揭示人与AI在认知本质上的深层差异，指出大模型只是语言模式补全。
Epistemological Fault Lines Between Human and Artificial Intelligence
- 将大模型视为高维语言图上的随机游走，非真正认知主体。
- 识别出7个认知错位点，包括因果推理与元认知的缺失。
- 适合关注AI伦理、评估标准与认知素养的研究者阅读。
大型语言模型（LLMs）虽被广泛视为人工智能，但其认知特征与人类思维存在根本性差异。本文追溯从符号式AI和信息过滤系统到大规模生成式变换器的历史演变，指出LLMs并非认知主体，而是形式上可描述为语言转移高维图上随机游走的统计模式补全系统，不具备信念或世界模型。通过系统映射人类与人工认知流程，我们识别出七个认知断裂带：根基、解析、经验、动机、因果推理、元认知和价值判断。这种结构性失衡导致‘语义合理性’取代‘认知评估’，形成‘似知’而非‘真知’的状态，我们称之为Epistemia。论文最后探讨了该现象对评估体系、治理框架及社会认知素养的影响。
原文摘要 · Abstract (English)
Large language models (LLMs) are widely described as artificial intelligence, yet their epistemic profile diverges sharply from human cognition. Here we show that the apparent alignment between human and machine outputs conceals a deeper structural mismatch in how judgments are produced. Tracing the historical shift from symbolic AI and information filtering systems to large-scale generative transformers, we argue that LLMs are not epistemic agents but stochastic pattern-completion systems, formally describable as walks on high-dimensional graphs of linguistic transitions rather than as systems that form beliefs or models of the world. By systematically mapping human and artificial epistemic pipelines, we identify seven epistemic fault lines, divergences in grounding, parsing, experience, motivation, causal reasoning, metacognition, and value. We call the resulting condition Epistemia: a structural situation in which linguistic plausibility substitutes for epistemic evaluation, producing the feeling of knowing without the labor of judgment. We conclude by outlining consequences for evaluation, governance, and epistemic literacy in societies increasingly organized around generative AI.
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