Multimodal models

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

4,615 stories · RSS feed

arXiv Machine Learning

Jun 11

Right Predictions, Misleading Explanations: On the Vulnerability of Vision-Language Model Explanations

arXiv:2605. 16651v2 Announce Type: replace-cross Abstract: Explanation mechanisms are increasingly used to support transparency and trust in vision-language models (VLMs), particularly in settings where model decisions require human oversight.

By Narges Babadi, Hadis Karimipour

llms multimodal safety

arXiv AI

Jun 11

Pretrained self-supervised speech models can recognize unseen consonants

arXiv:2606. 11542v1 Announce Type: cross Abstract: Modern pretrained self-supervised automatic speech recognition models are trained on large-scale audio data to encode speech into contextualized representations.

By Chihiro Taguchi, \'Eric Le Ferrand, Hirosi Nakagawa, Hitomi Ono, Kanji Kato, Emily Prud'hommeaux, David Chiang

fine-tuning multimodal

arXiv AI

Jun 11

Human-Guided Agentic AI for Multimodal Clinical Prediction: Lessons from the AgentDS Healthcare Benchmark

arXiv:2602. 19502v2 Announce Type: replace Abstract: Agentic AI systems are increasingly capable of autonomous data science workflows, yet clinical prediction tasks demand domain expertise that purely automated approaches struggle to provide.

By Lalitha Pranathi Pulavarthy, Raajitha Muthyala, Aravind V Kuruvikkattil, Zhenan Yin, Rashmita Kudamala, Saptarshi Purkayastha

agents multimodal benchmarks safety

arXiv Machine Learning

Jun 11

Visual-Redundancy-Controlled Parallel Decoding for Diffusion-Based Multimodal Large Language Models

arXiv:2605. 25820v2 Announce Type: replace Abstract: Diffusion-based multimodal large language models (dMLLMs) decode by iteratively predicting tokens at multiple masked positions in parallel.

By Yulin Yuan, Hongshuo Zhao, Xiangming Meng

llms diffusion multimodal benchmarks

arXiv AI

Jun 11

Ouroboros-Spatial: Closing the Data-Model Loop for Spatial Reasoning

arXiv:2606. 11719v1 Announce Type: cross Abstract: Spatial reasoning remains a persistent challenge for multimodal large language models (MLLMs).

By Enhan Zhao, Wei Wu, Yuanrui Zhang, Xueliang Zhao, Di He

llms fine-tuning multimodal benchmarks

arXiv AI

Jun 11

Embodied-R1.5: Evolving Physical Intelligence via Embodied Foundation Models

arXiv:2606. 11324v1 Announce Type: cross Abstract: We introduce Embodied-R1.

By Yifu Yuan, Yaoting Huang, Xianze Yao, Yutong Li, Shuoheng Zhang, Linqi Han, Pengyi Li, Jiangeng Sun, Wenting Jia, Zhao Zhang, Yuhao Liu, Ruihao Liao, Yucheng Hu, Qiyu Wu, Yuxiao Li, Zibin Dong, Fei Ni, Yan Zheng, Shuyang Gu, Yi Ma, Hongyao Tang, Han Hu, Jianye Hao

llms reinforcement-learning robotics fine-tuning multimodal benchmarks

arXiv AI

Jun 11

Metadata-Aware Multi-Prompt Reasoning for Zero-Shot Accident Understanding

arXiv:2606. 12047v1 Announce Type: cross Abstract: In this paper, we address the problem of zero-shot understanding of accidents from surveillance videos by identifying when an impact event occurs, what type of impact it is, and where in the frame it occurs using natural language.

By Tarandeep Singh, Soumyanetra Pal, Soham Biswas, Nishanth Chandran

llms multimodal benchmarks

arXiv AI

Jun 11

The Unreasonable Effectiveness of Discrete-Time Gaussian Process Mixtures for Robot Policy Learning

arXiv:2505. 03296v2 Announce Type: replace-cross Abstract: We present Mixture of Discrete-time Gaussian Processes (MiDiGap), a novel approach for flexible policy representation and imitation learning in robot manipulation.

By Jan Ole von Hartz, Adrian R\"ofer, Joschka Boedecker, Abhinav Valada

robotics multimodal benchmarks

arXiv AI

Jun 11

AVIS: Adaptive Test-Time Scaling for Vision-Language Models

arXiv:2606. 11576v1 Announce Type: cross Abstract: Modern Vision-Language Models (VLMs) benefit from chain-of-thought prompting and test-time scaling, but these gains often come with prohibitive inference cost due to large visual contexts and long decoding chains.

By Ahmadreza Jeddi, Minh Ngoc Le, Amirhossein Kazerouni, Hakki Can Karaimer, Hue Nguyen, Iqbal Mohomed, Michael Brudno, Alex Levinshtein, Konstantinos G. Derpanis, Babak Taati, Radek Grzeszczuk

llms reinforcement-learning efficiency multimodal benchmarks

arXiv AI

Jun 11

MultiToP: Learning to Patch Visual Tokens to Mitigate Hallucinations in Video Large Multimodal Models

arXiv:2606. 11792v1 Announce Type: cross Abstract: Video Large Multimodal Models have achieved remarkable progress in video understanding, yet they remain prone to hallucinations, where generated responses are not faithfully supported by the input video.

By Yuansheng Gao, Wenbin Xing, Jiahao Yuan, Kaiwen Zhou, Han Bao, Zonghui Wang, Wenzhi Chen

efficiency multimodal safety

arXiv AI

Jun 11

MentisOculi: Revealing the Limits of Reasoning with Mental Imagery

arXiv:2602. 02465v2 Announce Type: replace Abstract: Frontier models are transitioning from multimodal large language models (MLLMs) that merely ingest visual information to unified multimodal models (UMMs) capable of native interleaved generation.

By Jana Zeller, Thadd\"aus Wiedemer, Fanfei Li, Thomas Klein, Prasanna Mayilvahanan, Matthias Bethge, Felix Wichmann, Ryan Cotterell, Wieland Brendel

llms multimodal

arXiv Machine Learning

Jun 11

DAM-VLA: Decoupled Asynchronous Multimodal Vision Language Action model

arXiv:2606. 12105v1 Announce Type: cross Abstract: Vision-language-action (VLA) models inherit a shared synchronous clock from vision-language pretraining, processing every input at one rate.

By Pankhuri Vanjani, Zhuoyue Li, Jakub Suliga, Moritz Reuss, Gianluca Geraci, Xinkai Jiang, Rudolf Lioutikov

robotics multimodal

arXiv AI

Jun 11

Towards Fully Automated Exam Grading: Fairness-Aware Recognition of Handwritten Answers with Foundation Models

arXiv:2606. 11477v1 Announce Type: cross Abstract: Correcting handwritten exams by hand is time-consuming and error-prone, particularly for large cohorts, while fully digital exams tend to force a didactic narrowing towards closed question formats.

By Hartwig Grabowski

multimodal benchmarks safety

arXiv AI

Jun 11

SVoT: State-aware Visualization-of-Thought for Spatial Reasoning via Reinforcement Learning

arXiv:2606. 11770v1 Announce Type: new Abstract: Spatial reasoning remains a challenge for Multimodal Large Language Models (MLLMs), as it requires reliable multi-hop inference over both intermediate states and state transitions.

By Chao Lei, Yanbei Jiang, Markus Hiller, Zhijian Zhou, Xunye Tian, Krista A. Ehinger, Nir Lipovetzky

llms reinforcement-learning multimodal benchmarks

arXiv Machine Learning

Jun 11

Learning What to Say to Your VLA: Mostly Harmless Vision Language Action Model Steering

arXiv:2606. 12299v1 Announce Type: cross Abstract: Vision-Language-Action (VLA) models provide a natural language interface to robot control, but the mapping from language to behavior is often brittle and unintuitive: semantically similar instructions can induce drastically different behaviors, while some capabilities may not be elicitable through prompting alone.

By Hyun Joe Jeong, Gokul Swamy, Andrea Bajcsy

llms robotics fine-tuning multimodal

arXiv AI

Jun 11

MARIC: Multi-Agent Reasoning for Image Classification

arXiv:2509. 14860v2 Announce Type: replace-cross Abstract: Image classification has traditionally relied on parameter-intensive model training, requiring large-scale annotated datasets and extensive fine tuning to achieve competitive performance.

By Wonduk Seo, Minhyeong Yu, Hyunjin An, Seunghyun Lee

llms agents computer-vision fine-tuning multimodal benchmarks

arXiv AI

Jun 11

Bridging the Morphology Gap: Adapting VLA Models to Dexterous Manipulation via Intent-Conditioned Fine-Tuning

arXiv:2606. 12109v1 Announce Type: cross Abstract: Vision-Language-Action (VLA) models have demonstrated remarkable zero-shot generalization in robotic manipulation, yet the vast majority of pre-trained pipelines remain strictly confined to low-DoF parallel grippers.

By Chuanke Pang, Junyi Huang, Zhijun Zhao, Yaobing Wang, Kun Xu, Xilun Ding

diffusion robotics fine-tuning multimodal benchmarks

arXiv Machine Learning

Jun 11

Corpus Augmentation for Sign Language Translation via LLM-Guided Video Stitching

arXiv:2606. 11925v1 Announce Type: cross Abstract: Sign language translation (SLT) converts sign language video into spoken language text and holds significant promise for improving accessibility and enabling communication between signing and non-signing communities.

By Zsolt Robotka, \'Ad\'am R\'ak, Jalal Al-Afandi, Andr\'as Horv\'ath, Gy\"orgy Cserey

llms fine-tuning multimodal safety

arXiv AI

Jun 11

From Prompts to Tokens: Internalizing Causal Supervision in Vision-Language Model for Multi-Image Causal Reasoning

arXiv:2606. 11745v1 Announce Type: cross Abstract: Visual causal reasoning is essential for understanding and intervening in the physical world, requiring identification of causal variables from visual inputs and reasoning over intervention effects.

By Haoping Yu, Yuanxi Li, Jing Ma

llms multimodal

arXiv AI

Jun 11

MedCTA: A Benchmark for Clinical Tool Agents

arXiv:2606. 11702v1 Announce Type: cross Abstract: To make clinically grounded decisions, medical AI agents are expected to go beyond simple recognition and be capable of tool retrieval, evidence acquisition, and integration.

By Tajamul Ashraf, Hyewon Jeong, Fida Mohammad Thoker, Bernard Ghanem

agents nlp multimodal benchmarks

Get the daily briefing