JoyAgent-JDGenie：GAIA技术报告

JoyAgent-JDGenie: Technical Report on the GAIA

October 1, 2025

作者: Jiarun Liu, Shiyue Xu, Shangkun Liu, Yang Li, Wen Liu, Min Liu, Xiaoqing Zhou, Hanmin Wang, Shilin Jia, zhen Wang, Shaohua Tian, Hanhao Li, Junbo Zhang, Yongli Yu, Peng Cao, Haofen Wang

cs.AI

摘要

大型語言模型正日益被部署為執行複雜現實任務的自動化代理，然而現有系統往往專注於孤立的改進，缺乏針對魯棒性和適應性的統一設計。我們提出了一種通用型代理架構，該架構整合了三大核心組件：一個集體多代理框架，將規劃與執行代理與評審模型投票相結合；一個跨越工作、語義及程序層次的分層記憶系統；以及一套精煉的工具集，用於搜索、代碼執行和多模態解析。在全面基準測試中，我們的框架持續超越開源基準，並接近專有系統的性能。這些結果證明了系統級整合的重要性，並為構建能夠跨越多領域和多任務運作的可擴展、具韌性及適應性的人工智慧助手指明瞭方向。

English

Large Language Models are increasingly deployed as autonomous agents for complex real-world tasks, yet existing systems often focus on isolated improvements without a unifying design for robustness and adaptability. We propose a generalist agent architecture that integrates three core components: a collective multi-agent framework combining planning and execution agents with critic model voting, a hierarchical memory system spanning working, semantic, and procedural layers, and a refined tool suite for search, code execution, and multimodal parsing. Evaluated on a comprehensive benchmark, our framework consistently outperforms open-source baselines and approaches the performance of proprietary systems. These results demonstrate the importance of system-level integration and highlight a path toward scalable, resilient, and adaptive AI assistants capable of operating across diverse domains and tasks.