Current AI paradigms, as "architects of experience," face fundamental challenges in explainability and value alignment. This paper introduces "Weight-Calculatism," a novel cognitive architecture grounded in first principles, and demonstrates its potential as a viable pathway toward Artificial General Intelligence (AGI). The architecture deconstructs cognition into indivisible Logical Atoms and two fundamental operations: Pointing and Comparison. Decision-making is formalized through an interpretable Weight-Calculation model (Weight = Benefit * Probability), where all values are traceable to an auditable set of Initial Weights. This atomic decomposition enables radical explainability, intrinsic generality for novel situations, and traceable value alignment. We detail its implementation via a graph-algorithm-based computational engine and a global workspace workflow, supported by a preliminary code implementation and scenario validation. Results indicate that the architecture achieves transparent, human-like reasoning and robust learning in unprecedented scenarios, establishing a practical and theoretical foundation for building trustworthy and aligned AGI.

翻译：当前人工智能范式作为“体验的构建者”，在可解释性与价值对齐方面面临根本性挑战。本文提出基于第一性原理的新型认知架构“权重计算主义”，并论证其作为实现通用人工智能的可行路径的潜力。该架构将认知解构为不可分割的逻辑原子与两项基本操作：指向与比较。决策过程通过可解释的权重计算模型形式化（权重 = 收益 × 概率），所有数值均可追溯至可审计的初始权重集合。这种原子化解构实现了彻底的可解释性、对新情境的内在泛化能力以及可追溯的价值对齐。我们通过基于图算法的计算引擎与全局工作流工作流程详述其实现方案，并提供初步代码实现与场景验证。结果表明，该架构在未知场景中实现了透明化、类人推理与鲁棒学习，为构建可信且对齐的通用人工智能奠定了理论与实践基础。