This story was originally published on HackerNoon at: https://hackernoon.com/zenos-paradox-and-the-problem-of-ai-tokenization.
Token prediction forces LLMs to drift. This piece shows why, what Zeno can teach us about it, and how fidelity-based auditing could finally keep models grounded
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Zeno Effect is a structural flaw baked into how autoregressive models predict tokens: one step at a time, based only on the immediate past. It looks like coherence, but it’s often just momentum without memory.

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