What question did this study set out to answer?

To investigate a lightweight method for validating reasoning integrity in AI outputs without model inference.

February 19, 20260 citationsOpen Access

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

Key Points

To investigate a lightweight method for validating reasoning integrity in AI outputs without model inference.
Developed the Subtractive Filter for reasoning validation
Assessed its performance using the HaluEval benchmark
Analyzed 58,293 samples for structural reasoning failures and factual errors
Achieved 91.3% F1 score on structural reasoning failures
Obtained 4.0% F1 score on factual hallucinations
Processed 82,544 samples per second

Abstract

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.

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