AI Tools & Frameworks Directory

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 ...

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…

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...

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 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...

Respond.io, one of Malaysia startups to watch, uses AI agents to handle high volumes of customer inquiries and charges per convo, not per seat.

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...

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...

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...

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 ...

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...

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 ...

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 ...

OpenAI introduces Deployment Simulation, a method to predict AI model behavior before deployment using real conversation data to improve safety and evaluation accuracy.

The Trump administration's decision that forced Anthropic to pull its latest cybersecurity models could be reactionary, retaliatory, or both, but the message is clear: The AI industry isn't immune from U.S. government interference.

Meta announced Monday that it's rolling out a wave of new AI features on Facebook, the latest sign of the company's effort to catch up in the AI race and keep users more engaged on the platform.

A group made up of dozens of cybersecurity experts urged the White House to remove export control restrictions on Anthropic’s models Fable and Mythos, arguing that the order is going to limit the ability of cybersecurity defenders to secure their software and products.

A detailed look at MCP that turned my scattered tool definitions into a stable, discoverable server The post The Protocol That Cleaned Up Our Agent Architecture appeared first on Towards Data Science.

Salesforce says it wants to use Fin's team and technology to improve Agentforce, its existing enterprise platform that businesses can use to build custom AI agents that automate tasks.

In this article, we’ll build time-series machine learning models in Python using sktime and explore its core data structures for forecasting workflows.

A single model hands you a single answer and no sense of how much it hinges on the dozens of choices buried inside it. The post I Built 11 Models to Predict the 2026 World Cup. They Crown Four Different Champions. appeared first on Towards Data Science.

NewCore argues the next challenge in enterprise security will be managing AI agents, not people.

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