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 4

On-the-fly Repulsion in the Contextual Space for Rich Diversity in Diffusion Transformers

arXiv:2603. 28762v2 Announce Type: replace-cross Abstract: Modern Text-to-Image (T2I) diffusion models have achieved remarkable semantic alignment, yet they often suffer from a significant lack of variety, converging on a narrow set of visual solutions for any given prompt.

By Omer Dahary, Benaya Koren, Daniel Garibi, Daniel Cohen-Or

llms diffusion multimodal safety

arXiv Machine Learning

Jun 4

Stateful Visual Encoders for Vision-Language Models

arXiv:2606. 04433v1 Announce Type: cross Abstract: Vision-language models (VLMs) are increasingly used in multi-image, multi-turn agentic settings where decisions depend on visual changes.

By Zirui Wang, Junwei Yu, Adam Yala, David M. Chan, Joseph E. Gonzalez, Trevor Darrell

llms agents fine-tuning multimodal

arXiv AI

Jun 4

MapAgent: An Industrial-Grade Agentic Framework for City-scale Lane-level Map Generation

arXiv:2606. 04513v1 Announce Type: new Abstract: Lane-level maps are critical infrastructure for autonomous driving and lane-level navigation, yet constructing and maintaining standardized lane networks for hundreds of cities remains highly labor-intensive.

By Deguo Xia, Zihan Li, Haochen Zhao, Dong Xie, Yuyao Kong, Xiyan Liu, Jizhou Huang, Mengmeng Yang, Diange Yang

agents robotics efficiency multimodal

arXiv Machine Learning

Jun 4

Test-Time Compute Scaling for ASR with Depth-Conditioned Looped Transformers

arXiv:2606. 04678v1 Announce Type: new Abstract: End-to-end ASR systems typically use fixed-depth acoustic encoders at inference, making it difficult to trade additional test-time computation for improved recognition without training a larger model.

By Yacouba Kaloga, Shashi Kumar, Shakeel A. Sheikh, Driss Khalil, Petr Motlicek, Ina Kodrasi

llms rag multimodal

arXiv AI

Jun 4

Dynamic Content Moderation in Livestreams: Combining Supervised Classification with MLLM-Boosted Similarity Matching

arXiv:2512. 03553v3 Announce Type: replace-cross Abstract: Content moderation remains a critical yet challenging task for large-scale user-generated video platforms, especially in livestreaming environments where moderation must be timely, multimodal, and robust to evolving forms of unwanted content.

By Wei Chee Yew, Hailun Xu, Sanjay Saha, Xiaotian Fan, Hiok Hian Ong, David Yuchen Wang, Kanchan Sarkar, Zhenheng Yang, Danhui Guan

llms multimodal safety

arXiv AI

Jun 4

NoRA: Evaluating Grounded Reasonableness in Visual First-person Normative Action Reasoning

arXiv:2606. 04806v1 Announce Type: cross Abstract: LLMs and agentic systems are increasingly deployed in social environments, making normative competence critical for safe and appropriate behavior.

By Sichao Li, Sai Ma, Daniel Kilov, Secil Yanik Guyot, Zhuang Li, Seth Lazar

llms agents multimodal benchmarks safety

arXiv AI

Jun 4

Transformer-Based Autonomous Driving Models and Deployment-Oriented Compression: A Survey

arXiv:2304. 10891v3 Announce Type: replace-cross Abstract: Transformer-based models are becoming a central paradigm in autonomous driving because they can capture long-range spatial dependencies, multi-agent interactions, and multimodal context across perception, prediction, and planning.

By Juan Zhong, Yuhang Shi, Zukang Xu, Xi Chen

llms agents robotics efficiency multimodal safety

arXiv AI

Jun 4

DEFLECT: Temporal Counterfactual Preference Learning for Delay-Robust Asynchronous VLAs

arXiv:2605. 19294v2 Announce Type: replace-cross Abstract: Vision-Language-Action (VLA) policies increasingly rely on asynchronous inference to hide large-model latency behind ongoing robot motion.

By Yixiang Zhu, Yonghao Chen, Zijie Yang, Yusong Hu, Xinyu Chen

robotics fine-tuning multimodal safety

arXiv Machine Learning

Jun 4

Hyper-ICL: Attention Calibration with Hyperbolic Anchor Distillation for Multimodal In-Context Learning

arXiv:2606. 04434v1 Announce Type: cross Abstract: Multimodal In-Context Learning (ICL) has emerged as a practical inference paradigm for Multimodal Large Language Models, where a small set of interleaved image-text In-Context Demonstrations (ICDs) conditions the model to solve new tasks.

By Niloufar Alipour Talemi, Hossein Kashiani, Fatemeh Afghah

llms efficiency multimodal benchmarks

arXiv AI

Jun 4

GenSpan: Generation-Calibrated Motion Span Priors for Multi-Verb Video Corpus Moment Retrieval

arXiv:2603. 22121v2 Announce Type: replace-cross Abstract: Video Corpus Moment Retrieval (VCMR) aims to retrieve both the correct video and its temporal segment corresponding to a natural-language query, a task that is especially challenging for multi-verb queries where temporal action ordering is critical.

By Yunzhuo Sun, Xinyue Liu, Yanyang Li, Nanding Wu, Linlin Zong, Xianchao Zhang, Wenxin Liang

llms multimodal benchmarks

arXiv AI

Jun 4

Can VLMs Predict Future States? Bootstrapping World Models from Inverse Dynamics

arXiv:2506. 06006v3 Announce Type: replace-cross Abstract: Can unified vision-language models (VLMs) perform forward dynamics prediction (FDP), i.

By Yifu Qiu, Yftah Ziser, Anna Korhonen, Shay B. Cohen, Edoardo M. Ponti

llms fine-tuning multimodal benchmarks

arXiv AI

Jun 4

A Unified Geometric Space for Topological Alignment Between Transformer-Based Models and Human Brain Networks

arXiv:2510. 24342v2 Announce Type: replace Abstract: Prior brain-AI alignment studies are typically constrained by specific inputs and tasks, limiting their ability to capture organizational properties across models with different modalities.

By Silin Chen, Yuzhong Chen, Caiwei Wang, Zifan Wang, Junhao Wang, Zifeng Jia, Keith M Kendrick, Tuo Zhang, Lin Zhao, Dezhong Yao, Tianming Liu, Xi Jiang

llms fine-tuning multimodal safety

arXiv AI

Jun 4

EvoPrompt: Guided Prompt Evolution for Vision-Language Models Adaptation

arXiv:2603. 09493v2 Announce Type: replace-cross Abstract: The adaptation of large-scale vision-language models (VLMs) to downstream tasks with limited labeled data remains a significant challenge.

By Enming Zhang, Jiayang Li, Yanlong Wang, Yanru Wu, Zhenyu Liu, Yang Li

llms rag fine-tuning multimodal benchmarks

arXiv Machine Learning

Jun 4

FoeGlass: Simple In-Context Learning Is Enough for Red Teaming Audio Deepfake Detectors

arXiv:2606. 05101v1 Announce Type: cross Abstract: Audio deepfake detection (ADD) models are critical for countering the malicious use of text-to-speech (TTS) models.

By Sepehr Dehdashtian, Jacob H Seidman, Vishnu N Boddeti, Gaurav Bharaj

llms fine-tuning multimodal benchmarks safety

arXiv AI

Jun 4

VGGSounder: Audio-Visual Evaluations for Foundation Models

arXiv:2508. 08237v4 Announce Type: replace-cross Abstract: The emergence of audio-visual foundation models underscores the importance of reliably assessing their multi-modal understanding.

By Daniil Zverev, Thadd\"aus Wiedemer, Ameya Prabhu, Matthias Bethge, Wieland Brendel, A. Sophia Koepke

multimodal benchmarks

arXiv AI

Jun 4

Audio Interaction Model

arXiv:2606. 05121v1 Announce Type: cross Abstract: Audio is an inherently interactive modality, yet today's Large Audio Language Models (LALMs) are offline, and streaming audio models each handle only a single task such as streaming ASR or voice chatting.

By Zhifei Xie, Zihang Liu, Ze An, Xiaobin Hu, Yue Liao, Ziyang Ma, Dongchao Yang, Mingbao Lin, Deheng Ye, Shuicheng Yan, Chunyan Miao

llms multimodal benchmarks

arXiv AI

Jun 4

Bad Seeing or Bad Thinking? Rewarding Perception for Multimodal Reasoning

arXiv:2605. 14054v2 Announce Type: replace Abstract: Achieving robust perception-reasoning synergy is a central goal for advanced Vision-Language Models (VLMs).

By Haozhe Wang, Qixin Xu, Changpeng Wang, Taofeng Xue, Chong Peng, Wenhu Chen, Fangzhen Lin

llms agents reinforcement-learning multimodal

arXiv AI

Jun 4

Breaking Bad Molecules: Are MLLMs Ready for Structure-Level Molecular Detoxification?

arXiv:2506. 10912v4 Announce Type: replace Abstract: Toxicity remains a leading cause of early-stage drug development failure.

By Fei Lin, Ziyang Gong, Cong Wang, Tengchao Zhang, Yonglin Tian, Yining Jiang, Ji Dai, Chao Guo, Xiaotong Yu, Xue Yang, Gen Luo, Fei-Yue Wang

llms efficiency multimodal benchmarks safety

arXiv AI

Jun 4

M$^3$Eval: Multi-Modal Memory Evaluation through Cognitively-Grounded Video Tasks

arXiv:2606. 05008v1 Announce Type: cross Abstract: As multi-modal models advance towards long-form video understanding, memory emerges as a critical capability.

By Jie Huang, Ruixun Liu, Sirui Sun, Xinyi Yang, Yin Li, Yixin Zhu, Yiwu Zhong

multimodal benchmarks

arXiv AI

Jun 4

Geometry-Aware Distillation for Prompt Tuning Biomedical Vision-Language Models

arXiv:2606. 04922v1 Announce Type: cross Abstract: Current prompt-based and adapter-based tuning of vision-language models (VLMs) is attractive for medical imaging, where clinical data sensitivity favors frozen backbones and annotations are limited.

By Tran Dinh Tien, Zhiqiang Shen

llms efficiency multimodal benchmarks safety

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