Agent-as-a-judge: Evaluate agents with agents

M Zhuge, C Zhao, D Ashley, W Wang… - arxiv preprint arxiv …, 2024 - arxiv.org
Contemporary evaluation techniques are inadequate for agentic systems. These
approaches either focus exclusively on final outcomes--ignoring the step-by-step nature of …

The emerged security and privacy of llm agent: A survey with case studies

F He, T Zhu, D Ye, B Liu, W Zhou, PS Yu - arxiv preprint arxiv:2407.19354, 2024 - arxiv.org
Inspired by the rapid development of Large Language Models (LLMs), LLM agents have
evolved to perform complex tasks. LLM agents are now extensively applied across various …

Two heads are better than one: A multi-agent system has the potential to improve scientific idea generation

H Su, R Chen, S Tang, X Zheng, J Li, Z Yin… - arxiv preprint arxiv …, 2024 - arxiv.org
The rapid advancement of scientific progress requires innovative tools that can accelerate
discovery. While recent AI methods, particularly large language models (LLMs), have shown …

LLM-Based Multi-Agent Systems for Software Engineering: Literature Review, Vision and the Road Ahead

J He, C Treude, D Lo - ACM Transactions on Software Engineering and …, 2025 - dl.acm.org
Integrating Large Language Models (LLMs) into autonomous agents marks a significant shift
in the research landscape by offering cognitive abilities that are competitive with human …

Autonomous agents for collaborative task under information asymmetry

W Liu, C Wang, Y Wang, Z **e, R Qiu, Y Dang… - arxiv preprint arxiv …, 2024 - arxiv.org
Large Language Model Multi-Agent Systems (LLM-MAS) have achieved great progress in
solving complex tasks. It performs communication among agents within the system to …

FinVision: A Multi-Agent Framework for Stock Market Prediction

S Fatemi, Y Hu - Proceedings of the 5th ACM International Conference …, 2024 - dl.acm.org
Financial trading has been a challenging task, as it requires the integration of vast amounts
of data from various modalities. Traditional deep learning and reinforcement learning …

Do as We Do, Not as You Think: the Conformity of Large Language Models

Z Weng, G Chen, W Wang - arxiv preprint arxiv:2501.13381, 2025 - arxiv.org
Recent advancements in large language models (LLMs) revolutionize the field of intelligent
agents, enabling collaborative multi-agent systems capable of tackling complex problems …

A Survey on Multi-Generative Agent System: Recent Advances and New Frontiers

S Chen, Y Liu, W Han, W Zhang, T Liu - arxiv preprint arxiv:2412.17481, 2024 - arxiv.org
Multi-generative agent systems (MGASs) have become a research hotspot since the rise of
large language models (LLMs). However, with the continuous influx of new related works …