Multimodal models

Vision-language models, speech and cross-modal systems that read, look and listen in the same forward pass.

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arXiv AI

Jun 30

DOPD: Dual On-policy Distillation

arXiv:2606. 30626v1 Announce Type: new Abstract: On-policy distillation (OPD) offers superior capacity transfer by supervising student-sampled trajectories with dense token-level signals.

By Xinlei Yu, Gen Li, Qingyi Si, Guibin Zhang, Yuqi Xu, Congcong Wang, Shuai Dong, Kaiwen Tuo, Xiangyu Zeng, Kaituo Feng, Qunzhong Wang, Yang Shi, Xiaobin Hu, Xiangyu Yue, Jiaqi Wang, Shuicheng Yan

llms efficiency multimodal

arXiv AI

Jun 30

Few-Shot Domain Incremental Learning via Continual Vision-Language Consolidation

arXiv:2606. 30190v1 Announce Type: cross Abstract: Existing domain-incremental learning (DIL) strategies call for massive amounts of data to adapt to new domains and suffer from the overfitting problem in the case of data scarcity.

By Naeem Paeedeh, Mahardhika Pratama, Wolfgang Mayer, Mukesh Prasad, Weiping Ding, Yew-Soon Ong

llms diffusion fine-tuning multimodal benchmarks

arXiv AI

Jun 30

Automating the Design of Embodied AgentArchitectures

arXiv:2606. 30111v1 Announce Type: cross Abstract: Embodied agents are typically built as hand-designed compositions of perception, memory, planning, and action modules.

By Jian Zhou, Sihao Lin, Jin Li, Shuai Fu, Gengze Zhou, Qi Wu

agents nlp robotics efficiency multimodal

arXiv AI

Jun 30

ManimAgent: Self-Evolving Multimodal Agents for Visual Education

arXiv:2606. 30296v1 Announce Type: new Abstract: Multi-round reflection lets agents built on large language models recover from failures within a single task, but each task remains an isolated episode: lessons learned across many reflection rounds on one task are discarded before the next begins.

By Wenjia Jiang, Zongyuan Cai, Yuanhang Shao, Chenru Wang, Boyan Han, Zhixue Song, Keyu Chen, Shengwei An, Xu Yang, Zhou Yang

llms rag agents multimodal

arXiv AI

Jun 30

LWDrive: Layer-Wise World-Model-Guided Vision-Language Model Planning for Autonomous Driving

arXiv:2606. 29879v1 Announce Type: cross Abstract: Vision-Language Models (VLMs) provide powerful semantic understanding and commonsense reasoning for End-to-End Autonomous Driving (E2E-AD) planning.

By Chen Yang, Yuhao Wei, Ze Xu, Ziheng Zou, Shuang Liang, Delin Ouyang, Lingfeng Qi, Jie Li, Guofa Li

llms agents robotics multimodal benchmarks

arXiv AI

Jun 30

FADE: Mitigating Hallucinations by Reducing Language-Prior Dominance in Large Vision-Language Models

arXiv:2606. 29431v1 Announce Type: new Abstract: Despite the impressive capabilities of Large Vision-Language Models (LVLMs), they remain susceptible to hallucination, generating content inconsistent with the input image.

By Yichen Guo, Kai Tang, Fenglai Lin, Yiding Sun, Dongshuo Zhang, Wenya Wang, Lin William Cong, Shanghang Zhang

llms multimodal benchmarks safety

arXiv Machine Learning

Jun 30

Room Scene Discovery and Grouping in Unstructured Vacation Rental Image Collections

arXiv:2507. 00263v2 Announce Type: replace-cross Abstract: The rapid growth of vacation rental (VR) platforms has led to an increasing volume of property images, often uploaded without structured categorization.

By Vignesh Ram Nithin Kappagantula, Shayan Hassantabar

llms rag multimodal

arXiv AI

Jun 30

Unlocking the Visual Record of Materials Science: A Large-Scale Multimodal Dataset from Scientific Literature

arXiv:2606. 29667v1 Announce Type: cross Abstract: The materials science literature encodes decades of experimental knowledge in figures, yet this visual record remains locked away and inaccessible to AI at scale.

By Subham Ghosh, Shubham Tiwari, Mohammad Ibrahim, Abhishek Tewari

llms fine-tuning multimodal safety

arXiv Machine Learning

Jun 30

Symbiotic-MoE: Unlocking the Synergy between Generation and Understanding

arXiv:2604. 07753v2 Announce Type: replace-cross Abstract: Empowering Large Multimodal Models (LMMs) with image generation often leads to catastrophic forgetting in understanding tasks due to severe gradient conflicts.

By Xiangyue Liu, Zijian Zhang, Miles Yang, Zhao Zhong, Liefeng Bo, Ping Tan

llms diffusion multimodal benchmarks

arXiv Machine Learning

Jun 30

BrainJanus: A Unified Model for Understanding and Generation across Brain, Vision, and Language

arXiv:2606. 30319v1 Announce Type: cross Abstract: Modeling the bidirectional correspondence between external sensory stimuli and internal neural activity has emerged as a critical frontier in neuroscience.

By Haitao Wu, Qirui Zhang, Zhouheng Yao, Shangquan Sun, Qihao Zheng, Mianxin Liu, Chi Zhang, Wanli Ouyang, Chunfeng Song, Changqing Zhang, Jiamin Wu

nlp multimodal benchmarks safety

arXiv Machine Learning

Jun 30

A Diagnostic Framework and Multi-Evaluator Audit of Evaluator-Driven Preference Dynamics in Self-Adapting LLM Agents

arXiv:2606. 29719v1 Announce Type: new Abstract: Measurements of proprietary LLM evaluators can become invalid within weeks -- we document one case and provide the diagnostic framework to detect it.

By Liu Zewen

llms agents multimodal benchmarks

arXiv AI

Jun 30

Feature-level Interaction Explanations in Multimodal Transformers

arXiv:2603. 13326v2 Announce Type: replace-cross Abstract: Multimodal Transformers often produce predictions without clarifying how different modalities jointly support a decision.

By Yeji Kim, Housam Khalifa Bashier Babiker, Mi-Young Kim, Randy Goebel

llms multimodal benchmarks

arXiv AI

Jun 30

SA-VLA: State-aware tokenizer for improving Vision-Language-Action Models' performance

arXiv:2606. 30113v1 Announce Type: cross Abstract: Discrete action tokenization provides a compact interface for autoregressive VLA policies, but accurately recovering continuous robot actions from discrete codes remains challenging.

By Tengyue Jiang, Chunpu Xu, Jiayue Kang, Yao Mu

llms nlp robotics multimodal

arXiv AI

Jun 30

EfficientUICoder: A Bidirectional Token Compression Framework for Efficient MLLM-Based UI Code Generation

arXiv:2509. 12159v2 Announce Type: replace-cross Abstract: Multimodal Large Language Models have demonstrated exceptional performance in UI2Code tasks, significantly enhancing website development efficiency.

By Jingyu Xiao, Zhongyi Zhang, Yuxuan Wan, Yintong Huo, Yang Liu, Michael R. Lyu

llms multimodal

arXiv AI

Jun 30

Beyond SFT-to-RL: Pre-alignment via Black-Box On-Policy Distillation for Multimodal RL

arXiv:2604. 28123v3 Announce Type: replace-cross Abstract: The standard post-training recipe for large multimodal models (LMMs) applies supervised fine-tuning (SFT) on curated demonstrations followed by reinforcement learning with verifiable rewards (RLVR).

By Sudong Wang, Weiquan Huang, Xiaomin Yu, Zuhao Yang, Hehai Lin, Keming Wu, Chaojun Xiao, Chen Chen, Wenxuan Wang, Beier Zhu, Yunjian Zhang, Chengwei Qin

llms reinforcement-learning fine-tuning efficiency multimodal benchmarks safety

arXiv Machine Learning

Jun 30

Same Concept, Different Directions: Cross-Modal Feature Heterogeneity in Sparse Autoencoders

arXiv:2606. 29888v1 Announce Type: new Abstract: Vision-language models map images and text into a joint embedding space.

By Chungpa Lee, Jihoon Kwon, Kyle Min, Jy-yong Sohn

llms rag multimodal safety

arXiv Machine Learning

Jun 30

MemLeak: Diagnosing Information Leaks in Multimodal Agent Memory

arXiv:2606. 29788v1 Announce Type: new Abstract: When a multimodal AI agent is asked to forget a fact, current memory systems usually delete the text entry and report success.

By Kuan Wang, Chao Zhang

agents multimodal benchmarks

arXiv AI

Jun 30

Steerable Visual Representations

arXiv:2604. 02327v2 Announce Type: replace-cross Abstract: Pretrained Vision Transformers (ViTs) such as DINOv2 and MAE provide generic image features that can be applied to a variety of downstream tasks such as retrieval, classification, and segmentation.

By Jona Ruthardt, Manu Gaur, Deva Ramanan, Makarand Tapaswi, Yuki M. Asano

llms computer-vision multimodal benchmarks

arXiv AI

Jun 30

Data-Efficient Multimodal Alignment for Histopathology-based Molecular Prediction

arXiv:2606. 29949v1 Announce Type: cross Abstract: H&E-stained whole-slide images offer cohort-scale availability and rich spatial context but lack molecular specificity, whereas bulk RNA-seq provides transcriptome-wide resolution at high cost with limited archival availability.

By Dominik Winter, Dominik Vonficht, Lo\"ic Le Bescond, Christian Gebbe, Marco Rosati, Richard J. Chen, Markus Schick, Ross Stewart, Nicolas Brieu

llms fine-tuning multimodal safety

arXiv AI

Jun 30

VLK: Learning Humanoid Loco-Manipulation from Synthetic Interactions in Reconstructed Scenes

arXiv:2606. 30645v1 Announce Type: cross Abstract: Perception-based humanoid loco-manipulation requires connecting egocentric observations and task instructions to whole-body motion.

By Yen-Jen Wang, Jiaman Li, Sirui Chen, Takara E. Truong, Pei Xu, Pieter Abbeel, Rocky Duan, Koushil Sreenath, Angjoo Kanazawa, Carmelo Sferrazza, Guanya Shi, Karen Liu

robotics multimodal

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