DALM is a proposed language model architecture that enforces algebraic constraints via a three-phase process over domain lattices to prevent cross-domain knowledge contamination during generation.
Domain-Contextualized Inference: A Computable Graph Architecture for Explicit-Domain Reasoning
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abstract
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.
years
2026 2verdicts
UNVERDICTED 2representative citing papers
A five-layer computable graph architecture makes domain an explicit first-class parameter, yielding domain-scoped pruning, substrate-agnostic operations, and transparent inference with reliability conditions C1-C4.
citing papers explorer
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DALM: A Domain-Algebraic Language Model via Three-Phase Structured Generation
DALM is a proposed language model architecture that enforces algebraic constraints via a three-phase process over domain lattices to prevent cross-domain knowledge contamination during generation.
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Domain-Contextualized Inference: A Computable Graph Architecture for Explicit-Domain Reasoning
A five-layer computable graph architecture makes domain an explicit first-class parameter, yielding domain-scoped pruning, substrate-agnostic operations, and transparent inference with reliability conditions C1-C4.