A revised arXiv paper introduces CRAMER, a technique for controlling sequential recommendation models through request-aware masking. The approach aims to let such models respond more flexibly to users' immediate, time-sensitive requests rather than only their long-term behavior patterns. The paper appears on arXiv's cs.AI and cs.LG listings as a cross-list replacement.
A new arXiv paper examines what happens when human corrections are applied only to identified editable spans of a model's output. The authors decompose the localized gradient into edited and untouched portions at a fixed checkpoint, showing that selective feedback channels can amplify relative selection bias. They also study gradient geometry, target mismatch, and importance weighting as factors in this effect.
A research paper presents a method for computing exact local responses to attention interventions in transformer models. Using the derivative of rotary position embeddings, candidate edits can be scored from a cached baseline plus a single backward pass, avoiding full recomputation. The approach aims to make attention-level analysis and editing more computationally efficient.
A newly posted preprint investigates how many components inside a transformer actually determine a given token prediction, measuring the signed contributions of individual units and channels to the final logits. It reports that a single output can depend on anywhere from thousands to hundreds of thousands of components, whose effects partly offset one another. The work, titled Through the Looking Glass, presents methods for directly reading out and editing transformer internals.