Solving a million step LLM task with zero errors

This research paper introduces how we can reliably complete complex, multi-step tasks with zero errors. The core concept is **extreme decomposition** of a task into minimal subtasks handled by focused "microagents," which overcomes the inherent, escalating error rate of monolithic LLMs over long horizons. This modular approach integrates an **efficient error correction** mechanism—specifically, a first-to-ahead-by-$k$ voting scheme—and a process of **red-flagging** unreliable outputs, drastically improving the probability of success. Empirical results on the Towers of Hanoi benchmark demonstrate that MAKER successfully solves a task requiring over one million LLM steps flawlessly, suggesting that MDAPs offer an **orthogonal and scalable path** for AI development beyond merely increasing the size and intelligence of base LLMs. The analysis also provides **cost scaling laws** showing that this framework scales efficiently, with cost increasing only log-linearly with the number of steps, making it an economically viable approach for large-scale applications.