Remember the Lab: Adaptive Visual Abstraction for Long-Horizon Laboratory Understanding

Modern laboratory research involves long-horizon experimental processes, where states, procedures, and outcomes continuously evolve across repeated experiments. With the emergence of multimodal foundation models, AI assistants have shown great potential for laboratory research. However, effective assistance in dynamic laboratory environments requires the ability to maintain and utilize knowledge accumulated throughout experimental processes. Existing multimodal systems mainly focus on current observations, limiting their ability to reason over evolving laboratory processes across long temporal horizons. We introduce RA-Lab, a multimodal memory-augmented agent framework for long-horizon laboratory video understanding. RA-Lab constructs persistent experimental memories through two key components: Adaptive Visual Abstraction, which transforms raw observations into experience-level visual memories, and Targeted Multimodal Retrieval, which retrieves relevant historical experiences based on the current observation and query. The retrieved memories are combined with current observations to support memory-augmented laboratory reasoning. To evaluate long-horizon laboratory understanding, we introduce LabVQA, a benchmark for laboratory video reasoning over experimental procedures, states, and temporal relationships. Extensive experiments demonstrate that RA-Lab effectively constructs and utilizes multimodal experimental memories, improving long-horizon laboratory reasoning compared with existing multimodal models and memory-augmented agents.