Introduction

We present UniScientist, an agentic large language model featuring 30 billion total parameters, with only 3 billion activated per token. Developed by UniPat AI, the model is specifically designed for universal scientific research tasks spanning 50+ disciplines. UniScientist achieves state-of-the-art performance across a range of research benchmarks, including FrontierScience-Research, FrontierScience-Olympiad, DeepResearch Bench, DeepResearch Bench II, and ResearchRubrics.

More details can be found in our Blog.

Key Features

• Evolving Polymathic Synthesis: A human-LLM collaborative data paradigm that generates research-grade scientific problems across 50+ disciplines, each accompanied by co-evolved rubrics refined through completeness, consistency, and distinguishability checks.
• Agentic Research Loop: The model conducts scientific research by iteratively acquiring evidence, deriving formally-justified results, and updating hypotheses via abductive inference, using tools including web_search, google_scholar, page_fetching, and code_interpreter.
• Report Aggregation: Given multiple candidate research reports, the model learns to synthesize a consolidated report integrating the best elements, enabling research quality to self-evolve over time.

Download

You can download the model then run the inference scripts in https://github.com/UniPat-AI/UniScientist.

@misc{unipat2026uniscientist,
  title   = {UniScientist: Advancing Universal Scientific Research Intelligence},
  author  = {UniPat AI Team},
  year    = {2026},
  howpublished = {\url{https://github.com/UniPat-AI/UniScientist}}
}