The paper introduces a method to retrofit depth recurrence into pretrained vision‑language models, reducing the language backbone’s parameter count by pruning layers and looping over the remaining ones with linear adapters. A three‑stage recovery training—language‑only pretraining, vision‑language projector alignment, and full fine‑tuning—helps recover performance, with distillation from the non‑recurrent model further improving results. Experiments on TinyLLaVA‑1.4B show the retrofitted recurrent model outperforms the unpruned baseline while saving 23.3% of language parameters and performs better on reasoning‑oriented benchmarks.
By Madhav Iyengar, Andreas Geiger, Hilde K\"uhne
LoopLook introduces a lightweight encoder-side recurrent perception framework that equips pretrained Vision‑Language Models with adaptive visual depth. By repeatedly revisiting a middle‑to‑late window of the vision encoder and injecting question representations at each loop, it refines visual evidence in a task‑directed manner. The learned stopping mechanism and token‑wise Mixture‑of‑Looks allow each visual token to selectively integrate information across loops, yielding consistent performance gains across multiple backbones and benchmarks.
By Wencheng Ye, Anning Hu, Hongye Fang, Tianyi Wang, Junchi Yan
arXiv:2603. 11211v3 Announce Type: replace-cross Abstract: Incremental Learning (IL) aims to learn new tasks while preserving previously acquired knowledge.
By Haihua Luo, Xuming Ran, Jiangrong Shen, Timo H\"am\"al\"ainen, Zhonghua Chen, Qi Xu, Fengyu Cong
arXiv:2606.21562v2 Announce Type: replace
Abstract: Transformers are AI's workhorse but their computational cost becomes prohibitive when processing long sequences. We target long-horizon streaming v...
By Philippe Weinzaepfel, Christian Wolf, Mert B\"ulent Sariyildiz, Guillaume Bono, Gianluca Monaci
MLLMCLIP introduces a heterogeneous distillation framework that transfers multimodal knowledge from a generative Multimodal Large Language Model (MLLM) teacher directly into a discriminative CLIP student, eliminating the need for synthetic hard negatives. The method uses an attention-based per-layer token selection and a CKA-based distillation loss to bridge architectural differences between the two models. As a result, MLLMCLIP achieves state‑of‑the‑art compositional accuracy and improves zero‑shot classification and image‑text retrieval performance.
By Jongsuk Kim, Qiyu Wu, Zhuoyuan Mao, Hiromi Wakaki, Junmo Kim, Yuki Mitsufuji
The paper introduces FPCD, a framework that stabilizes the modality gap in vision‑language models during continual learning. It combines Fast Null‑space Projection, which projects gradients onto approximate null spaces to reduce interference, with Contrastive Distillation that aligns image‑to‑text and text‑to‑image similarity distributions using a small reference set. On the 11‑task MTIL benchmark, FPCD achieves strong performance, maintaining stable modality gaps and superior zero‑shot retrieval on COCO Captions and Flickr30k.
By Tiantian Peng, Yuyang Liu, Shuo Yang, Qiuhe Hong, YongHong Tian