Beyond "Hallucinations": A Framework for Stable Human-AI Reasoning

ArXi:2510.14665v2 Announce Type: replace As large language models (LLMs) become integrated into everyday and high-stakes decision-making, they inherit the ambiguity and biases of human language. While they produce fluent and coherent outputs, they rely on statistical pattern prediction rather than grounded reasoning, creating a risk of outputs that are plausible but incorrect. This paper argues that these failures are not only technical but cognitive.