In Navigating Complexity: Orchestrated Problem Solving with Multi-Agent LLMs, researchers explore how Large Language Models can tackle vague, complex problems through decomposition. An orchestrating LLM works with users to break down the problem into sub-problems, each solved by dedicated LLM agents or functions.

• An orchestrator LLM helps understand and decompose complex, vague problems.
• Workflow involves interaction, follow-up questions, and sub-problem assignment.
• Parallel solving by specialised agents overseen by the orchestrating LLM.
• Overcomes limitations like token constraints and enhances problem-solving granularity.
• Offers scalability and nuanced solutions, akin to human problem-solving.

The study signifies an evolutionary step towards human-like problem-solving by AI, emphasizing the importance of orchestration over mere algorithmic enhancements for addressing real-world challenges.