Multilingual Inference LDIR Expansions

Canon² — Trust Layer Research Archive. The Lume Deterministic Inference Relay (LDIR) provides the multilingual substrate through which the Lume programming language resolves natural-language expressions into deterministic computational intent. The canonical LDIR specification defines 31 inference rules across 4 tiers, governing how English-language expressions traverse the seven-layer tolerance chain to produce canonical Intent Tuples. As the Lume ecosystem extends to serve globally distributed teams, autonomous agents operating across linguistic boundaries, and synthetic organisms communicating in heterogeneous language environments, the canonical LDIR's English-centric design encounters a fundamental limitation: it cannot resolve intents expressed in Japanese, Spanish, Arabic, Mandarin, or any non-English language without a structured multilingual expansion architecture. I present LDIR Expansions, a deterministic multilingual inference framework that extends the canonical LDIR to resolve natural-language intents across an arbitrary number of human languages while preserving the bit-identical determinism, certificate-anchored provenance, and governance-constrained execution that the Lume ecosystem requires. The framework introduces three architectural innovations: language-specific normalization modules that transform diverse natural-language inputs into a Universal Intent Schema (UIS) independent of any specific language; cross-lingual semantic equivalence verification that guarantees identical Intent Tuples for semantically equivalent expressions regardless of source language; and certificate-bound language routing that records the source language, the normalization path, and the equivalence verification result in the Trust Layer Certificate Fabric [3]. The framework integrates with the Lume compiler [1] through the expanded tolerance chain, with Lume-V [4] through language-aware envelope derivation, with DAIGS [5] through multilingual cognitive substrate extensions, with SOR through cross-lingual organism communication, and with GUPAS [2] through the six-layer governance architecture. I also identify and formalize six failure modes specific to multilingual deterministic inference—semantic collapse, over-normalization, under-normalization, conflicting constraints, cultural drift, and intent inversion—that must be detected and resolved at compile time. To my knowledge, this paper presents the first complete multilingual inference architecture for a deterministic natural-language programming ecosystem.