Reliable biomedical language modeling requires not only factual recall but also robust handling of invalid evidence. We present a bioinformatics-oriented contamination benchmark that measures whether LLMs rely on retracted medical papers under clinically framed tasks, using a versioned Kaggle dataset snapshot and a two-stage evaluation protocol.
This revision keeps the Trojan Paper Medical Benchmark workflow and updates metric presentation to ensure formulas are readable in web rendering, while preserving the same web-first retraction discovery and contamination-evaluation protocol.
Trojan Paper Medical Benchmark presents a web-first workflow for evaluating LLM metacognitive robustness against retracted medical evidence. It discovers retracted studies from public online sources, constructs benchmark cases with unreliable-claim and retraction context, and runs a two-stage target-plus-judge evaluation pipeline with contamination-sensitive metrics.