The paper introduces TA-SPA, a black‑box jailbreak method for multimodal large language models that generates transferable perturbations in a text‑anchored semantic space. It combines Text‑Anchored Semantic Factorization (TASF) to separate cross‑modal semantic factors from modality‑specific residuals with Semantic‑Preserving Augmentation (SPA) to diversify harmful target anchors while maintaining semantic consistency. Experiments demonstrate strong attack effectiveness and transferability to commercial MLLMs, with competitive performance against representative defenses.
By Wenyun Li, Guiping Cao, Xiangyuan Lan, Zheng Zhang
The paper introduces Hierarchical Exponential-Gaussian Mixture (HEGM) models to predict watch-time distributions for short-video recommendations, addressing issues such as variance collapse, component redundancy, and inactive components found in the prior Exponential-Gaussian Mixture Network (EGMN). HEGM employs a hierarchical skip-watch decomposition, KL-based variance regularization, structured initialization, and removes certain regularizers to improve mixture stability and interpretability. Experiments on public and large-scale industrial datasets show that HEGM enhances ranking accuracy and threshold-event prediction while maintaining competitive point-estimation accuracy, and a 1.5‑month production A/B test demonstrates statistically significant engagement lifts.
By Sofia Gulevskaia, Mikhail Trapeznikov, Aleksandr Poslavsky, Alexander D'yakonov
arXiv:2608.22429v1 Announce Type: new
Abstract: Multimodal Large Language Models (MLLMs) capable of thinking with images often rely on external tools for fine-grained perception. However, this relian...
By Changjiang Jiang, Qiannian Zhao, Lei Xin, Jinxiang Xie, Preslav Nakov, Zhuohan Xie
arXiv:2608.21853v1 Announce Type: new
Abstract: Large language models are increasingly moving beyond text processing, adding support for other modalities such as images and audio. While text understa...
By S{\l}awomir Dadas, Micha{\l} Pere{\l}kiewicz, Rafa{\l} Po\'swiata, Ma{\l}gorzata Gr\k{e}bowiec, Bart{\l}omiej Jaworski, Izabela Wo\'zniakowska
arXiv:2608.21415v1 Announce Type: cross
Abstract: Large Vision-Language Models (LVLMs) have achieved remarkable performance across a wide range of tasks; however, they often inherit social biases fro...
By Yisong Xiao, Aishan Liu, Yongxin Huang, Zonghao Ying, Shiji Zhao, Tianlin Li, Yong Han, Jian Yang, Xianglong Liu
DRAgent introduces a discriminative reasoning framework for Referring Expression Segmentation that replaces direct coordinate prediction with a two‑stage selection process. First, a detector generates candidate boxes; then a multimodal large language model (MLLM) discriminates the correct target through high‑recall screening and instance‑wise verification. The chosen box serves as a spatial prompt for a foundation segmentation model, and a self‑consistency‑filtered data pipeline is used for LoRA‑based fine‑tuning, yielding competitive results on RefCOCO, RefCOCO+, and RefCOCOg.
By Yujie Qi, Luyan Zhang
arXiv:2604.08884v2 Announce Type: replace-cross
Abstract: Multimodal Large Language Models (MLLMs) have achieved strong performance on RGB image understanding, yet their ability to use spectral evide...
By Xinyu Zhang, Zurong Mai, Qingmei Li, Xiaoya Fan, Zjin Liao, Haoyuan Liang, Yibin Wen, Yuhang Chen, Chan Tsz Ho, Bi Tianyuan, Ruifeng Su, Zihao Qiang, Juepeng Zheng, Jianxi Huang, Yutong Lu, Haohuan Fu
arXiv:2512.01946v4 Announce Type: replace-cross
Abstract: Robust robotic manipulation requires reliable failure detection and recovery. Although recent Vision-Language Models (VLMs) show promise in r...
By Paul Pacaud, Ricardo Garcia, Shizhe Chen, Cordelia Schmid
arXiv:2606.11269v2 Announce Type: replace
Abstract: Personality assessment aims to infer stable traits from dynamic behaviors across modalities like language, voice, and facial expressions. Existing...
By Jia Li, Qian Chen, Wei Wang, Xinyu Li, Zhenzhen Hu, Dongsheng Shao, Richang Hong, Meng Wang
arXiv:2608.21810v1 Announce Type: cross
Abstract: Clinical decision-making is inherently experience-driven: physicians progressively refine their reasoning by synthesizing patient history, multimodal...
By Md Asaduzzaman Jabin, Khoa Le, Lin Zhao, Tianming Liu
arXiv:2608.23330v1 Announce Type: new
Abstract: Video understanding requires intelligent agents to transcend mere recognition of visual facts and comprehend the underlying intents behind human action...
By Jiapeng Li, Ping Wei, Wenjuan Han, Song-Chun Zhu, Lifeng Fan
arXiv:2608.23074v1 Announce Type: new
Abstract: Vision-language models (VLMs) can answer spatial questions, yet the mechanisms connecting object grounding to spatial reasoning remain poorly understoo...
By Xiwei Liu, Yulong Li, Xinlin Zhuang, Xuhui Li, Zhixiang Lu, Haolin Yang, Imran Razzak, Yutong Xie
arXiv:2608.23189v1 Announce Type: new
Abstract: We present EchoWM, an omnimodal world model for enterable generative media that responds to continuous navigation while jointly generating 720p video,...
By Songchun Zhang, Yaowei Li, Junhao Zhuang, Weiyang Jin, Haoyu Wang, Xin Lu, Yilang Sun, Shiyi Zhang, Haoran Li, Xiaoxiao Ma, Yuming Li, Yijun Liu, Yaofeng Su, Yanwen Ma, Haoyu Wu, Zihan Su, Yue Ma, Lvmin Zhang, Haoyang Huang, Zeyue Xue, Anyi Rao, Nan Duan
arXiv:2608.22366v1 Announce Type: new
Abstract: Vision-language models (VLMs) are increasingly being used for document understanding, yet their role in Arabic and Islamic manuscript recognition remai...
By Moshiur Farazi, Firoj Alam, Abderrahmane Maaradji, Zakaria Maamar, Hamdy Mubarak, Wajdi Zaghouani
arXiv:2608.22323v1 Announce Type: new
Abstract: The application of Large Language Models (LLMs) to diagnostic decision-making has garnered growing interest. However, existing benchmarks largely focus...
By Lai Wei, Yuchao Chen, Zhenbiao Cao, Xiaojin Zhang, Zhongyu Wei, Bangting Wang, Wei Chen, Xiang Bai
arXiv:2608.21438v1 Announce Type: new
Abstract: Text guided 3D scene editing provides an intuitive interface for modifying reconstructed environments, but remains difficult because natural language d...
By Xiujin Liu, Tianyu Yang, Yilun Zhao, Xiangliang Zhang
arXiv:2608.21431v1 Announce Type: new
Abstract: Knowledge-based Visual Question Answering aims to answer questions about an image by integrating external knowledge with visual and textual information...
By Qiyou Liu, Yong Zhang, Jianjie Luo, Zhenguo Yang, Yi Yu
FOVEA introduces a cache‑friendly, on‑demand visual evidence adaptation for multimodal speculative decoding, enabling a lightweight draft model to dynamically retrieve a bounded subset of visual memory based on a cumulative‑mass rule. The retrieved visual readout is fused with the draft hidden state via a lightweight gated residual correction, avoiding the insertion of visual tokens into the autoregressive context. Experiments on various vision‑language backbones and benchmarks show that FOVEA improves draft acceptance and speeds up end‑to‑end decoding by up to 2.13× compared to traditional autoregressive decoding.
By Hengjie Zhu, Dayan Wu, Zihao Zhang, Xinze Liu, Jingxuan Yu, Peng Fu, Zheng Lin, Weiping Wang, Ding Wang
arXiv:2608.23329v1 Announce Type: cross
Abstract: Open-world video understanding often requires a model to locate sparse visual evidence and acquire external knowledge that is absent from the video a...
By Wenqi Liu, Shijie Ma, Yunxiao Wang, Meng Liu, Qile Su, Han Liu, Bohan Hou, Xuanyu Zheng, Changyi Liu, Tianke Zhang, Haonan Fan, Kaiyu Jiang, Yingxin Li, Jiankang Chen, Xu Wang, Bin Wen, Tingting Gao, Han Li, Jianhua Yin, Yinwei Wei, Xuemeng Song
arXiv:2608.22713v1 Announce Type: new
Abstract: Clinical diagnosis requires progressive integration of patient history, physical examination, laboratory findings, medical images, and diagnostic-infor...
By Yufan Wang, Rui Yang, Yi Liu, Yi Lin, Yifan Peng