English Mode Intent Resolution at Compile Time

Canon² — Trust Layer Research Archive. Natural-language programming interfaces promise to eliminate the cognitive distance between human intention and computational execution, but every existing implementation relies on probabilistic interpretation—neural language models, statistical parsers, or heuristic pattern matchers—that introduces irreducible variance into the compilation pipeline. When the same English sentence is interpreted twice by a probabilistic system, there is no guarantee that the second interpretation matches the first. This variance is tolerable for conversational assistants and code suggestion tools, but it is categorically incompatible with the deterministic execution, certificate-anchored provenance, and governance-constrained autonomy that the Lume ecosystem requires. I present English Mode Intent Resolution at Compile Time, a deterministic natural-language compilation pipeline that transforms unconstrained English prose into canonical Abstract Syntax Trees through a six-stage resolution process: lexical normalization, semantic grounding, constraint extraction, intent tuple formation, canonical AST mapping, and certificate-bound validation. The pipeline operates entirely at compile time, producing a fully resolved, cryptographically sealed Intent Tuple before any runtime execution begins. Each resolution stage is deterministic: identical English input produces identical intermediate representations at every stage and identical final AST output across all compilations, regardless of the platform, the time of day, or the number of prior compilations. The pipeline integrates with the Lume compiler through the LDIR seven-layer tolerance chain [1], with the Trust Layer Certificate Fabric [3] through compile-time certificate emission, with Lume-V [4] through envelope constraint derivation, with DAIGS [5] through cognitive substrate integration, and with GUPAS [2] through the six-layer governance architecture. I also identify and formalize six failure modes—ambiguity collapse, over-specification, under-specification, conflicting constraints, semantic drift, and intent inversion—that deterministic natural-language compilers must detect and resolve at compile time rather than deferring to runtime heuristics. To my knowledge, this paper presents the first complete compile-time intent resolution pipeline for natural-language programming that provides bit-identical deterministic output with cryptographic provenance guarantees.