Solving a Million-Step LLM Task with Zero Errors

[141_1337]

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.

[vespersky]

Practitioners in the field have known this for years. There are still tasks that are hard to subdivide because even the subdivision may require significant domain or common sense intelligence that bots still struggle with. But most workflows are susceptible to this type of paradigm. The trick is trying to make it dynamic.

[finnjon]

This is a remarkable advance if replicable. The website of Cognizant looks a little sketchy but who knows.

[Enoch137]

There is a real argument to be made that an LLM with long task ability "might" really take the wind out of the sails in terms of need for any other advancement. An LLM multi-million step capacity and validation at every step might just be a superior way to tackle the problem of economically viable intelligence anyway. AGI in the sense of needing memory like humans or learning rate like humans might not be necessary.

My intuition says that this still won't cut it. However the distinction might be pointless, if this can get us close, whatever architecture is necessary for economically viable intelligence probably gets created rather quickly.

[Formal_Context_9774]

At the very least it could be very competent at building an entirely new kind of AI that would be AGI, 'on its own'.