Solving a Million-Step LLM Task with Zero Errors
4 days ago
- #Task Decomposition
- #Large Language Models
- #Artificial Intelligence
- LLMs have made significant progress in reasoning and tool use but struggle with extended processes due to persistent error rates.
- Recent experiments show LLMs fail in tasks requiring more than a few hundred steps, like the Towers of Hanoi benchmark.
- MAKER is introduced as the first system to solve a million-step LLM task with zero errors by extreme task decomposition.
- The approach uses microagents for subtasks and multi-agent voting for error correction at each step.
- Massively Decomposed Agentic Processes (MDAPs) may enable solving complex problems at organizational and societal levels without relying solely on LLM improvements.