DR Tulu

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.

BAR

Allen Institute for AI

BAR, or Branch-Adapt-Route, is Ai2’s released modular language-model family built from an Olmo 2 7B anchor and independently trained capability experts. Math, coding, tool-use, and safety experts are combined through a router into a final five-expert mixture, enabling capability updates without retraining the full model. The public checkpoints include the original anchor, intermediate two-expert combinations, and final combined model as variants. This is a concrete weight release supporting a post-training method, rather than a framework-only announcement. Expert merging and routing must be evaluated for interactions and task quality.