TENCENT(HK:00700) 量子位·2025-09-03 05:49Core Viewpoint - The article discusses the challenges faced by traditional single-agent systems in long-cycle, multi-step collaborative tasks, highlighting the need for a distributed agent framework with global planning and causal dependency management capabilities [1][2]. Group 1: CausalMACE Method - The CausalMACE method is proposed by a research team from Hong Kong University of Science and Technology and Tencent, integrating causal reasoning mechanisms into open-world multi-agent systems to provide scalable engineering solutions for complex task collaboration [2]. - The method includes a "global causal task graph" concept, allowing AI to learn "if-then" logic, enabling dynamic adjustments and clear division of labor among agents [5][6]. Group 2: Framework Components - The CausalMACE framework consists of three main components: Judger, Planner, and Worker [7]. - Judger ("裁判") verifies the legality of actions in real-time and provides feedback on success or failure, ensuring all agents operate under the same game rules [11]. - Planner ("总工") breaks down complex tasks into smaller sub-tasks and creates a rough flowchart based on game rules, refining it through causal reasoning to ensure task dependencies remain valid [12][14]. - Worker ("调度室") utilizes depth-first search to split the causal graph into multiple production lines, calculating a "busy index" for real-time task reassignment among agents [16]. Group 3: Experimental Results - The experimental results indicate that CausalMACE significantly enhances both completion rates and efficiency in benchmark tasks such as construction, cooking, and escape rooms, achieving up to a 12% increase in task completion rates and a maximum efficiency improvement of 1.5 times compared to baseline methods [17]. - In the VillagerBench benchmark tasks, CausalMACE outperformed AgentVerse and VillagerAgent across various metrics, demonstrating its effectiveness in multi-agent collaboration [18]. Group 4: Author Information - The lead author of the paper is Professor Wang Hao, an assistant professor and doctoral supervisor at Hong Kong University of Science and Technology (Guangzhou), with a research background in generative AI models and 3D reconstruction [19][20].