Large language models (LLMs) are largely motivated by their performance on popular topics and benchmarks at the time of their release. However, over time, contamination occurs due to significant exposure of benchmark data during training. This poses a risk of model performance inflation if testing is not carefully executed. To address this challenge, we present GRAFITE, a continuous LLM evaluation platform through a comprehensive system for maintaining and evaluating model issues. Our approach enables building a repository of model problems based on user feedback over time and offers a pipeline for assessing LLMs against these issues through quality assurance (QA) tests using LLM-as-a-judge. The platform enables side-by-side comparison of multiple models, facilitating regression detection across different releases. The platform is available at https://github.com/IBM/grafite. The demo video is available at www.youtube.com/watch?v=XFZyoleN56k.
@article{arxiv.2603.18173,
title = {GRAFITE: Generative Regression Analysis Framework for Issue Tracking and Evaluation},
author = {Ja Young Lee and Mírian Silva and Mohamed Nasr and Shonda Witherspoon and Enzo Bozzani and Veronique Demers and Radha Ratnaparkhi and Hui Wu and Sara Rosenthal},
journal= {arXiv preprint arXiv:2603.18173},
year = {2026}
}