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 Machine Learning

Jun 19

Low-Burden Data Augmentation for Dysarthric ASR via Zero-Shot Voice Cloning

arXiv:2606. 19823v1 Announce Type: cross Abstract: Automatic speech recognition remains unreliable for dysarthric speech due to data scarcity and high inter-speaker variability.

By Satwinder Singh, Qianli Wang, Zihan Zhong, Clarion Mendes, Hasegawa-Johnson, Waleed Abdulla, Seyed Reza Shahamiri

fine-tuning multimodal

arXiv AI

Jun 19

REVEAL++: Differentiable Phenotypic Grouping for Vision-Language Retinal Modeling of Alzheimer's Disease Risk

arXiv:2606. 19522v1 Announce Type: new Abstract: The retina offers a noninvasive window into neurodegenerative disease, capturing subtle structural patterns associated with a risk of future cognitive decline.

By Ethan Elio Meidinger, Seowung Leem, Zeyun Zhao, Ruogu Fang

rag multimodal safety

arXiv Machine Learning

Jun 19

PASQA: Pitch-Accent-Focused Speech Quality Assessment Model Trained on Synthetic Speech with Accent Errors

arXiv:2606. 20137v1 Announce Type: cross Abstract: Existing mean opinion score (MOS) prediction models typically predict utterance-level naturalness MOS and can be insensitive to localized pitch-accent errors.

By Masaya Kawamura, Yuma Shirahata, Kentaro Mitsui, Reo Shimizu

multimodal

arXiv Machine Learning

Jun 19

Computational Methods and Challenges in Cell-Free DNA Analysis for Multi-Cancer Early Detection

arXiv:2606. 20174v1 Announce Type: new Abstract: Cell-free DNA (cfDNA) is a promising avenue for non-invasive multicancer early detection (MCED), in that, it can enable multiple cancer detection simultaneously from a single blood draw, with particular sensitivity to cancers that currently lack established screening programs.

By Nicko Starkey, Marcin W. Wojewodzic, Krzysztof Rzecki

multimodal safety

arXiv AI

Jun 19

VCG: A Multimodal Retrieval Framework for E-Commerce Video Feeds under Extreme Cold-Start Conditions

arXiv:2606. 19627v1 Announce Type: cross Abstract: The digital commerce landscape is shifting from static, search-driven catalogs to dynamic, immersive video feeds.

By Katya Mirylenka, Egor Malykh, Mahdyar Ravanbakhsh, Michael Gygli, Marco-Andrea Buchmann, Andrew Dzhoha, Svitlana Borzenko, Francesca Catino, Mohamed Gaafar, Maarten Versteegh, Thomas Kober, Dario d'Andrea, Ellie Langhans

llms rag multimodal

arXiv Machine Learning

Jun 19

Bioacoustic Geolocation: Species Sounds as Geographic Signals

arXiv:2505. 18726v3 Announce Type: replace-cross Abstract: Can we determine someone's geographic location solely from the sounds they hear?

By Mustafa Chasmai, Wuao Liu, Subhransu Maji, Grant Van Horn

multimodal benchmarks

arXiv AI

Jun 19

Lagrange: An Open-Vocabulary, Energy-Based Sparse Framework for Generalized End-to-End Driving

arXiv:2606. 20274v1 Announce Type: new Abstract: Scaling end-to-end autonomous driving to complex, open-world environments requires perceptual models that generalize to anomalous scenarios and planners that produce kinematically valid trajectories.

By Shihao Ji, HongXi Li, Zihui Song, Mingyu Li

llms agents robotics multimodal benchmarks

arXiv Machine Learning

Jun 19

Humanoid Everyday: A Comprehensive Robotic Dataset for Open-World Humanoid Manipulation

arXiv:2510. 08807v2 Announce Type: replace-cross Abstract: From loco-motion to dextrous manipulation, humanoid robots have made remarkable strides in demonstrating complex full-body capabilities.

By Zhenyu Zhao, Hongyi Jing, Xiawei Liu, Jiageng Mao, Abha Jha, Hanwen Yang, Rong Xue, Sergey Zakharov, Vitor Guizilini, Yue Wang

agents robotics multimodal benchmarks

arXiv AI

Jun 19

UniMM: A Unified Mixture Model Framework for Multi-Agent Simulation

arXiv:2501. 17015v2 Announce Type: replace Abstract: Simulation plays a crucial role in assessing autonomous driving systems, where the generation of realistic multi-agent behaviors is a key aspect.

By Longzhong Lin, Xuewu Lin, Kechun Xu, Haojian Lu, Lichao Huang, Rong Xiong, Yue Wang

agents robotics multimodal benchmarks safety

arXiv AI

Jun 19

ZeSTA: Zero-Shot TTS Augmentation with Domain-Conditioned Training for Data-Efficient Personalized Speech Synthesis

arXiv:2603. 04219v2 Announce Type: replace-cross Abstract: We investigate the use of zero-shot text-to-speech (ZS-TTS) as a data augmentation source for low-resource personalized speech synthesis.

By Youngwon Choi, Jinwoo Oh, Hwayeon Kim, Hyeonyu Kim

rag fine-tuning multimodal

arXiv Machine Learning

Jun 19

A deep learning framework for jointly solving transient Fokker-Planck equations with arbitrary parameters and initial distributions

arXiv:2604. 06001v2 Announce Type: replace-cross Abstract: Efficiently solving the Fokker-Planck equation (FPE) is central to analyzing complex parameterized stochastic systems.

By Xiaolong Wang, Jing Feng, Qi Liu, Chengli Tan, Yuanyuan Liu, Yong Xu

multimodal

arXiv AI

Jun 19

Evaluating and Enhancing Negation Comprehension in Remote Sensing MLLMs

arXiv:2606. 20177v1 Announce Type: cross Abstract: Multimodal Large Language Models (MLLMs) have demonstrated remarkable success in various Remote Sensing (RS) tasks.

By Haochen Han, Jue Wang, Alex Jinpeng Wang, Fangming Liu

llms multimodal benchmarks safety

arXiv AI

Jun 19

Vero: An Open RL Recipe for General Visual Reasoning

arXiv:2604. 04917v3 Announce Type: replace-cross Abstract: What does it take to build a visual reasoner that works across charts, science, spatial understanding, and open-ended tasks?

By Gabriel Sarch, Linrong Cai, Qunzhong Wang, Haoyang Wu, Danqi Chen, Zhuang Liu

llms reinforcement-learning efficiency multimodal benchmarks

arXiv AI

Jun 19

Co-policy: Responsive Human-Robot Co-Creation for Musical Performances

arXiv:2606. 19914v1 Announce Type: cross Abstract: Art has long stood as a pivotal expression of human creativity.

By Xuetao Li, Wenke Huang, Mang Ye, Zijian Liu, Jinhua Xie, Jifeng Xuan, Miao Li

diffusion robotics fine-tuning multimodal safety

arXiv AI

Jun 19

FlowEdit: Associative Memory for Lifelong Pronunciation Adaptation in Flow-Matching TTS

arXiv:2606. 20518v1 Announce Type: new Abstract: Flow-matching text-to-speech systems achieve remarkable zero-shot quality but remain static after deployment: pronunciation errors on out-of-vocabulary proper nouns persist unless the model is retrained.

By Harshit Singh, Ayush Pratap Singh, Nityanand Mathur

rag multimodal benchmarks

arXiv AI

Jun 19

ProMUSE: Progressive Multi-modal Uncertainty-guided Staged Evidential Alzheimer Disease Classification

arXiv:2606. 19371v1 Announce Type: cross Abstract: Alzheimer's disease (AD) is a fatal disorder that destroys memory and cognitive skills in the elderly population.

By Long Doan, Branden Chen, Ethan Litton, Huan Huang, Jiajing Huang, Yixin Xie, Weihua Zhou, Nandakumar Narayanan, Chen Zhao

multimodal

arXiv AI

Jun 19

Improving End-to-End Speech Recognition for Dysarthric Speech through In-Domain Data Augmentation

arXiv:2606. 19797v1 Announce Type: cross Abstract: Dysarthric speech recognition is crucial for facilitating effective communication among individuals with dysarthria.

By Paban Sapkota, Hemant Kumar Kathania, Sudarsana Reddy Kadiri, Shrikanth Narayanan

fine-tuning multimodal

arXiv AI

Jun 19

FlowFake: Liquid Networks for Audio Deepfake Detection

arXiv:2606. 19579v1 Announce Type: cross Abstract: Audio deepfakes generated by neural text-to-speech and voice-cloning systems threaten speaker verification and public discourse at scale.

By Shivaay Dhondiyal, Divyansh Sharma, Dinesh Kumar Vishwakarma

multimodal benchmarks

arXiv AI

Jun 19

HilDA: Hierarchical Distillation with Diffusion for Advancing Self-Supervised LiDAR Pre-trainin

arXiv:2606. 20189v1 Announce Type: cross Abstract: Leveraging Vision Foundation Models (VFMs) for camera-to-LiDAR knowledge distillation offers a promising solution to the scarcity of annotated data needed to represent the immense geometric and kinematic diversity of real-world autonomous driving (AD).

By Maciej Wozniak, Jesper Ericsson, Hariprasath Govindarajan, Truls Nyberg, Thomas Gustafsson, Patric Jensfelt, Olov Andersson

agents diffusion computer-vision robotics efficiency multimodal benchmarks safety

arXiv AI

Jun 19

Confidence Calibration for Multimodal LLMs: An Empirical Study through Medical VQA

arXiv:2606. 19950v1 Announce Type: cross Abstract: Multimodal Large Language Models (MLLMs) show great potential in medical tasks, but their elicited confidence often misaligns with actual accuracy, potentially leading to misdiagnosis or overlooking correct advice.

By Yuetian Du, Yucheng Wang, Ming Kong, Tian Liang, Qiang Long, Bingdi Chen, Qiang Zhu

llms nlp multimodal

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