arXiv:2508.06062cs.AIcs.LG2025-08被引 2

提出用语义模型模拟人类认知想象,提升AI推理能力。

Don't Forget Imagination!

  • 用概率因果关系构建可学习的语义模型,模拟认知想象
  • 模型能保持想象情境的一致性,支持推理过程中的上下文验证
  • 适合需要深度推理和情境理解的AI系统研究者

认知想象是人类思维中的关键能力,不是简单的心理图像,而是能够心智化地构建连贯的整体概念与因果关系体系,作为推理、决策和预测的语义背景。本文认为当前对认知想象的作用仍严重低估,导致AI在推理时缺乏背景支持,无法自我验证合理性。为此,我们提出语义模型——一种可学习的数学模型,基于概率因果关系,能像神经网络一样训练。该模型可模拟认知想象,确保想象情境的一致性,并以透明方式操控由因果关系连接的互相关联事实,实现可解释的推理过程。

原文摘要 · Abstract (English)

Cognitive imagination is a type of imagination that plays a key role in human thinking. It is not a ``picture-in-the-head'' imagination. It is a faculty to mentally visualize coherent and holistic systems of concepts and causal links that serve as semantic contexts for reasoning, decision making and prediction. Our position is that the role of cognitive imagination is still greatly underestimated, and this creates numerous problems and diminishes the current capabilities of AI. For instance, when reasoning, humans rely on imaginary contexts to retrieve background info. They also constantly return to the context for semantic verification that their reasoning is still reasonable. Thus, reasoning without imagination is blind. This paper is a call for greater attention to cognitive imagination as the next promising breakthrough in artificial intelligence. As an instrument for simulating cognitive imagination, we propose semantic models -- a new approach to mathematical models that can learn, like neural networks, and are based on probabilistic causal relationships. Semantic models can simulate cognitive imagination because they ensure the consistency of imaginary contexts and implement a glass-box approach that allows the context to be manipulated as a holistic and coherent system of interrelated facts glued together with causal relations.

认知想象语义模型推理增强

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