Developer Builds Epistemic Gate to Block LLM Data Poisoning During Fine-Tuning

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A developer completed a 17-day project aimed at preventing data poisoning in large language model fine-tuning, building what they call an epistemic gate across a series of experiments (EXP01–EXP16) run on a 2006 Toshiba laptop. Early experiments revealed that penalizing false training data in absolute terms caused loss divergence, leading to the adoption of a Softplus-based contrast function that penalizes falsehood only relative to the truth. The gate evolved from a dictionary-based lookup to a semantic vector approach, which increased the detection margin roughly 15-fold and reduced noise compared to lexical matching. A software bug in EXP04 accidentally produced results that later informed the project's definitive architecture, named Beatriz, which combined rewriting on true anchors with bounded epistemic contrast. The developer reports that LoRA, not a DPO-style reference loss, ultimately resolved perplexity explosion, and plans to publish results from the remaining experiments shortly.

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