Olmo 3.1

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.

MiniCPM5

OpenBMB

MiniCPM5 is OpenBMB’s fifth-generation dense language-model family for local and resource-constrained use. The 1.08B model was announced May 19, followed by a 2.52B model on September 7. Both support a documented 128K context and target reasoning, coding, tool use, writing, and instruction following. The training recipe combines high-quality data, instruction tuning, and complementary reinforcement-learning signals. Apache-licensed base, intermediate, supervised, and final checkpoints are grouped as variants, alongside routine deployment formats. Small parameter counts improve local practicality but do not eliminate errors or the need for application-specific evaluation.

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.