Getting the right answer the first time, is how you save time and tokens (github.com)

A new framework called MICT (Map, Iterate, Check, Transform) has been introduced to enhance the performance of Large Language Models (LLMs) by fostering "System 2" thinking, which emphasizes analytical and deliberate processing. This method shifts LLMs from their traditional linear text prediction approach to a more structured, cyclical architecture that significantly reduces hallucinations—errors that can arise during complex tasks—by mandating a four-stage processing loop before generating final outputs. As LLMs currently face challenges with intricate logical problems and multi-step reasoning, the MICT framework mitigates these issues by allowing models to evaluate multiple hypotheses and constraints, ultimately choosing the most effective solution. The significance of the MICT framework lies in its ability to improve the reliability and accuracy of AI responses, particularly in complex scenarios like software development or system architecture. By implementing MICT, agents can "show their work" through detailed structured outputs, thus providing transparency in their decision-making process. Additionally, the framework's hierarchical task management—via the Hierarchical Contextual Transformation System (HCTS)—allows LLMs to tackle large-scale tasks by breaking them down into manageable components, enhancing their effectiveness in handling diverse and intricate queries. This advancement is part of Project Genesis, which explores cutting-edge developments in AI-native operating systems and computational physics.