks at depth, and the cost of improved performance through extended internal reasoning scales poorly. Symbolic delegation offers a complementary route: a language model translates the ...