# URSA: The Universal Research and Scientific Agent

arXiv: https://arxiv.org/abs/2506.22653 · HTML: https://mlsanity.com/paper/2506.22653/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- Placed in Web, search, and research agents, one of 16 directions in LLM agents and tool use.
- Builds on 13 of its 29 references that are in the map.
- First posted on arXiv 27 Jun 2025.

## Placed in

- LLM agents and tool use / [Web, search, and research agents](https://mlsanity.com/agents/web-search-and-research-agents/)

## Builds on (most cited first, 13 of 13)

- [The AI Scientist: Towards Fully Automated Open-Ended Scientific Discovery](https://mlsanity.com/paper/2408.06292/) (2024)
- [Agent Laboratory: Using LLM Agents as Research Assistants](https://mlsanity.com/paper/2501.04227/) (2025)
- [The AI Scientist-v2: Workshop-Level Automated Scientific Discovery via Agentic Tree Search](https://mlsanity.com/paper/2504.08066/) (2025)
- [Accelerating scientific discovery with Co-Scientist](https://mlsanity.com/paper/2502.18864/) (2025)
- [Large Language Models to Enhance Bayesian Optimization](https://mlsanity.com/paper/2402.03921/) (2024)
- [Towards Scientific Intelligence: A Survey of LLM-based Scientific Agents](https://mlsanity.com/paper/2503.24047/) (2025)
- [SciAgents: Automating scientific discovery through multi-agent intelligent graph reasoning](https://mlsanity.com/paper/2409.05556/) (2024)
- [Agentic AI for Scientific Discovery: A Survey of Progress, Challenges, and Future Directions](https://mlsanity.com/paper/2503.08979/) (2025)
- [Generative to Agentic AI: Survey, Conceptualization, and Challenges](https://mlsanity.com/paper/2504.18875/) (2025)
- [Agent AI with LangGraph: A Modular Framework for Enhancing Machine Translation Using Large Language Models](https://mlsanity.com/paper/2412.03801/) (2024)
- [Aviary: training language agents on challenging scientific tasks](https://mlsanity.com/paper/2412.21154/) (2024)
- [Strategic Chain-of-Thought: Guiding Accurate Reasoning in LLMs through Strategy Elicitation](https://mlsanity.com/paper/2409.03271/) (2024)
- [Evaluating the Performance and Robustness of LLMs in Materials Science Q&A and Property Predictions](https://mlsanity.com/paper/2409.14572/) (2024)

## Built on by (most cited first, 6 of 6)

- [PDE foundation model-accelerated inverse estimation of system parameters in inertial confinement fusion](https://mlsanity.com/paper/2603.04606/) (2026)
- [Multi-Agent Collaboration for Automated Design Exploration on High Performance Computing Systems](https://mlsanity.com/paper/2603.11515/) (2026)
- [Chain-based Adaptive Reconfiguration Over Lattices for Hallucination Reduction](https://mlsanity.com/paper/2605.27706/) (2026)
- [Experiments in Agentic AI for Science](https://mlsanity.com/paper/2605.26305/) (2026)
- [Hierarchical Prompt-Domain Control and Learning for Resource-Constrained Agentic Language Models](https://mlsanity.com/paper/2605.27703/) (2026)
- [When Scientific Cognition Is No Longer Scarce](https://mlsanity.com/paper/2610.10241/) (2026)

## Abstract (arXiv)

Large language models (LLMs) have moved far beyond their initial form as simple chatbots, now carrying out complex reasoning, planning, writing, coding, and research tasks. These skills overlap significantly with those that human scientists use day-to-day to solve complex problems that drive the cutting edge of research. Using LLMs in quotesagentic AI has the potential to revolutionize modern science and remove bottlenecks to progress. In this work, we present URSA, a scientific agent ecosystem for accelerating research tasks. URSA consists of a set of modular agents and tools, including coupling to advanced physics simulation codes, that can be combined to address scientific problems of varied complexity and impact. This work highlights the architecture of URSA, as well as examples that highlight the potential of the system.