LLM Multi-Agent Systems: Challenges and Open Problems
This work systematically identifies and addresses four open challenges in large language model (LLM)-driven multi-agent systems: inefficient dynamic task allocation, insufficient robustness in collaborative reasoning, difficulty in hierarchical context modeling, and weak long-range memory coordination. To tackle these, we propose a novel architecture integrating iterative debate mechanisms, hierarchical context encoding, memory-augmented retrieval, and blockchain-based smart contract integration. We establish the first comprehensive challenge taxonomy covering collaborative reasoning, dynamic context modeling, and cross-layer memory coordination—distilling six fundamental unsolved problems. Furthermore, we introduce the first verifiable, scalable, and interpretable LLM multi-agent paradigm tailored to real-world distributed environments (e.g., blockchain systems). Our framework unifies theoretical advancement and practical deployment, providing a principled roadmap for both research and engineering.