arXiv AI

MM-ISTS: Cooperating Irregularly Sampled Time Series Forecasting with Multimodal Vision-Text LLMs

arXiv:2603. 05997v2 Announce Type: replace-cross Abstract: Irregularly sampled time series (ISTS) are widespread in real-world scenarios, exhibiting asynchronous observations on uneven time intervals across diverse variables.

arXiv AI

Jun 17

Rethinking Multimodal Fusion for Time Series: Text Modalities Need Constrained Fusion

arXiv:2603. 22372v2 Announce Type: replace-cross Abstract: Recent advances in multimodal learning have motivated the integration of auxiliary modalities such as text or vision into time series (TS) forecasting.

By Seunghan Lee, Jun Seo, Jaehoon Lee, Sungdong Yoo, Minjae Kim, Tae Yoon Lim, Dongwan Kang, Hwanil Choi, SoonYoung Lee, Wonbin Ahn

multimodal

arXiv AI

Jun 18

InstructTime++: Time Series Classification with Multimodal Language Modeling via Implicit Feature Enhancement

arXiv:2601. 14968v2 Announce Type: replace-cross Abstract: Most existing time series classification methods adopt a discriminative paradigm that maps input sequences directly to one-hot encoded class labels.

By Mingyue Cheng, Xiaoyu Tao, Huajian Zhang, Qi Liu, Zhiding Liu, Yucong Luo, Yiheng Chen, Enhong Chen

llms multimodal benchmarks safety

arXiv AI

Jun 6

Seeing Time: Benchmarking Chronological Reasoning and Shortcut Biases in Vision-Language Models

arXiv:2606. 05702v1 Announce Type: new Abstract: Recent advancements in Vision-Language Models (VLMs) have significantly enhanced their ability to interpret complex visual semantics, yet their capacity for chronological reasoning remains under-explored.

By Haoyu Zhou, Qing Qing, Caichong Li, Qixin Zhang, Yongcheng Jing, Ziqi Xu, Juncheng Hu, Xikun Zhang, Renqiang Luo

llms multimodal benchmarks safety

arXiv AI

Jun 9

VFEM: Visual Feature Empowered Multivariate Time Series Forecasting with Cross-Modal Fusion

arXiv:2510. 03244v2 Announce Type: replace-cross Abstract: Large time series foundation models often adopt channel-independent architectures to handle varying data dimensions, but this design ignores crucial cross-channel dependencies.

By Yanlong Wang, Hang Yu, Jian Xu, Fei Ma, Hongkang Zhang, Tongtong Feng, Zijian Zhang, Shao-Lun Huang, Danny Dongning Sun, Xiao-Ping Zhang

multimodal benchmarks

arXiv AI

Jun 8

Modality Gap-Driven Subspace Alignment Training Paradigm For Multimodal Large Language Models

arXiv:2602. 07026v3 Announce Type: replace-cross Abstract: Despite the success of multimodal contrastive learning in aligning visual and linguistic representations, a persistent geometric anomaly, the Modality Gap, remains: embeddings of distinct modalities expressing identical semantics occupy systematically offset regions.

By Xiaomin Yu, Yi Xin, Yuhui Zhang, Wenjie Zhang, Chonghan Liu, Hanzhen Zhao, Chen Liu, Xiaoxing Hu, Ziyue Qiao, Hao Tang, Xiaobin Hu, Chengwei Qin, Hui Xiong, Yu Qiao, Shuicheng Yan

llms rag fine-tuning multimodal safety

arXiv AI

Jul 17

VLT: A Vision-Language-Time Series Multimodal Foundation Model for Industrial Intelligence

arXiv:2607. 14510v1 Announce Type: new Abstract: Industrial time series serve as the foundation for Prognostics and Health Management (PHM) to ensure the reliability and safety of industrial equipment such as aero-engines.

By Haiteng Wang, Jingheng Yan, Xiaokang Wang, Lei Ren

llms multimodal benchmarks safety

arXiv AI

Jun 29

Spectral Text Fusion: A Frequency-Aware Approach to Multimodal Time-Series Forecasting

arXiv:2602. 01588v3 Announce Type: replace-cross Abstract: Multimodal time series forecasting is crucial in real-world applications, where decisions depend on both numerical data and contextual signals.

By Huu Hiep Nguyen, Minh Hoang Nguyen, Dung Nguyen, Hung Le

rag multimodal benchmarks safety

arXiv Machine Learning

1d ago

FuseLIP: Multimodal Embeddings via Early Fusion of Discrete Tokens

arXiv:2506. 03096v2 Announce Type: replace-cross Abstract: Contrastive language-image pre-training aligns features of text-image pairs in a common latent space via distinct encoders for each modality.

By Christian Schlarmann, Francesco Croce, Nicolas Flammarion, Matthias Hein

llms rag diffusion multimodal

arXiv AI

Jul 3

SEPS: Semantic-enhanced Patch Slimming Framework for fine-grained cross-modal alignment

arXiv:2511. 01390v2 Announce Type: replace-cross Abstract: Fine-grained cross-modal alignment aims to establish precise local correspondences between vision and language, forming a cornerstone for visual question answering and related multimodal applications.

By Xinyu Mao, Junsi Li, Haoji Zhang, Yu Liang, Ming Sun

llms diffusion nlp multimodal safety

Hugging Face Trending Papers

Jun 4

Seeing Time: Benchmarking Chronological Reasoning and Shortcut Biases in Vision-Language Models

Recent advancements in Vision-Language Models (VLMs) have significantly enhanced their ability to interpret complex visual semantics, yet their capacity for chronological reasoning remains under-explored. In this paper, we introduce a novel benchmark specifically designed to evaluate how VLMs perceive and reason about chronological information within and across images.

llms multimodal benchmarks safety

arXiv AI

Jul 20

LVSum: A Benchmark for Timestamp-Aware Long Video Summarization

arXiv:2604. 10024v2 Announce Type: replace-cross Abstract: Long video summarization presents significant challenges for multimodal large language models (MLLMs), particularly in maintaining temporal fidelity over extended durations and producing summaries that are both semantically and temporally grounded.

By Alkesh Patel, Melis Ozyildirim, Ying-Chang Cheng, Ganesh Nagarajan

llms nlp multimodal benchmarks safety

arXiv AI

Jul 21

Learning to Detect Cross-Modal Negation: An Analysis of Latent Representations and an Attention-Based Solution

arXiv:2607. 17712v1 Announce Type: new Abstract: Detecting high-level semantic concepts like negation across modalities remains a challenge for current multimodal systems.

By Ali AbuSaleh, Leon Hammerla, Alexander Mehler

llms rag multimodal

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