Content-Consistent Logic: Generalized Quantifiers and Relational Reasoning in Natural Language Processing (内容一致性逻辑：广义量词与关系推理的自然语言处理)

This paper, building upon the "Dual-Mechanism Model" proposed in Content-Consistent Logic: The Irreplaceability of Traditional Logic in Natural Language Inference (Sun, 2026), further addresses relational reasoning and generalized quantifier reasoning in natural language. It argues that traditional logic is not inherently incapable of handling relations, but rather has not yet established a mechanism for relational deduction and inheritance suited to natural language. Although modern first-order logic can formalize relational structures through variables and quantifiers, its core objective is truth-preservation rather than content-coherent deduction in natural language. Consequently, it cannot replace the innate reasoning mechanisms of natural language itself. This paper proposes that relational reasoning in natural language fundamentally still depends on predicate inheritance within conceptual subordination structures. Through principles such as concept extension, quantifier internalization, relation binarization, the single-step subordination rule, logical descent, and logical ascent, many relational inferences can be reduced back to conceptual reasoning forms in traditional logic. On this basis, the paper further examines how generalized quantifiers (e.g., "most," "almost all," "many," "few") are constrained and mapped in natural language, arguing that their essence is not truth-functional computation in the modern logical sense, but rather content-consistent mapping within a determined domain. The paper systematically analyzes the seven types of generalized quantifier inference examples presented by Zhou (2019), demonstrating that natural language can accomplish complex relational reasoning through conceptual hierarchies, quantifier specification, scope restriction, and descent/ascent logical mechanisms, without relying on higher-order formalization systems in modern logic. The paper further points out that several valid syllogistic forms requiring reduction in traditional syllogistic can be directly supported by descent and ascent inheritance principles at the conceptual level, thus laying the groundwork for a future comprehensive replacement of reduction techniques. This paper maintains that the core objective of natural language logic is not abstract truth-value computation, but the consistency-preservation of conceptual content. Logic is primarily a system of content deduction, rather than a binary truth-functional calculus. The foundational status of traditional logic in natural language inference has not been genuinely superseded by modern logic.