JoyAgent-JDGenie: Technical Report on the GAIA
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.
68dde0406024653e8a3ed0be
Jiarun Liu
68dde0406024653e8a3ed0bf
Shiyue Xu
68dde0406024653e8a3ed0c0
Shangkun Liu
68dde0406024653e8a3ed0c1
Yang Li
68dde0406024653e8a3ed0c2
Wen Liu
68dde0406024653e8a3ed0c3
Min Liu
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Xiaoqing Zhou
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Hanmin Wang
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Shilin Jia
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zhen Wang
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Shaohua Tian
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Hanhao Li
68dde0406024653e8a3ed0ca
Junbo Zhang
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Yongli Yu
68dde0406024653e8a3ed0cc
Peng Cao
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Haofen Wang
JoyAgent-JDGenie: Technical Report on the GAIA
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.
A generalist agent architecture combining multi-agent planning, hierarchical memory, and a refined tool suite outperforms existing systems in diverse tasks.
multi-agent framework planning agents execution agents critic model voting hierarchical memory system working memory semantic memory procedural memory tool suite search code execution multimodal parsing