JoyAgent-JDGenie: Technical Report on the GAIA
This addresses the need for scalable and resilient AI assistants across diverse domains, though it appears incremental as it integrates existing components into a unified system.
The authors tackled the problem of autonomous agents lacking robustness and adaptability by proposing a generalist agent architecture with multi-agent planning, hierarchical memory, and refined tools, achieving performance approaching proprietary systems on a comprehensive benchmark.
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.