发表机构
Electronics and Telecommunications Research Institute（韩国电子通信研究院）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本研究提出认知场网络（CFN），一种循环Transformer，通过隐场重入实现无需显式记忆的持久认知，实验证明其能产生内容依赖的持久动力学，为研究历史依赖认知提供平台。
AI 中文摘要
认知场理论（CFT）提出，认知源于记忆修饰的集体动力学，该动力学生成一个持久的宏观认知场。在此，我们开发了一种认知场网络（CFN），这是一种循环Transformer，其中组织化的隐场通过\\[ \Phi_{n+1}=F_{\theta}(X_{n+1},\Phi_n) \\]重新进入后续推理。CFN并未规定显式的记忆操作，而是允许新信息作用于一个已经依赖于历史的集体状态。我们发现，学习会组织出持久的、内容依赖的循环动力学，其时间尺度随训练的循环视界系统性增加。语义延续将循环状态传播到远超该视界，而无需重放目标答案。在没有内容特定支持的情况下，该场表现出有限的被动弛豫，而周期性重新暴露于相关输入会反复更新存续状态，并驱动其趋向近似稳定的非零状态。无关输入和无循环控制无法重现此行为，而近似改写的重新暴露产生较弱的更新，证明了表征敏感的持久性。这些结果区分了三种动力学过程：集体记忆修饰形成并维持依赖于历史的认知场，结构化输入重组该场，跨周期重入使所得状态对后续推理因果可用。因此，CFN提供了一个受控的计算平台，用于研究无需单独规定记忆系统的持久、依赖历史的认知动力学。
英文摘要
Cognitive Field Theory (CFT) proposes that cognition arises from memory-dressed collective dynamics that generate a persistent macroscopic cognitive field. Here we develop a Cognitive Field Network (CFN), a recurrent Transformer in which the organized hidden field re-enters subsequent inference through \[ Φ_{n+1}=F_θ(X_{n+1},Φ_n). \] Rather than prescribing an explicit memory operation, the CFN allows new information to act on an already history-dependent collective state. We find that learning organizes persistent, content-dependent recurrent dynamics whose timescale increases systematically with the trained recurrent horizon. Semantic continuation propagates the recurrent state far beyond this horizon without replay of the target answer. Without content-specific support, the field exhibits finite passive relaxation, whereas periodic re-exposure to relevant input repeatedly renews the surviving state and drives it toward an approximately stationary nonzero regime. Unrelated-input and recurrence-off controls do not reproduce this behavior, while near-paraphrased re-exposure produces weaker renewal, demonstrating representation-sensitive persistence. These results distinguish three dynamical processes: collective memory dressing forms and sustains a history-dependent cognitive field, structured input reorganizes this field, and cross-cycle re-entry makes the resulting state causally available to subsequent inference. The CFN therefore provides a controlled computational platform for studying persistent, history-dependent cognitive dynamics without a separately prescribed memory system.
Comments36 pages, 13 figures
