Technique
Generative.
Producing text, images or structured content as output, and the quality loop that has to sit around it before any of it reaches a customer.
3 papers · Technique
Latent distillation needs no shared grid
Distilling a vision encoder into an autoencoder bottleneck makes diffusion converge faster, and everyone did it by matching each latent cell to the teacher cell above it. Adrien Ramanana Rahary and colleagues show that correspondence is unnecessary: one pooled descriptor per image matches it, 5.77 gFID against 6.04. The margin is inside seed noise and the authors say so. The result is not a better score, it is that distillation now reaches 1D latents and text teachers, where the position wise method cannot run at all.
Read the paper →- September 2026OperationsAGA-12
EU AI Act 2026: what businesses need to know
A practical EU AI Act compliance guide for businesses, current as of September 2026: what already applies after the Digital Omnibus amendments, what lands in December 2027 and August 2028, whether you are a provider or a deployer, what counts as high risk AI, the fines, and a checklist you can run this quarter.
Luis Hasanaj · Read → - July 2026OperationsAGA-07
Catalog enrichment: generating localized product content at scale
A pipeline that turns a bare product image into a full, locale-aware catalog entry: categorized attributes, translated copy, culturally-appropriate imagery and an interactive 3D asset, with a vision-language reflection loop scoring every output before it ships.
Luis Hasanaj · Read →