mlsanityData to 14 Oct 2026

URSA: The Universal Research and Scientific Agent

arXiv 2506.22653 ↗First posted 27 Jun 2025cs.AIcited by 6

On the map: LLM agents and tool use → Web, search, and research agents

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.

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.

Builds on

13 of 29 references.

Closest ancestors · 6

Same direction, newest first

  1. 2025The AI Scientist-v2: Workshop-Level Automated Scientific Discovery via Agentic Tree Search10 Apr
  2. 2025Towards Scientific Intelligence: A Survey of LLM-based Scientific Agents31 Mar
  3. 2025Agentic AI for Scientific Discovery: A Survey of Progress, Challenges, and Future Directions12 Mar
  4. 2025Agent Laboratory: Using LLM Agents as Research Assistants8 Jan
  5. 2024Aviary: training language agents on challenging scientific tasks30 Dec
  6. 2024SciAgents: Automating scientific discovery through multi-agent intelligent graph reasoning9 Sep

Other references · 7

  1. 2024The AI Scientist: Towards Fully Automated Open-Ended Scientific Discoverycited by 1,071
  2. 2025Accelerating scientific discovery with Co-Scientistcited by 401
  3. 2024Large Language Models to Enhance Bayesian Optimizationcited by 135
  4. 2025Generative to Agentic AI: Survey, Conceptualization, and Challengescited by 33
  5. 2024Agent AI with LangGraph: A Modular Framework for Enhancing Machine Translation Using Large Language Modelscited by 28
  6. 2024Strategic Chain-of-Thought: Guiding Accurate Reasoning in LLMs through Strategy Elicitationcited by 19
  7. 2024Evaluating the Performance and Robustness of LLMs in Materials Science Q&A and Property Predictionscited by 9

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2026: 6.

6 papers.

Similar, not cited

Close in wording, no citation either way

  1. 2025Autonomous Agents for Scientific Discovery: Orchestrating Scientists, Language, Code, and Physics
  2. 2025ScienceBoard: Evaluating Multimodal Autonomous Agents in Realistic Scientific Workflows
  3. 2025TusoAI: Agentic Optimization for Scientific Methods
  4. 2024Many Heads Are Better Than One: Improved Scientific Idea Generation by A LLM-Based Multi-Agent System
  5. 2025SciSciGPT: Advancing Human-AI Collaboration in the Science of Science
  6. 2025Agent4S: The Transformation of Research Paradigms from the Perspective of Large Language Models
  7. 2026SciResearcher: Scaling Deep Research Agents for Frontier Scientific Reasoning
  8. 2026BloClaw: An Omniscient, Multi-Modal Agentic Workspace for Next-Generation Scientific Discovery