arXiv AI August 5, 2026 By Yeong-Joon Ju, Seong-Whan Lee

From Generator to Embedder: Harnessing Innate Abilities of Multimodal LLMs via Building Zero-Shot Discriminative Embedding Model

arXiv:2508. 00955v3 Announce Type: replace-cross Abstract: Adapting generative Multimodal Large Language Models (MLLMs) into universal embedding models typically demands resource-intensive contrastive pre-training, while traditional hard negative mining methods suffer from severe false negative contamination.

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

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

arXiv Machine Learning

Jun 2

When Hard Negatives Hurt: Bridging the Generative-Discriminative Gap in Hard Negative Synthesis for Retrieval

arXiv:2606. 01304v1 Announce Type: new Abstract: Hard negative mining has become the dominant strategy for training retrievers, yet it faces intrinsic limitations: negatives are bounded by corpus availability, selected by retriever score rather than diagnostic value, and increasingly contaminated by false positives as the retriever improves.

By Zhicheng Zhang, Jiwei Tang, Kuicai Dong, Xiaopeng Li, Jieming Zhu, Jingyu Li, Qianhui Zhu, Fengyuan Lu, Wang Jiaheng, Gang Wang, Hai-Tao Zheng, Zhaocheng Du

llms

arXiv AI

6d ago

VLM2Rec: Resolving Modality Collapse in Vision-Language Model Embedders for Multimodal Sequential Recommendation

arXiv:2603. 17450v2 Announce Type: replace-cross Abstract: Sequential Recommendation (SR) in multimodal settings typically relies on small frozen pretrained encoders, which limits semantic capacity and prevents Collaborative Filtering (CF) signals from being fully integrated into item representations.

By Junyoung Kim, Woojoo Kim, Wonbin Kweon, Jaehyung Lim, Dongha Kim, Hwanjo Yu

llms rag fine-tuning multimodal

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

Hugging Face Trending Papers

Jun 22

VideoLatent: Video-Language Learning via Latent Self-Forcing

Recent advancements in chain-of-thought (CoT) reasoning have shown promise in enhancing video understanding and reasoning capabilities of multimodal large language models (MLLMs). However, existing CoT-based MLLMs require labor-intensive CoT annotations and incur substantial training and inference overhead.

llms 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

Related stories

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

When Hard Negatives Hurt: Bridging the Generative-Discriminative Gap in Hard Negative Synthesis for Retrieval

VLM2Rec: Resolving Modality Collapse in Vision-Language Model Embedders for Multimodal Sequential Recommendation

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

VideoLatent: Video-Language Learning via Latent Self-Forcing

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