Energy based AI reasoning model – Sudoku solver performance comparison (sudoku.logicalintelligence.com)

A new performance comparison has been released for an energy-based AI model (EBM) called Kona, which solves Sudoku puzzles more efficiently than traditional AI models, including the latest large language models (LLMs). Unlike typical approaches that rely on guesswork and backtracking, Kona evaluates the entire puzzle simultaneously and can provide solutions within seconds. This demonstrates a significant leap towards developing self-aligning systems, which is a crucial stepping stone toward achieving artificial general intelligence (AGI). The testing environment was carefully designed to ensure a fair evaluation of logical reasoning capabilities. By disabling code execution, the EBM’s performance starkly contrasts with LLMs that might leverage brute-force search techniques to solve puzzles. This ability to reason logically without "cheating" indicates Kona's potential for deeper understanding and inference capabilities, positioning it as a promising contender in the ongoing quest for more advanced AI models that can think and solve problems like humans.