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cs.CV2026
MTA-Agent: An Open Recipe for Multimodal Deep Search Agents
Xiangyu Peng, Can Qin, An Yan +4
Multimodal large language models (MLLMs) have demonstrated strong capabilities in visual understanding, yet they remain limited in complex, multi-step reasoning that requires deep…
cs.CV2025
VLM2Vec-V2: Advancing Multimodal Embedding for Videos, Images, and Visual Documents
Rui Meng, Ziyan Jiang, Ye Liu +10
Multimodal embedding models have been crucial in enabling various downstream tasks such as semantic similarity, information retrieval, and clustering over different modalities. How…