First Agentic System to Solve a Million-Step Reasoning Problem with Zero Errors (arxiv.org)

Researchers introduced MAKER, an agentic system that for the first time completes a task requiring over one million chained LLM steps with zero errors. The paper frames this as a solution to a long-standing limitation: large language models accumulate a nonzero error rate that derails long, dependent processes (prior benchmarks like Towers of Hanoi failed after a few hundred steps). MAKER achieves error-free execution by breaking the overall task into extremely small, verifiable subtasks processed by dedicated microagents and by applying an efficient multi-agent voting-based error-correction protocol at every step. The result is significant because it demonstrates a practical path to scale LLM-driven procedures from hundreds to millions (and, in principle, far more) of steps without relying solely on continual improvements to base models. Key technical elements are extreme task decomposition, high modularity that isolates faults, and lightweight consensus among focused agents to detect and correct mistakes immediately. That design — termed massively decomposed agentic processes (MDAPs) — suggests new architectures for organizational- or society-level automation. Trade-offs remain: MDAPs impose coordination and compute overhead and are best suited to highly structured, stepwise problems where local verification is possible, but they provide a compelling blueprint for reliably extending LLMs to very long-range reasoning and workflows.