Pipeline diagnostician. LLMs could help identify the root causes of irreproducible results when paired with multi-sensors equipped robots or drones. In the future, an ideal experimentation paradigm is to log the lifetime recording and all the metadata of every sample. When an inexplicable phenomenon occurs, all related logs can be input into the multi-modal LLMs for analysis. Leveraging its superior hypothesis generation ability, the model can propose a list of potential causes, allowing human experts to further investigate the top few that they deem likely. This approach could also be applied in industrial processing pipelinesif a significant drop in the production yield is noticed, LLMs can be employed to identify the "culprit", with human engineers stepping in when complex realworld adjustments are required. This role becomes viable when LLMs can process vast amounts of images (videos), and will be further enhanced when multimodal information (vendor-provided metadata for samples, moisture sensing, sound sensing, etc.) gets well aligned with the visual information.