The paper investigates how new concepts can be integrated into unified multimodal models (UMMs) by separating generation and understanding objectives through a novel visual entity bound to a single task direction. Experiments show that the effectiveness of cross‑task usability depends on where the concept is injected into the shared computation, with a mid‑stack alignment objective achieving high concept acquisition with minimal loss to overall performance. The study highlights that unified weights alone are insufficient; the two directions must share a semantic format at the entry point for efficient concept integration.
By Zongyang Qiu, Yihan Wu, Kaixuan Fan, Bo Li, Hui Xiong
JUICE (Joint Unified multimodal modeling In Continuous Encoder spaces) is a single flow‑matching Transformer that jointly models images and text in pretrained continuous encoder spaces, allowing each modality to be generated by the same process with its own noise level. The model can perform tasks such as image generation, captioning, visual question answering, and joint generation by deciding which modalities are clean and which are generated. Experiments show that joint and conditional denoising together outperform either alone, and that JUICE achieves strong performance on GenEval, VQAv2, and visual spatial planning tasks, even enabling coupled sampling where both modalities are generated simultaneously.
By Jiahan Li, Huangyuan Su, Shengbang Tong, Shusheng Yang, Ruihan Guo, Suozhi Huang, Hexu Zhao, Pratyusha Sharma, Sherry Yang, Jiatao Gu, Rob Fergus, Kyunghyun Cho
The paper investigates how many transformer components influence a token prediction by measuring the absolute contribution of each unit and channel to the logit. It finds that thousands of components contribute to a single prediction, yet a small subset—often just dozens—carries the majority of the predictive mass. Across models ranging from 124 M to 7 B parameters, the proportion of the model involved in a prediction remains around one to three percent, independent of size, and the study demonstrates that specific components can be directly read and written to modify model behavior without additional training.
By Mark Oskin
arXiv:2608. 10251v1 Announce Type: cross Abstract: A transformer's answer lives on one axis: the direction its unembedding reads.
By Mark Oskin
arXiv:2610. 17117v2 Announce Type: replace-cross Abstract: Prior research into interpretability does not test whether a representation stays consistent under paraphrasing.
By Bryce Grant, Peng Wang
arXiv:2610.22796v1 Announce Type: new
Abstract: Reusing a Transformer block across depth increases computation without replicating its parameters, but successive passes have no dedicated state throug...
By Ilia Zaznov