构建模块化认知框架，辅助复杂问题推理与决策。
A Modular Cognitive Architecture for Assisted Reasoning: The Nemosine Framework
- 将认知任务分解为规划、评估、交叉验证等模块化角色。
- 通过形式化规范确保结构一致性与可复现性。
- 适合需要系统性分析的AI辅助决策场景。
本文提出Nemosine框架，一种支持辅助推理、结构化思维与系统性分析的模块化认知架构。该模型通过功能化的认知模块（即‘人格’）组织规划、评估、交叉验证和叙事整合等任务。框架融合元认知、分布式认知与模块化认知系统的原理，为辅助问题求解与决策支持提供可操作的结构。其架构通过形式化规范、内部一致性标准与可复现的组件进行文档化，旨在为未来计算实现提供清晰的概念基础，并推动符号化模块化推理架构的研究。
原文摘要 · Abstract (English)
This paper presents the Nemosine Framework, a modular cognitive architecture designed to support assisted reasoning, structured thinking, and systematic analysis. The model operates through functional cognitive modules ("personas") that organize tasks such as planning, evaluation, cross-checking, and narrative synthesis. The framework combines principles from metacognition, distributed cognition, and modular cognitive systems to offer an operational structure for assisted problem-solving and decision support. The architecture is documented through formal specification, internal consistency criteria, and reproducible structural components. The goal is to provide a clear conceptual basis for future computational implementations and to contribute to the study of symbolic-modular architectures for reasoning.
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