Inference-Time Steering

Memory is a useful product feature until it becomes a junk drawer. That is the quiet problem behind many “personalized” AI systems. A user has a history. The system retrieves some of it. The model receives a longer prompt. The output becomes, in theory, more personal. In practice, the assistant often behaves like someone who read your old emails in a hurry and decided this was the same as knowing you. ...

TL;DR for operators GRAINS is not “fine-tuning, but cheaper.” That framing misses the point and commits the usual business sin of turning a mechanism into a procurement slogan. The paper’s useful claim is more specific: token-level attribution can be converted into an inference-time steering signal. Instead of retraining model weights, GrAInS identifies which text or image tokens most strongly push the model toward preferred or dispreferred outputs, builds layer-wise steering vectors from those activation shifts, and applies normalized edits during inference.1 ...