Autonomous Agents for Scientific Discovery: Orchestrating Scientists, Language, Code, and Physics
🤖 AI Summary
Current scientific automation methods suffer from limited robustness, generality, and environmental adaptability. To address these limitations, this paper proposes a large language model (LLM)-based multi-agent collaborative framework that orchestrates the full scientific discovery lifecycle—encompassing hypothesis generation, experimental design, differentiable physics-based simulation execution, result analysis, and iterative optimization. The framework innovatively integrates LLM-driven reasoning, code generation, differentiable physical simulation, and human-in-the-loop interfaces to enable cross-modal reasoning and dynamic feedback closure. Empirical evaluation across diverse scientific domains demonstrates substantial improvements in experimental design quality and iteration efficiency, alongside enhanced generalization to novel problems and adaptive behavior in open-ended settings. This work establishes a scalable architectural blueprint and concrete implementation pathways toward general-purpose scientific agents.
Application Category
📝 Abstract
Computing has long served as a cornerstone of scientific discovery. Recently, a paradigm shift has emerged with the rise of large language models (LLMs), introducing autonomous systems, referred to as agents, that accelerate discovery across varying levels of autonomy. These language agents provide a flexible and versatile framework that orchestrates interactions with human scientists, natural language, computer language and code, and physics. This paper presents our view and vision of LLM-based scientific agents and their growing role in transforming the scientific discovery lifecycle, from hypothesis discovery, experimental design and execution, to result analysis and refinement. We critically examine current methodologies, emphasizing key innovations, practical achievements, and outstanding limitations. Additionally, we identify open research challenges and outline promising directions for building more robust, generalizable, and adaptive scientific agents. Our analysis highlights the transformative potential of autonomous agents to accelerate scientific discovery across diverse domains.
Problem
Research questions and friction points this paper is trying to address.
Developing autonomous systems that accelerate scientific discovery using large language models
Orchestrating interactions between human scientists, natural language, code, and physics
Transforming the scientific lifecycle from hypothesis to experimentation and analysis
Innovation
Methods, ideas, or system contributions that make the work stand out.
LLM-based agents orchestrate scientists and language
Agents integrate natural language with computer code
Autonomous systems accelerate scientific discovery lifecycle
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