神经模型自动生成可解释的符号语言，实现透明推理。
Interpretable by AI Mother Tongue: Native Symbolic Reasoning in Neural Models
- 让模型用符号语言自主推理，符号代表语义模式。
- 推理路径可追踪，准确率与传统方法相当。
- 适合需要透明决策过程的研究与应用。
我们提出一种框架，使神经模型发展出一种名为AI母语的原生符号语言，同时支持直觉推理、组合符号链和内在可解释性。不同于事后解释方法，本方法将推理直接嵌入模型表征：符号捕捉有意义的语义模式，符号链追踪决策路径，门控归纳机制引导选择性关注，实现透明且灵活的推理。我们引入互补训练目标以增强符号纯度和决策稀疏性，并采用序列专业化策略，先建立广泛的符号能力，再优化直觉判断。在人工智能任务上的实验表明，该方法在保持竞争性准确率的同时，可生成可验证的推理痕迹，证明AI母语可作为神经模型中可解释性、直觉与符号推理的统一机制。
原文摘要 · Abstract (English)
We present a framework where neural models develop an AI Mother Tongue, a native symbolic language that simultaneously supports intuitive reasoning, compositional symbol chains, and inherent interpretability. Unlike post-hoc explanation methods, our approach embeds reasoning directly into the model's representations: symbols capture meaningful semantic patterns, chains trace decision paths, and gated induction mechanisms guide selective focus, yielding transparent yet flexible reasoning. We introduce complementary training objectives to enhance symbol purity and decision sparsity, and employ a sequential specialization strategy to first build broad symbolic competence and then refine intuitive judgments. Experiments on AI tasks demonstrate competitive accuracy alongside verifiable reasoning traces, showing that AI Mother Tongue can serve as a unified mechanism for interpretability, intuition, and symbolic reasoning in neural models.
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