World Wire

OpenBMB has released MiniCPM5-2B , the second checkpoint in the MiniCPM5 series and the follow-up to MiniCPM5-1B . It is a dense causal language model with 2,516,756,480 parameters, of which 1,981,982,720 sit outside the embeddings. It uses 42 layers, grouped-query attention with 16 query heads and 2 key/value heads, and a native context window of 131,072 tokens. The architecture is standard LlamaForCausalLM , so mainstream engines load it with no custom kernels and no model-code fork. Is it deployable? Yes. The weights are Apache 2.0 and run through vLLM, SGLang, Transformers, llama.cpp, Ollama, LM Studio, MLX and FlagOS. What the benchmark table actually shows OpenBMB compares MiniCPM5-2B against LFM2.5-2.6B, Qwen3.5-2B and Gemma-4-E2B-it in the same size class, and lists Qwen3.5-4B, granite-4.2-3B, Nemotron-3-Nano-4B, Gemma-4-E4B-it and LFM2.5-8B-A1B for reference. Across 34 benchmark rows it averages 53.9. The best baseline in that set is Qwen3.5-4B at 51.1, then gra...

Robot manipulation datasets have grown far slower than the models trained on them, mostly because collection stays closed and centralized. Expert operators gather demonstrations on lab hardware, process them offline, and ship a fixed benchmark that never grows again. A research team from Axis Robotics , UC Berkeley, Georgia Tech, NTU… is proposing a different shape for the problem. Their system, AXIS , moves demonstration collection into the browser, sends everything else to backend GPUs, and treats the dataset as something that keeps expanding rather than something that ships once. Is it deployable? Partially. The training code is public as a patch layer over OpenPI , and the teleoperation platform is live in any browser. The dataset on Hugging Face is gated at 2.36 TB and restricted to non-commercial academic use. No policy checkpoints are released. The browser and backend split The core system decision is asymmetry. Contributors teleoperate a Franka Research 3 ...

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...