DAG of DAGs crushes math benchmark (drive.google.com)

A new innovation called Mycelium has emerged, leveraging the concept of a "DAG of DAGs," which effectively decomposes complex mathematical problems into atomic signatures or reusable solution patterns. This project employs large language models (LLMs) to structure problems into directed acyclic graphs (DAGs), allowing for the matching of steps against a growing database of known solutions through cosine similarity. The signature database continuously evolves as new problems are solved, enabling faster problem solving and deeper insights into the internal reasoning of LLMs. This initiative is significant for the AI/ML community because it not only enhances the efficiency of mathematical reasoning by transforming how LLMs approach problem-solving but also potentially unveils the fundamental operations of these models. By documenting and introspecting the functional patterns within LLMs, Mycelium provides a framework for better understanding their decision-making processes. The system's architecture, including recursive decomposition and adaptive routing among signatures based on performance, opens new avenues for AI learning and optimization, pushing the boundaries of how AI can tackle complex tasks.