arXiv AI June 8, 2026 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

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

Read the original on arXiv AI →

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.

Summary generated by The Flow from the publisher's feed. The full article lives at arXiv AI.

arXiv AI

Jul 14

PivotMerge: Bridging Heterogeneous Multimodal Pre-training via Post-Alignment Model Merging

arXiv:2604. 22823v2 Announce Type: replace-cross Abstract: Multimodal Large Language Models (MLLMs) rely on multimodal pre-training over diverse data sources, where different datasets often induce complementary cross-modal alignment capabilities.

By Zibo Shao, Baochen Xiong, Xiaoshan Yang, Yaguang Song, Qimeng Zhang, Haifeng Chen, Changsheng Xu

llms fine-tuning multimodal benchmarks safety

arXiv AI

Jul 3

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

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

llms diffusion nlp multimodal safety

arXiv AI

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Multimodal Representation Alignment for Cross-modal Information Retrieval

arXiv:2506. 08774v2 Announce Type: replace-cross Abstract: Different machine learning models can represent the same underlying concept in different ways.

By Fan Xu, Luis A. Leiva

llms rag multimodal benchmarks safety

arXiv AI

Jun 30

Rank-Aware Hyperbolic Alignment for Vision-Language Dataset Distillation

arXiv:2606. 29464v1 Announce Type: cross Abstract: Vision-language dataset distillation (VLDD) compresses a large image-text paired dataset into a small set of synthetic pairs that can efficiently train contrastive vision-language models under strict data and compute budgets.

By Jongoh Jeong, Sun-Kyung Lee, Kuk-Jin Yoon

llms rag efficiency multimodal benchmarks safety

arXiv AI

Jul 16

Inverse-LLaVA: Rethinking Multimodal Alignment via Text-to-Vision Mapping

arXiv:2508. 12466v2 Announce Type: replace-cross Abstract: Traditional multimodal learning approaches rely on alignment pre-training to bridge vision and language modalities, typically by projecting visual features into discrete text token spaces using large-scale image--text data.

By Xuhui Zhan, Tyler Derr

llms rag multimodal benchmarks safety

arXiv AI

Jun 24

When Language Overwrites Vision: Over-Alignment and Geometric Debiasing in Vision-Language Models

arXiv:2605. 08245v4 Announce Type: replace-cross Abstract: Vision-Language Models (VLMs) increasingly power high-stakes applications, from medical imaging to autonomous systems, yet they routinely hallucinate, confidently describing content not present in the input.

By Harshvardhan Saini, Samyak Jha, Yiming Tang, Dianbo Liu

llms rag agents fine-tuning multimodal benchmarks safety

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PivotMerge: Bridging Heterogeneous Multimodal Pre-training via Post-Alignment Model Merging

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

Multimodal Representation Alignment for Cross-modal Information Retrieval

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Inverse-LLaVA: Rethinking Multimodal Alignment via Text-to-Vision Mapping

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