03 [RAG] Attention Heads That Reach Back to Earlier Layers A new attention mechanism called MoDA lets each attention head pull information from different depths of the network, not just the most recent layer. Deep AI models tend to “forget” useful signals computed early on as information passes through dozens of layers of processing, and fixing this without redesigning the whole architecture is genuinely tricky. Models that better preserve early-layer signals could be more accurate and efficient across tasks like document retrieval, reasoning, and summarization — without needing to be made even larger. link
04 [Code] Teaching AI to Actually Use Unfamiliar Code Libraries Most coding AI tools struggle when asked to write code using private or internal libraries they’ve never seen before — knowing an API exists isn’t the same as knowing how to use it correctly. Simply showing a model the documentation at query time turns out to be surprisingly ineffective, because reading a reference and reasoning through how to apply it are fundamentally different skills. This matters for any company with internal codebases, where today’s AI coding assistants are largely blind and unhelpful. link
05 [RAG] CT scan AI bottleneck fixed with smarter retrieval method Medical AI that writes radiology reports from 3D CT scans was missing important findings because it was cramming all the visual information into just 2 usable dimensions out of 512 available — a massive waste of capacity that caused blind spots. Adding a retrieval step that pulls in similar past cases at report-generation time sidesteps this compression problem without needing to retrain the underlying image model. Radiologists and patients could benefit from AI reports that catch more conditions the first time, reducing missed diagnoses in busy imaging departments. link