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Computer Science > Artificial Intelligence

arXiv:2511.09030 (cs)
[Submitted on 12 Nov 2025]

Title:Solving a Million-Step LLM Task with Zero Errors

Authors:Elliot Meyerson, Giuseppe Paolo, Roberto Dailey, Hormoz
Shahrzad, Olivier Francon, Conor F. Hayes, Xin Qiu, Babak Hodjat,
Risto Miikkulainen
View a PDF of the paper titled Solving a Million-Step LLM Task with
Zero Errors, by Elliot Meyerson and 7 other authors
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    Abstract:LLMs have achieved remarkable breakthroughs in
    reasoning, insights, and tool use, but chaining these abilities
    into extended processes at the scale of those routinely executed
    by humans, organizations, and societies has remained out of
    reach. The models have a persistent error rate that prevents
    scale-up: for instance, recent experiments in the Towers of Hanoi
    benchmark domain showed that the process inevitably becomes
    derailed after at most a few hundred steps. Thus, although LLM
    research is often still benchmarked on tasks with relatively few
    dependent logical steps, there is increasing attention on the
    ability (or inability) of LLMs to perform long range tasks. This
    paper describes MAKER, the first system that successfully solves
    a task with over one million LLM steps with zero errors, and, in
    principle, scales far beyond this level. The approach relies on
    an extreme decomposition of a task into subtasks, each of which
    can be tackled by focused microagents. The high level of
    modularity resulting from the decomposition allows error
    correction to be applied at each step through an efficient
    multi-agent voting scheme. This combination of extreme
    decomposition and error correction makes scaling possible. Thus,
    the results suggest that instead of relying on continual
    improvement of current LLMs, massively decomposed agentic
    processes (MDAPs) may provide a way to efficiently solve problems
    at the level of organizations and societies.

Comments: Main paper: 14 pages, 29 pages with references and appendix
Subjects: Artificial Intelligence (cs.AI); Computation and Language
          (cs.CL); Multiagent Systems (cs.MA)
Cite as:  arXiv:2511.09030 [cs.AI]
          (or arXiv:2511.09030v1 [cs.AI] for this version)
          https://doi.org/10.48550/arXiv.2511.09030
          Focus to learn more
          arXiv-issued DOI via DataCite

Submission history

From: Elliot Meyerson [view email]
[v1] Wed, 12 Nov 2025 06:27:55 UTC (7,906 KB)
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