AI RESEARCH
DIVE: Embedding Compression via Self-Limiting Gradient Updates
arXiv CS.LG
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ArXi:2605.20689v1 Announce Type: cross High-dimensional embeddings from large language models impose significant storage and computational costs on vector search systems. Recent embedding compression methods, including Matryoshka-Adaptor (EMNLP 2024), Search-Adaptor (ACL 2024), and SMEC (EMNLP 2025), enable dimensionality reduction through lightweight residual adapters, but their