Universal multimodal retrieval requires compact embeddings that preserve task-relevant semantic information across diverse modalities. Prior works have incorporated latent reasoning into multimodal embedding learning to refine this information before embedding extraction. However, most existing approaches remain confin...
Pei-Xi Wu, Ming-Zhou Jiang, Fei-Peng Ma et al.· 0 citations
MMEACR introduces a dual-track memory architecture that separates interpretable agent reasoning from fine-grained multimodal matching, and achieves strong overall performance against competitive LLM-based and agent-based baselines.
Hao Cong, Huizu Lin, Zihan Wang et al.· arXiv.org· 2 citations
AUSO (Action-level Unified Skill Optimization), which unifies skill learning and skill use through a progressive, action-aware optimization process, and consistently improves agent performance and out-of-distribution generalization over competitive baselines.
Huizu Lin, Chengkai Huang, Tian Gao et al.· 0 citations
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