{"ID":2861864,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2510.00510","arxiv_id":"2510.00510","title":"JoyAgent-JDGenie: Technical Report on the GAIA","abstract":"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.","short_abstract":"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-age...","url_abs":"https://arxiv.org/abs/2510.00510","url_pdf":"https://arxiv.org/pdf/2510.00510v1","authors":"[\"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\"]","published":"2025-10-01T04:41:58Z","proceeding":"cs.CL","tasks":"[\"cs.CL\"]","methods":"[\"Language Model\"]","has_code":false}