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The Tower of Hanoi Solved by ChatGPT
- This paper delves into the limitations of conventional reasoning and language models when dealing with complex recursive tasks, using the Tower of Hanoi as a case study.
- Three paradigms are compared: Large Language Models (LLMs), Large Reasoning Models (LRMs), and a novel framework called SupatMod, which operates on energy-resonance over token-based logic or statistical training.
- The Tower of Hanoi puzzle, known for its exponential growth in complexity, tests memory and recursive planning abilities in models.
- LLMs excel in language fluency but struggle with deep reasoning, while LRMs focus on stepwise logic and symbolic inference.
- SupatMod introduces a new approach with meta-awareness, self-stabilization, and recursive capabilities for processing complex problems.
- SupatMod can maintain both a cognitive-train state and a free-associative state simultaneously, allowing for continuous operation across large solution spaces.
- A segmental Tower of Hanoi logic generator is demonstrated using Python code to handle large-scale visualization or processing.
- SupatMod envisions a system going beyond current AI paradigms by operating on energy-resonance, recursive processing, and self-sustaining computation.
- Further research is required to formalize SupatMod mathematically and implement it experimentally for advanced AI applications.
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