Breaking complex reasoning into multiple fresh inference passes with shared artifacts lets LLMs catch and correct mistakes earlier, improving accuracy on multi-step tasks like counting, ordering, and multi-hop reasoning.
This paper proposes Chained Recursive Language Models (Chained RLM), a system where an LLM is called multiple times in sequence to solve complex reasoning tasks. Instead of trying to do everything in one long response, each call gets the original problem plus a summary of previous work, allowing the model to inspect and fix earlier mistakes before moving forward.