Technique
Multimodal.
Models that read images, video and documents alongside text, and the architectures that let a system act on what it sees.
8 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 →- October 2026ResearchAGA-42
ViT-5, and what vision should not borrow from language
A team at Johns Hopkins and UC Santa Cruz swept the vision transformer component by component and found that SwiGLU, the gated block essentially every language model uses, is actively harmful in vision when paired with LayerScale. Not because either is wrong, but because both filter channels and stacking them suppresses too much. A reading of the full ablation, what else survives the transplant, and why a component validated alone is not validated.
Luis Hasanaj · Read → - October 2026ResearchAGA-53
Reward aggregation is an attack surface
Yu Yuan and colleagues found a reward hacking failure that no single reward permits: the weighted sum that combines them. Between training steps 35 and 40 their scalar reward rose from 1.445 to 1.973 while accuracy fell from 0.258 to 0.090, because format and length saturated as correctness collapsed. Paired with a multimodal study where adding a visual-grounding reward made hacking worse at every model scale, the argument is that each reward dimension you add is a new substitution the policy can make.
Luis Hasanaj · Read → - September 2026EngineeringAGA-35
Hardware-native AI: the alarm that sees
A position paper. The next operating system layer will be built into devices rather than shipped as an app on top of them, and home security is the clearest worked example: an alarm today is a deterrent that detects motion, while a camera that understands what it is looking at is a different category of thing. The interesting engineering is not the vision model. It is deciding how far up the response ladder a device may go without a person.
Luis Hasanaj · Read → - September 2026ResearchAGA-29
When the verifier moves inside the model
Four papers from one week in robotics, image generation and view synthesis all pay for an expensive correctness check during training and then delete it before inference. It is a real efficiency result and it removes the thing you would otherwise have logged. Reading DexAgent, InternW0-Delta, UMM-Reflection and GeoVerse together, and what disappears along with the verifier.
Luis Hasanaj · Read → - September 2026ResearchAGA-28
QiYao-M: two kinds of forecasting signal
A forecasting model from ECNU and Huawei argues that the extra signals you feed a forecaster are not one category. Your own history and the world outside play different roles and deserve different mechanisms. The headline gain over other foundation models is about one percent. The part worth your attention is that a new external signal can be added without retraining anything.
Luis Hasanaj · Read → - August 2026EngineeringAGA-08
The multimodal shopping assistant: an agentic architecture for open-ended discovery
An agentic architecture for a shopping assistant that reasons over text and images together, resolves references across a multi-turn conversation, and retrieves products from a retailer's own catalog, turning open-ended requests that keyword search fails into completed carts.
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 →