How can LLMs build scientific research agents?
Towards Scientific Intelligence: A Survey of LLM-based Scientific Agents
This paper surveys the emerging field of LLM-based scientific agents, specialized AI systems designed to automate and accelerate scientific discovery. Unlike general-purpose LLMs, these agents integrate domain-specific knowledge, scientific tools, and robust validation mechanisms.
Key points for LLM-based multi-agent systems include: specialized agent architectures encompassing planners (prompt-based, SFT, RL, process supervision), memory (historical context, external KBs, intrinsic knowledge), and toolsets (APIs, simulators); the distinction and advantages of scientific agents over general-purpose agents (structured workflow, persistent memory, specialized tools, rigorous validation); benchmark datasets for evaluating agent performance in both general reasoning and scientific tasks; real-world applications spanning diverse scientific domains (chemistry, biology, physics, astronomy); and ethical considerations regarding agency, transparency, hallucinations, security, bias, and accountability. The paper highlights the transformative potential of these agents while acknowledging ongoing challenges and suggesting future research directions.