Ai2 Releases an Open Model for Faster Scientific Reports With Traceable Sources

Researchers can now download the model behind Asta’s faster scientific reports rather than rely solely on its hosted service. Ai2 released AstaBrief 8B on October 2, 2026, alongside its training data. It turns a research question and retrieved literature excerpts into a cited report, with an example workflow for adapting it to researchers’ own PDFs.

Ai2 wanted preliminary reports that scientists could get quickly and refine in later turns. Its existing Claude-powered workflow first summarized and grouped retrieved excerpts, then wrote the answer section by section. The team tested whether a smaller, specialized model could preserve report quality while cutting generation time and serving costs.

Starting with Qwen3-8B, Ai2 chose training from example reports and preferred answers instead of a reinforcement-learning approach. It described that choice as cheaper, easier to debug and more manageable. After filtering user logs for relevance, quality and privacy, the team had 90,000 research-focused queries to draw from.

• Example-report training: Ai2 retained 47,000 reports after quality filtering. The reports came from a pipeline using Claude 3.5 Sonnet, Claude 3.7 Sonnet, o3, o4-mini and GPT-4.1.
• Preference training: A separate query subset produced about 6,000 report pairs. Ai2 kept pairs only when GPT-4.1 and DeepSeek-R1 agreed on which report was better.

Ai2 reports 95% agreement between its automated judges and human preferences. Using multiple report generators and requiring both judges to agree was intended to reduce noise without treating any single model’s answers or judgments as ground truth.

Early training improved content quality but still fell short on relevance and citation grounding. Ai2’s strongest filtering gains came from removing training reports with too few cited statements. More aggressive filters and combinations did not add meaningful gains: consistent attribution in the examples mattered more than a complicated filtering recipe.

The other filters checked whether reports generated too much text from too little evidence, relied on lower-ranked papers, or drew on too few retrieved papers. Those tests separated citation coverage from the relevance and breadth of the sources used.

51.1 secondsAsta Fast mode

Ai2 reports this average across the full Fast-mode pipeline.

178.5 secondsClaude-powered Thinking mode

Ai2’s comparison measures complete workflows, not isolated model generation speed.

The speed figures need a time boundary. Ai2 says most training and evaluation finished in 2025, and it has not rerun the full evaluation against today’s frontier models. The results therefore describe the training and system design it tested, not a current competitive ranking.

AstaBrief now powers Fast mode in Asta’s Generate a report feature, alongside Claude-powered Thinking mode. Given the question and relevant excerpts, it writes the complete report in one pass. That skips the summarization and grouping stages of Thinking mode; literature retrieval still supplies the material the model synthesizes.

The open weights also give institutions a route to run report generation on their own infrastructure. Ai2 points to research questions that reveal sensitive or unpublished work as a reason for local deployment. Its example workflow provides a starting point for generating reports from local PDFs.

Ai2’s main development test used 200 user-written computer science questions. It separately measured necessary content, paragraph relevance, whether citations supported attached claims, and whether claims had citation support.

Secondary checks included DeepScholarBench, a 63-question research-synthesis benchmark built from recent ArXiv papers, and comparisons with Claude-powered reports. A small human study covered 14 questions from three scientific researchers, who ranked reports for preference, completeness, relevance, organization and citation accuracy.

But Ai2 identifies a harder problem: a report can cite the right study and still overstate its findings. It might generalize beyond the study’s sample or turn an observation into a recommendation. Its development metrics focused mainly on coverage, relevance and citation grounding. Ai2 says richer evaluation should also test whether reports preserve the scope and strength of source claims.