Zeno’s Paradox and the Problem of AI Tokenization

Large Language Models like ChatGPT suffer from "drift" rather than traditional hallucination, where each token prediction relies only on immediately preceding text without reference to the original input. This creates cumulative errors that compound when text passes between models, similar to Zeno's paradox where motion breaks down into disconnected steps. Solutions like "fidelity-constrained refinement" could continuously compare outputs against original inputs to prevent this structural drift.