Beyond Black Boxes: PerceptionLM Delivers Open, Reproducible Vision AI for Deep UnderstandingJenray·Follow14 min read·Just now--Explore PerceptionLM (PLM), Meta AI’s groundbreaking open-source Vision-Language Model. Learn about its transparent data, reproducible training, and new benchmarks tackling the limitations of closed-AI for detailed image and video understanding.The field of Artificial Intelligence is witnessing a revolution, particularly in how machines perceive and understand the visual world. Vision-Language Models (VLMs) — systems that can process and reason about both images/videos and text — are becoming increasingly powerful, enabling applications from advanced image search and video summarization to interactive assistants and robotics. We see headlines about models generating stunning images from text or answering complex questions about video content.However, a significant shadow hangs over this progress: the “black box” problem. Many of the highest-performing VLMs are closed-source. Their architectural details, the massive datasets they’re trained on, and the specific recipes used to train them remain corporate secrets. While these models achieve impressive benchmark scores, this secrecy hinders genuine scientific advancement. How can we know if performance gains come from novel techniques or simply from training on evaluation data? How can the broader research community build upon, verify, or even fairly compare against these opaque systems?