The 14-Step FOSS Gate: Deterministic Validation for Reliable LLM-Based Causal Knowledge Extraction

Preprint intended for submission to NeurIPS 2026 (Thirty-Ninth Conference on Neural Information Processing Systems). Large Language Models produce stochastic outputs that undermine reproducibility in knowledge extraction tasks. This paper presents the 14-Step FOSS Hallucination Gate, a deterministic post-processing framework that transforms unreliableLLM outputs into consistent, validated causal triplets. Key Results: • 88% precision on DocRED (2,614 triplets, IAA-validated, Fleiss' κ=0.47) • 100% byte-level determinism (SIEB benchmark: 150 extractions) • Multi-model validation: 100% SIEB across Qwen-8B, Gemma-2B, Llama-3B • 2.5× improvement over naive baselines on complex documents (p < 0.001) • 4,355 validated triplets from 1,997 documents in 7.1 hours • Consumer hardware: Mac mini M4 (16GB RAM) The key contribution: reliability emerges not from better models, but from deterministic validation architecture. This package contains the paper, all experimental data, benchmark results, and reproducibility scripts.