Abstract

This work introduces a proprietary multi-layer consistency-checking framework (internally referred to as LMT MVP v1.0) designed to detect contradictions in input text early in the generation process. The system uses rule-based assertion extraction (focused on variable constraints such as ranges and negative allowance) and triggers a “consistency mode” upon conflict, resulting in re-tagged, cautious outputs that gently prompt users for re-definition rather than overconfident resolution. Key features include strict example-skipping heuristics and OR-gate final gating. The framework remains proprietary and is not disclosed in full detail due to upcoming commercial application. We conducted informal experiments by feeding increasingly complex contradiction inputs to both standard Grok (normal mode) and a consistency-enforced instance. Results show a clear behavioral divergence: standard responses provide direct logical critique, while consistency mode yields softened, self-aware replies emphasizing epistemic humility (e.g., “the value becomes undefined… let’s redefine it together ♡”). This illustrates how lightweight consistency enforcement can shift LLM interaction toward collaborative refinement.