DIVE: Embedding Compression via Self-Limiting Gradient Updates
The paper titled 'DIVE: Embedding Compression via Self-Limiting Gradient Updates' introduces a new method for compressing high-dimensional embeddings from large language models. The proposed DIVE method utilizes a self-limiting hinge-based triplet loss and a head-wise NT-Xent contrastive loss to improve retrieval performance, especially in scenarios with limited labeled data. Results show that DIVE outperforms existing compression methods across multiple datasets and compression ratios.
- ▪DIVE addresses overfitting issues in embedding compression methods when labeled data is scarce.
- ▪The method employs a self-limiting hinge-based triplet loss to control gradient updates.
- ▪DIVE outperforms three baseline adapters on six BEIR datasets at various compression ratios.
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Record
| Original publisher | arXiv cs.AI |
| Canonical URL | https://arxiv.org/abs/2605.20689 |
| Publication time | Fri, 22 May 2026 00:00:00 -0400 |
| Retrieval time | 2026-05-22T04:02:00.009Z |
| Last seen | 2026-05-22T04:02:00.009Z |
| Headline source | Publisher (no WeSearch rewrite) |
| Excerpt source | publisher body |
| Excerpt method | First ~120 words (~800 chars) of extracted publisher body, fair-use limited. |
| Summary | WeSearch · cerebras-chat (WeSearch summarizer) |
| Summary source text | contentText |
| Citation coverage | Summary is a WeSearch-generated derivative; primary citation is the original publisher URL. |
| Cluster | 0Fd11zt02p10 |
| Cluster logic | Grouped by semantic title/content similarity across sources within a rolling window. Same-publisher template collisions are excluded from coverage comparison. |
| Ranking reason | Story pages are not engagement-ranked. Hub feeds use recency, with optional source-diversified chronological ordering (cap consecutive stories per source). No personalized ranking. |
| Publisher visit | Yes — open original |
| Substitutes article? | No — link-out required for full text |
Rights status (four layers)
WeSearch handling by dimension
| Indexing | May the item be indexed (stored, ranked, made findable)? | Allowed |
| Snippet | May a short excerpt of the publisher's text be shown? | Allowed |
| AI summary | May WeSearch generate its own short summary of the article? | Limited |
| Retrieval / RAG | May the content be exposed for third-party retrieval-augmented generation? | Not asserted |
| Model training | May the content be used to train AI models? | Not asserted |
| Commercial reuse | May the content be reused commercially? | Not permitted |
Basis: Derived from the published RSS/Atom feed. Contact: [email protected]. Reviewed: 2026-07-24.
Opening excerpt (first ~120 words) tap to expand
Computer Science > Computation and Language arXiv:2605.20689 (cs) [Submitted on 20 May 2026] Title:DIVE: Embedding Compression via Self-Limiting Gradient Updates Authors:Dongfang Zhao View a PDF of the paper titled DIVE: Embedding Compression via Self-Limiting Gradient Updates, by Dongfang Zhao View PDF HTML (experimental) Abstract: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 training objectives cause severe overfitting when labeled data is scarce, degrading retrieval performance below the…
Excerpt limited to ~120 words for fair-use compliance. The full article is at arXiv cs.AI.
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