---
license: apache-2.0
language:
- en
pipeline_tag: text-generation
library_name: transformers
---

# 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](https://unipat.ai/blog/UniScientist).

## 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.

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