MAKER Comparison Protocol

While models like GPT and Claude have achieved breakthroughs in reasoning and tool use, they share a fundamental flaw when tackling real-world processes: they inevitably break down after a few hundred steps due to a persistent, low-level error rate. This "weakest link" problem means a 1% error rate on a traditional benchmark guarantees failure on a million-step task. But what if we could eliminate errors completely? A new paper introduces MAKER, a framework that solves this scalability crisis
So how does MAKER work? minimizing model cost and then implements 3 key features:

1. Maximal Agentic Decomposition (MAD): Breaking down a task into the smallest possible single-step subtasks, assigning each to a focused microagent. This prevents context overload and keeps the per-step error rate stable as the task length increases.

2. First-to-ahead-by-k Voting: Exploiting the modularity of MAD, multiple agents independently solve the same subtask, with a "first-to-ahead-by-k" voting scheme determining the correct output. This exponentially reduces the per-step error probability.

3. Red-Flagging: Discarding LLM responses that show structural or formatting anomalies, as these often correlate with reasoning errors, thereby increasing the effective per-step success rate

MAKER demonstrated how a system can reliably solve a million-step task with zero errors not by using a single, monolithic, super-intelligent LLM, but by coordinating a swarm of smaller, focused microagents.

You can interact with the model below and test different scenario by changing the number of discs and the k-voting variables.