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

大型语言模型正越来越多地被部署为执行复杂现实任务的自主代理，然而现有系统往往专注于孤立的改进，缺乏统一的设计来确保鲁棒性和适应性。我们提出了一种通用型代理架构，该架构整合了三大核心组件：一个结合规划与执行代理并通过评审模型投票的集体多代理框架，一个涵盖工作记忆、语义记忆和程序记忆的分层记忆系统，以及一套用于搜索、代码执行和多模态解析的精细化工具集。在全面基准测试中，我们的框架持续超越开源基线，并接近专有系统的性能。这些结果证明了系统级集成的重要性，并为构建能够跨领域和任务操作的可扩展、强韧且自适应的AI助手指明了一条路径。

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.