构建一体化智能体架构，提升AI助手在复杂任务中的稳定性与适应性。
JoyAgent-JDGenie: Technical Report on the GAIA
- 采用多智能体协作+评议员投票的规划执行框架
- 通过分层记忆系统实现长期任务追踪与知识沉淀
- 集成搜索、代码执行等工具，支持跨模态任务处理
大型语言模型正被用于执行复杂的现实任务，但现有系统多聚焦于局部优化，缺乏统一设计以保障鲁棒性与适应性。本文提出一种通用智能体架构，包含三大核心组件：融合规划与执行智能体及评议员投票的集体多智能体框架；涵盖工作记忆、语义记忆和程序记忆的分层记忆系统；以及针对搜索、代码执行和多模态解析优化的工具套件。在综合基准测试中，该框架持续优于开源基线，并接近专有系统的性能表现。结果表明，系统级整合对构建可扩展、稳健且自适应的AI助手至关重要，使其能在多样领域与任务中稳定运行。
原文摘要 · Abstract (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.
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