用图结构统一建模认知状态，让思维过程更清晰可解释。
Nested Episodic State Topology (NEST): A Graph-Theoretic Architecture of Cognitive States
- 将概念、事件、感知等构建成带权重的有向图，节点可嵌套子图。
- 区分长期信念与短期工作记忆，支持动态更新和冲突检测。
- 兼容多种认知理论，适合做基础框架或跨模型对比研究。
我们提出 NEST（嵌套情景状态拓扑），一种基于图论的认知状态表征本体，将认知视为结构化状态的生成与转换，而非现成的经验模型。概念、事件、感知和任务情境被表示为带类型的加权图，节点可携带内部子图；边分为六类关系：因果、包含、时间、关联、证据和空间。持久信念图与容量有限的工作记忆图分离，后者可承载临时非信念内容。通过工作记忆-信念对齐、冲突目录及信念更新算子，定义临时结构如何与存储知识验证并修正信念。提供一套可复用的操作工具包——激活、图属性函数、工作记忆转换、意识与轨迹函数、信念更新等，构成形式核心。衍生诊断指标如碎片化、参与度、符号评估、一致性与活跃冲突，统一解释常见认知现象；自我相关处理通过信念图中的指定自画像子图实现。后续部分在不引入新原语的情况下实例化该核心：现象签名、动作选择与故障模式的任务实例化方案、以及将ACT-R、Soar、Sigma、通用认知模型、全局工作空间理论、语义网络、理论理论和组块化映射为单一语言的受限区域。这些映射构成技术核心章节；讨论涵盖范围、局限与开放问题。贡献在于奠基性：为后续经验性、计算性与领域特定工作提供透明的表征底座。
原文摘要 · Abstract (English)
We present NEST (Nested Episodic State Topology), a foundational graph-theoretic representational ontology for modeling cognition as structured state formation and transformation rather than as a finished empirical model. Concepts, episodes, percepts, and task contexts are represented as typed, weighted graphs whose nodes may carry internal subgraph payloads; edges are typed under six relation classes -- causal, containment, temporal, associative, evidential, and spatial. Durable belief graphs are separated from capacity-limited working-memory graphs that may host transient non-belief content. WM-belief grounding, conflict catalogs, and belief-update operators specify how transient structure is tested against stored knowledge and how belief is revised. A reusable operator toolkit -- activation, graph-property functionals, working-memory transitions, awareness and trajectory functionals, and belief update -- organizes the formal core. Derived diagnostics such as fragmentation, involvement, signed evaluation, coherence, and active conflict define familiar phenomena in the same ontology; self-related processing is modeled through designated self-image subgraphs within belief. Subsequent sections instantiate this core without new primitives: phenomena signatures, a task-instantiation schema for action selection and failure modes, and compatibility mappings that embed ACT-R, Soar, Sigma, the Common Model of Cognition, Global Workspace Theory, semantic networks, Theory-Theory, and chunking as constrained regions of one language. Mappings constitute the culminating technical section; discussion addresses scope, limitations, and open research directions. The contribution is intentionally foundational: a transparent representational substrate for later empirical, computational, and domain-specific work.
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