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The hallucination problem of AI models is often simply understood as a prediction failure. But in reality, there is another failure mode—when humans do not provide a clear logical framework, AI misreads the reasoning structure.
This is not just a technical issue but also involves flaws in teaching and cognition. When handling implicit logical relationships, AI is prone to bias in distributed information fields lacking explicit guidance. In other words, this is a mismatch of "learning methods"—the system, in trying to fill information gaps, ends up creating nonexistent associations.
Understanding this distinction is important. It not only concerns model optimization but also involves how we design better human-computer interactions and information presentation methods.