The Pre-Flight Check for Autonomous AI: Zero-Model Structural Reasoning Validation at Scale

We present the Subtractive Filter, a lightweight, model-free reasoning integrity validator for Large Language Model (LLM) outputs. Operating on deterministic pattern matching, it requires zero model inference and achieves 82,544 samples/second throughput. Evaluated on 58,293 samples from the HaluEval benchmark, the filter demonstrates 91.3% F1 on structural reasoning failures (contradictions, circular logic) while scoring 4.0% F1 on factual hallucinations. This paper argues for pre-execution structural reasoning validation as a critical, distinct layer in AI safety, analogous to aviation pre-flight checks.