PosterSentry: A Lightweight Multimodal Classifier for Scientific Poster Quality Assurance in Open Repositories

Scientific posters are one of the most common ways researchers share results, with millions presented every year. They hold rich, early findings, yet are rarely shared or reused afterward. The Posters.science project builds an index of FAIR (Findable, Accessible, Interoperable, Reusable) posters by pulling machine-actionable metadata from PDFs in open-access repositories. PosterSentry is a lightweight, open-source multimodal classifier that decides whether a PDF is a true scientific poster by combining three feature streams: static text embeddings, visual features, and structural cues. It is designed to be small and fast, with a 10 KB model head and about 60 MB embedding backbone, and can classify PDFs in under 1 second per file on CPU. PosterSentry is the cheap gate at the front of the pipeline, run before costly LLM-based metadata extraction downstream.