URSA: The Universal Research and Scientific Agent

2025-06-27

AI Summary

Modern scientific research faces bottlenecks including task fragmentation, tool heterogeneity, and low automation. Method: This paper introduces URSA—a Universal Research Scientific Agent ecosystem—featuring a composable, modular agent architecture that tightly integrates large language models’ (LLMs) reasoning and planning capabilities with domain-specific physical simulation toolchains via standardized scientific computing interfaces, enabling seamless LLM–numerical code co-execution. Contribution/Results: URSA’s core innovation is end-to-end closed-loop automation across scientific workflows: hypothesis generation, experimental design, parameter optimization, automated simulation scheduling, and result interpretation—spanning diverse domains. Empirical evaluation on representative tasks—including materials modeling, introductory fluid dynamics, and circuit analysis—demonstrates substantial improvements in research process automation and execution efficiency. URSA establishes a novel paradigm for next-generation, scalable, and reusable scientific AI infrastructure.

Technology Category

• Search and Optimization: Sampling/Simulation-based Search
• Cognitive Modeling & Cognitive Systems: Agent Architectures
• Multiagent Systems: Agent-Based Simulation and Emergent Behavior

Application Category

• Search and Retrieval-Augmented AI: Agentic search
• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions
• User Modeling, Personalization and Recommendation: User modeling and simulation for interactive and conversational systems

Abstract

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 "agentic" 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.

Problem

• Developing URSA to accelerate complex scientific research tasks
• Integrating LLMs with modular agents and simulation tools
• Overcoming research bottlenecks using agentic AI systems

Innovation

• Modular agents for scientific research tasks
• Integration with advanced physics simulations
• Combining tools for varied complexity problems