基础模型通过模拟思考过程实现推理，无需人类认知根基。
Simulated Reasoning is Reasoning
- 让模型自动生成并迭代思维路径，模仿‘出声思考’
- 在少样本下可独立解决问题，但缺乏常识易出错
- 挑战传统推理观，推动安全与伦理新思考
推理长期被视为理解的桥梁。传统观点认为推理需依赖符号化思维。然而，基础模型（FM）表明，推理并不必然依赖符号系统：它们可通过模仿‘出声思考’、测试并自我迭代思维路径来实现推理。这种机制使模型能在少样本下自主解题，但因缺乏现实锚定与常识，导致推理过程脆弱。本文探讨该现象的哲学意义，指出‘随机鹦鹉’比喻已不再适用，呼吁重新审视由模型推理能力增长带来的安全性与适切性规范问题。
原文摘要 · Abstract (English)
Reasoning has long been understood as a pathway between stages of understanding. Proper reasoning leads to understanding of a given subject. This reasoning was conceptualized as a process of understanding in a particular way, i.e., "symbolic reasoning". Foundational Models (FM) demonstrate that this is not a necessary condition for many reasoning tasks: they can "reason" by way of imitating the process of "thinking out loud", testing the produced pathways, and iterating on these pathways on their own. This leads to some form of reasoning that can solve problems on its own or with few-shot learning, but appears fundamentally different from human reasoning due to its lack of grounding and common sense, leading to brittleness of the reasoning process. These insights promise to substantially alter our assessment of reasoning and its necessary conditions, but also inform the approaches to safety and robust defences against this brittleness of FMs. This paper offers and discusses several philosophical interpretations of this phenomenon, argues that the previously apt metaphor of the "stochastic parrot" has lost its relevance and thus should be abandoned, and reflects on different normative elements in the safety- and appropriateness-considerations emerging from these reasoning models and their growing capacity.
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