AI

AI companies use model specifications to define target behaviors during training and evaluation. Do current specs state the intended behaviors with enough precision, and do frontier models exhibit distinct behavioral profiles under the same spec? A team of researchers from Anthropic, Thinking Machines Lab and Constellation present a systematic method that stress tests model specs…

In this article we will analyze how Google, OpenAI, and Anthropic are productizing ‘agentic’ capabilities across computer-use control, tool/function calling, orchestration, governance, and enterprise packaging. Agent platforms, not only models, now define competitive advantage. Google is aligning Gemini 2.0 with an enterprise control plane on Vertex AI and a new ‘front door’ called Gemini Enterprise….

In this tutorial, we build an advanced computer-use agent from scratch that can reason, plan, and perform virtual actions using a local open-weight model. We create a miniature simulated desktop, equip it with a tool interface, and design an intelligent agent that can analyze its environment, decide on actions like clicking or typing, and execute…

Liquid AI released LFM2-VL-3B, a 3B parameter vision language model for image text to text tasks. It extends the LFM2-VL family beyond the 450M and 1.6B variants. The model targets higher accuracy while preserving the speed profile of the LFM2 architecture. It is available on LEAP and Hugging Face under the LFM Open License v1.0….

In this tutorial, we explore LitServe, a lightweight and powerful serving framework that allows us to deploy machine learning models as APIs with minimal effort. We build and test multiple endpoints that demonstrate real-world functionalities such as text generation, batching, streaming, multi-task processing, and caching, all running locally without relying on external APIs. By the…

A team of Salesforce AI researchers introduced WALT (Web Agents that Learn Tools), a framework that reverse-engineers latent website functionality into reusable invocable tools. It reframes browser automation around callable tools rather than long chains of clicks. Agents then call operations such as search, filter, sort, post_comment, and create_listing. This reduces dependence on large language…

Open vocabulary object detectors answer text queries with boxes. In remote sensing, zero shot performance drops because classes are fine grained and visual context is unusual. Google Research team proposess FLAME, a one step active learning strategy that rides on a strong open vocabulary detector and adds a tiny refiner that you can train in…

Computer-use agents have been limited to primitives. They click, they type, they scroll. Long action chains amplify grounding errors and waste steps. Apple Researchers introduce UltraCUA, a foundation model that builds an hybrid action space that lets an agent interleave low level GUI actions with high level programmatic tool calls. The model chooses the cheaper…

In this tutorial, we explore how to build a small yet functional multi-agent system using the uAgents framework. We set up three agents — Directory, Seller, and Buyer — that communicate via well-defined message protocols to simulate a real-world marketplace interaction. We design message schemas, define agent behaviors, and implement request-response cycles to demonstrate discovery,…

Anthrogen has introduced Odyssey, a family of protein language models for sequence and structure generation, protein editing, and conditional design. The production models range from 1.2B to 102B parameters. The Anthrogen’s research team positions Odyssey as a frontier, multimodal model for real protein design workloads, and notes that an API is in early access. https://www.biorxiv.org/content/10.1101/2025.10.15.682677v1.full.pdf…