World Wire

Most open model launches release one checkpoint and a benchmark table. The Institute of Foundation Models (IFM) released something wider last week. IFM is the frontier lab launched by MBZUAI in May 2025. K2 Horizon is a fleet of six models: 375B-A23B, 36B-A4B, 32B, 7B, 3.7B and 0.9B. Shipping alongside them are the pre-training corpus, intermediate checkpoints, training code, configs and fine-grained logs. IFM calls it the largest fully open-source model launch in AI history. Is it deployable? Yes, all six sizes sit on Hugging Face under Apache 2.0 , with FP8 and GGUF builds. Day-zero support covers vLLM , SGLang and Ollama, on NVIDIA, AMD and Cerebras hardware. Hosted APIs run through Compass, Cerebras and Nebius via platform.ifm.ai . What Actually Shipped The six models share a core architecture, vocabulary, training methodology, interfaces and deployment tooling. The 0.9B model uses a smaller vocabulary. That consistency is the point: teams can prototype on 3.7B a...

AI research agents can already propose, implement and score their own machine learning experiments. Idea generation is cheap; verification is not. Training one candidate can consume hours to days of GPU time, so an agent proposes far more candidates than it can afford to run. Which ones get run is the real lever on research progress. A research team from FAIR at Meta, the University of Oxford and University College London formalizes that lever as research preference and introduces AI Research Preference Models (RPMs) . An RPM ranks unexecuted candidates and picks one to execute. It never forecasts an absolute score, the team found language models unreliable at predicting metrics or execution outcomes. Is it deployable? Partially. RPMs use frozen pretrained LLMs with no fine-tuning, the scaffold AIRA-dojo and benchmark AIRS-Bench are open source, and the backbone Qwen3.6-27B is open weights. Where the RPM sits in the agent loop AIRA-dojo is an evolutionary tree se...

A team of researchers from UC Berkeley have released CUA-Lite , an open platform for computer-use agents (CUAs). The argument behind it is infrastructural rather than model-centric: training and benchmarking a CUA requires four pieces: agents, environments, traces, and a framework to evaluate and train them and all four are currently fragmented across separate repositories with incompatible interfaces. CUA-Lite puts them behind one action space, one data schema, and one command, across desktop, browser and mobile. Is it deployable? Yes. The stack installs with uv sync --all-extras on Python 3.12, and its lightweight sandboxes run on any Docker host without /dev/kvm , so cloud instances, CI runners and nested containers all work. The VM tax, and how Lite.OSWorld removes it The most concrete contribution is Lite.OSWorld . OSWorld provides a faithful Ubuntu desktop, but it ships as a full QEMU/KVM virtual machine per task, requiring nested virtualization that most managed ...