TDAG: A Multi-Agent Framework based on Dynamic Task Decomposition and Agent Generation
This addresses the challenge of adaptability in multi-agent systems for real-world tasks, though it appears incremental as it builds on existing LLM-based agent frameworks.
The authors tackled the problem of LLM-based agents struggling with complex tasks due to error propagation and limited adaptability by proposing TDAG, a multi-agent framework that dynamically decomposes tasks and generates subagents, which significantly outperformed baselines in experiments.
The emergence of Large Language Models (LLMs) like ChatGPT has inspired the development of LLM-based agents capable of addressing complex, real-world tasks. However, these agents often struggle during task execution due to methodological constraints, such as error propagation and limited adaptability. To address this issue, we propose a multi-agent framework based on dynamic Task Decomposition and Agent Generation (TDAG). This framework dynamically decomposes complex tasks into smaller subtasks and assigns each to a specifically generated subagent, thereby enhancing adaptability in diverse and unpredictable real-world tasks. Simultaneously, existing benchmarks often lack the granularity needed to evaluate incremental progress in complex, multi-step tasks. In response, we introduce ItineraryBench in the context of travel planning, featuring interconnected, progressively complex tasks with a fine-grained evaluation system. ItineraryBench is designed to assess agents' abilities in memory, planning, and tool usage across tasks of varying complexity. Our experimental results reveal that TDAG significantly outperforms established baselines, showcasing its superior adaptability and context awareness in complex task scenarios.