提出可显式处理领域信息的推理架构，让不同领域的推理更高效透明。
Domain-Contextualized Inference: A Computable Graph Architecture for Explicit-Domain Reasoning
- 将领域作为计算核心参数，实现按领域裁剪搜索空间
- 支持符号、神经、向量等多种计算底座的统一推理
- 适用于医疗等需透明推理过程的高可靠性场景
本文建立了一种与计算底座无关的推理架构，将领域作为显式的首要计算参数。该架构实现了领域限定的搜索空间裁剪，使每个查询的搜索空间从 O(N) 降低至 O(N/K)，可在符号、神经、向量及混合底座上进行独立于底座的执行，并保证每一步推理都携带其评估上下文，实现透明推理链。贡献在于架构层面而非逻辑设计。我们从五个维度形式化了计算理论：五层架构；三种领域计算模式（链索引、路径遍历作为 Kleisli 复合、向量引导的底座转换）；一种与底座无关的接口，包含 Query、Extend、Bridge 三个操作；可靠性条件 C1 至 C4 及三类失效模式；并通过 PHQ-9 临床推理案例进行验证。本文的核心贡献包括操作语义、复杂度边界、单子结构、底座转换和边界条件的计算理论。
原文摘要 · Abstract (English)
We establish a computation-substrate-agnostic inference architecture in which domain is an explicit first-class computational parameter. This produces domain-scoped pruning that reduces per-query search space from O(N) to O(N/K), substrate-independent execution over symbolic, neural, vector, and hybrid substrates, and transparent inference chains where every step carries its evaluative context. The contribution is architectural, not logical. We formalize the computational theory across five dimensions: a five-layer architecture; three domain computation modes including chain indexing, path traversal as Kleisli composition, and vector-guided computation as a substrate transition; a substrate-agnostic interface with three operations Query, Extend, Bridge; reliability conditions C1 to C4 with three failure mode classes; and validation through a PHQ-9 clinical reasoning case study. The computational theory including operational semantics, complexity bounds, monad structure, substrate transitions, and boundary conditions is the contribution of this paper.
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