A survey on large language model based autonomous agents

Autonomous agents have long been a research focus in academic and industry communities.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.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.In this paper,we present a comprehensive survey of these studies,delivering a systematic review of LLM-based autonomous agents from a holistic perspective.We first discuss the construction of LLM-based autonomous agents,proposing a unified framework that encompasses much of previous work.Then,we present a overview of the diverse applications of LLM-based autonomous agents in social science,natural science,and engineering.Finally,we delve into the evaluation strategies commonly used for LLM-based autonomous agents.Based on the previous studies,we also present several challenges and future directions in this field.

Index-free triangle-based graph local clustering

Motif-based graph local clustering(MGLC)is a popular method for graph mining tasks due to its various applications.However,the traditional two-phase approach of precomputing motif weights before performing local clustering loses locality and is impractical for large graphs.While some attempts have been made to address the efficiency bottleneck,there is still no applicable algorithm for large scale graphs with billions of edges.In this paper,we propose a purely local and index-free method called Index-free Triangle-based Graph Local Clustering(TGLC^(*))to solve the MGLC problem w.r.t.a triangle.TGLC^(*)directly estimates the Personalized PageRank(PPR)vector using random walks with the desired triangleweighted distribution and proposes the clustering result using a standard sweep procedure.We demonstrate TGLC^(*)’s scalability through theoretical analysis and its practical benefits through a novel visualization layout.TGLC^(*)is the first algorithm to solve the MGLC problem without precomputing the motif weight.Extensive experiments on seven real-world large-scale datasets show that TGLC^(*)is applicable and scalable for large graphs.