LUAR MUD is a text embedding model focused on authorship representation — it encodes writing samples into vector spaces where stylistic identity clusters together. It's built for tasks where *who* wrote something matters as much as *what* was written, making it useful for authorship verification and attribution workflows. The open-weight release means you can run it locally and fine-tune it for domain-specific corpora.