BAR

AstaBrief

Allen Institute for AI

AstaBrief is Ai2’s 8B scientific report-generation model, trained from Qwen3-8B with citation-focused supervised fine-tuning and direct preference optimization. It turns a research question and retrieved literature excerpts into a complete cited report in a single pass, and powers Asta’s Fast report mode. Ai2 releases the weights, training data, and a workflow for using local PDFs. The reported latency advantage evaluates the complete report pipeline rather than the checkpoint alone. Citation grounding is improved but does not guarantee that a synthesis preserves every study’s scope or conclusions.

EMO

Allen Institute for AI

EMO is Ai2’s open mixture-of-experts language model trained so reusable domain modules emerge without predefined semantic labels. Its 128 routed experts activate eight per token, giving 14B total and about 1B active parameters. A document-level routing objective encourages coherent expert groups that can be selected for particular tasks. The main checkpoint is trained on one trillion tokens with additional annealing. Ai2 releases weights, matched baselines, and training code for modularity research. Results for reduced expert subsets depend on the selection method and evaluation task.

MolmoWeb

Allen Institute for AI

MolmoWeb is a released vision-language model trained for browser tasks from screenshots and natural-language instructions. Its 4B and 8B weights are distinct from the browser harness: the checkpoints generate textual reasoning, browser actions, and task-completion responses. Training uses MolmoWebMix, an open collection of synthetic and human browsing trajectories. Native and Transformers-compatible checkpoints are serving variants; compatibility fixes after launch do not establish a new model generation. Completing a web task depends on the external browser, observation/action interface, and execution permissions.