contradiction-detection

RegDivergence-101 is a pilot benchmark for detecting cross-jurisdiction regulatory contradictions between FDA and EMA using LLMs, establishing baseline methods with varying performance in classifying regulatory relationships.

The article argues that vector databases alone act as data graveyards without true memory capabilities, and describes building a local AI memory layer with contradiction detection and forgetting cycles on top of Actian VectorAI DB.

Introduces BioDivergence, a benchmark and evaluation framework for detecting context-conditioned contradictions in biomedical abstracts, featuring a six-class conflict taxonomy and a silver dataset of 11,865 claim pairs.