A survey on large language model based autonomous agents

Abstract Autonomous agents have long been a research focus in academic and industry communities. Finally, we delve into the evaluation strategies commonly used for LLM-based autonomous agents.

⚡ This is an original paraphrased summary — not copied from the abstract. Full paper available at the source link below.

• 1 Previous research often focuses on training agents with limited knowledge within isolated environments, which diverges significantly from human learning processes, and makes the agents hard to achieve human-like decisions.
• 2 Recently, through the acquisition of vast amounts of Web knowledge, large language models (LLMs) have shown potential in human-level intelligence, leading to a surge in research on LLM-based autonomous agents.
• 3 In this paper, we present a comprehensive survey of these studies, delivering a systematic review of LLM-based autonomous agents from a holistic perspective.

This research advances how AI systems learn, reason, and solve problems — with direct implications for automation and scientific discovery.

This summary is based on publicly available metadata and abstract. For the full research paper, visit the original source: