From AI for Science to Agentic Science: A Survey on Autonomous Scientific Discovery

Key points

• Problem: Research on AI agents that carry out science on their own has grown fast, but earlier surveys each covered only one view: the research process, the level of autonomy, or the agent architecture. The authors argue that a unified framework for understanding and designing increasingly autonomous scientific systems is still missing.
• Scope: This paper is a survey that classifies existing work. It splits the role of AI into four levels (computational oracle, automated research assistant, autonomous scientific partner, and a future generative architect), groups Levels 2 and 3 as Agentic Science, and reviews agent systems in life sciences, chemistry, materials science, and physics.
• Taxonomy: The authors split the base abilities of a scientific agent into five: planning and reasoning, tool use, memory, collaboration between agents, and optimization and evolution; they tabulate the main methods for each and then discuss the difficulties specific to science. They treat discovery as a four-stage workflow (observation and hypothesis generation, experimental planning and execution, data and result analysis, and synthesis, validation, and evolution), and the per-domain tables mark which stages each system covers and whether it sits at Level 2 or Level 3.
• Evidence: The authors collect validated discoveries reported by the original papers in per-domain tables. They note that The Virtual Lab designed 92 nanobodies (small antibody fragments) and two of them bound recent variants of SARS-CoV-2 (the virus behind COVID-19) better in experiments. They also note that a cloud-based AI planner ran a workflow across five laboratories and found 21 new state-of-the-art organic solid-state laser emitters.
• Repository: The official repository linked on the first page, AgenticScience/Awesome-Agent-Scientists, is a paper list. Its README gathers links to related papers under Part 0 (fully autonomous research pipeline) and Parts 1 to 4 (life sciences, chemistry, materials science, and physics and astronomy).
• Limits: The review covers four natural-science domains, and Table 7 states that it lists only the most significant features of each paper. The authors cite state-of-the-art benchmarks showing that base LLMs turn conceptual plans into correct executable code with execution accuracy as low as 39%. They also note that judging AI-generated hypotheses still relies on costly and subjective human expert judgment.

Paper links

External research summaries. These are not HDATF publications or measured product results.