ChatDev 2.0, also called DevAll, is a no-code platform that lets users build, run, and inspect heterogeneous multi‑agent systems (MAS) powered by large language models. It combines a declarative executable graph abstraction with a cycle‑aware execution engine, enabling representation and execution of dynamic, cyclic interactions among diverse agents. The integrated visual interface allows users to author, monitor, and inspect MAS—including human‑in‑the‑loop steps—without writing code, and experiments show it matches state‑of‑the‑art MAS performance across three tasks.
By Yufan Dang, Shu Yao, Bowen Lai, Chenting Xu, Ruijie Shi, Wai-Shing Leung, Huatao Li, Chen Qian, Zhiyuan Liu
The article presents design principles and a software architecture, AGIMUD, for enabling interaction between humans and multiple agents in dynamic simulated worlds. It integrates socially-aware reasoning, emotional agent behavior, a multimodal human interface, and distributed AI processing to support real‑time, multi‑user dungeon (MUD) environments. The work builds on current AI/AGI and transformer‑based conversational agents to create sustainable, governance‑aware human‑agent reasoning systems.
By David Berga
arXiv:2606. 07594v1 Announce Type: new Abstract: Personal AI agents must increasingly operate across APIs, shells, web surfaces, and desktop GUIs, yet many systems remain tuned to a single interface and offer limited support for user teaching and auditability.
By Bo Zhang, Borui Zhang, Chenghao Jiang, Minglei Shi, Xiaofeng Wang, Zheng Zhu, Jie Zhou, Jiwen Lu
arXiv:2605. 25160v2 Announce Type: replace Abstract: GUI agents powered by large language models are advancing rapidly, creating urgent needs for evaluation and training based on realistic environments.
By Guohong Liu, Jialei Ye, Pengzhi Gao, Wei Liu, Jian Luan, Yunxin Liu, Yuanchun Li
arXiv:2507. 07445v3 Announce Type: replace Abstract: Autonomous agents navigating human society must master both production activities and social interactions, yet existing benchmarks rarely evaluate these skills simultaneously.
By Weihao Tan, Changjiu Jiang, Yu Duan, Mingcong Lei, Jiageng Li, Yitian Hong, Xinrun Wang, Bo An
arXiv:2607. 25076v1 Announce Type: new Abstract: Every major wave of platform software follows the same arc: an initial period of experimentation with competing frameworks and ad-hoc implementations, followed by the articulation of a small set of stable abstractions with well-defined semantics, and finally consolidation around those abstractions into a platform that applications can portably target.
By Gosia Steinder, Hubertus Franke
arXiv:2607. 05174v1 Announce Type: new Abstract: Language agents, i.
By Zhiheng Xi, Dingwen Yang, Jiaqi Liu, Jixuan Huang, Honglin Guo, Baodai Huang, Tinggang Chen, Qi Zhang, Zhonghang Lu, Chenyu Liu, Jiajun Sun, Jiazheng Zhang, Dingwei Zhu, Xin Guo, Junzhe Wang, Zhihao Zhang, Yuming Yang, Junjie Ye, Minghe Gao, Dongrui Liu, Jiaming Ji, Guohao Li, Tao Gui, Qi Zhang, Xuanjing Huang
arXiv:2604. 00073v3 Announce Type: replace-cross Abstract: There has been growing interest in building agents that can interact with digital platforms to execute meaningful enterprise tasks autonomously.
By Patrice Bechard, Orlando Marquez Ayala, Emily Chen, Jordan Skelton, Sagar Davasam, Srinivas Sunkara, Vikas Yadav, Sai Rajeswar
arXiv:2510. 04452v3 Announce Type: replace-cross Abstract: Computer use agents (or "agents") are generative AI that automates actions within user interfaces from user commands.
By Jenny T. Liang, Titus Barik, Jeffrey Nichols, Eldon Schoop, Ruijia Cheng
Large language models (LLMs) and vision-language models (VLMs) are expanding the range of behaviors that can be represented in agent-based simulations, but many contemporary platforms are difficult to...
arXiv:2404. 02039v5 Announce Type: replace Abstract: Game environments provide rich, controllable settings that stimulate many aspects of real-world complexity.
By Sihao Hu, Tiansheng Huang, Gaowen Liu, Ramana Rao Kompella, Fatih Ilhan, Selim Furkan Tekin, Yichang Xu, Zachary Yahn, Ling Liu
The paper introduces two minimal simulation foundations—SD-AgentFoundry-2D and SD-AgentFoundry-3D—for educational and rapid prototyping use with large language models (LLMs) and vision-language models (VLMs). SD-AgentFoundry-2D offers a 2D multi‑agent environment where LLM agents move, communicate, and react to local events such as fire, while SD-AgentFoundry-3D provides a 3D digital‑twin setting where a VLM interprets first‑person images to generate natural‑language movement instructions. Both frameworks run locally on macOS, Windows, and Linux, are intentionally lightweight, and are open for modification rather than being finished applications.
By Ryuki Hyodo