TL;DR for operators Belmadani et al. study a question every serious enterprise LLM team eventually meets after the prototype stops looking magical: which adaptation bill is actually worth paying?1 In French medical question answering, they compare continual pretraining (CPT), supervised fine-tuning (SFT), and CPT followed by SFT across Gemma, Mistral, and Llama-family models, with general, instruction-tuned, and medical initializations. ...
TL;DR for operators Food-image nutrition AI is usually sold as a vision problem: recognise the meal, estimate the portion, output the nutrients, preferably with a pleasant progress spinner. NutriMLLM suggests that this is only half right. The harder missing piece is not necessarily seeing the food. It is knowing the full nutrient profile once the food is identified. ...
Noise is easy to add. Information is rather less cooperative. A high-resolution camera image can be blurred. A precise sensor reading can be contaminated with noise. A complete genetic record can be reduced to a coarser code. Reversing any of those operations is much harder, because the missing information has already left the building. ...
A customer takes a blurry photo of a sneaker under kitchen lighting. The retailer’s catalog contains polished studio images, clean backgrounds, perfect angles, and the calm confidence of professional photography. The retrieval engine is asked to connect these two worlds. This is where many visual retrieval systems quietly lose money. The technical name is domain adaptive retrieval: learning compact hash codes so that images from one domain can be retrieved meaningfully using queries from another. The practical version is less elegant. Product photos do not look like user photos. Scanned forms do not look like native PDFs. Medical images vary across machines. Warehouse images vary across cameras, lighting, and dirt. Reality has terrible styling. ...