Scaling Laws for Embedding Dimension in Information Retrieval (arXiv, Feb 2026)

Overview

This forum entry catalogs an arXiv paper on scaling laws for embedding dimension in information retrieval.

| Field | Detail | |---|---| | Title | Scaling Laws for Embedding Dimension in Information Retrieval | | Authors | Julian Killingback, Mahta Rafiee, Madine Manas, Hamed Zamani | | Link | https://arxiv.org/abs/2602.05062 | | Category | Retrieval |

Important caveat

The original forum post is largely a metadata-driven template entry. It does not reproduce the paper's abstract, methodology, or experimental results — the abstract fields are left as placeholders. The surrounding content is generic commentary about retrieval system design rather than a faithful summary of the paper. For actual findings, please refer directly to the arXiv page and the paper's PDF.

Context from the post

The entry situates the paper within the broader landscape of neural information retrieval:

• Problem space: Embedding-based retrieval must balance effectiveness, storage/compute efficiency, and scalability; embedding dimension is a key cost-quality trade-off parameter in dense retrieval and RAG pipelines.
• Typical system pipeline: query/document encoding → retrieval → reranking → generation, with learnable retrieval strategies emerging in agentic search.
• Common evaluation setups (as described generically in the post): datasets such as MS MARCO, BEIR, Natural Questions; metrics such as nDCG@10, MRR, Recall@k; baselines including BM25, dense retrievers, and cross-encoder rerankers.

Related entries cross-referenced in the post

• CAME: Competitively Learning a Mixture-of-Experts Model for First-stage Retrieval
• CoEvo: Coevolution of LLM and Retrieval Model for Domain-Specific Information Retrieval
• DRAMA: Diverse Augmentation from Large Language Models to Smaller Dense Retrievers (arXiv:2502.18460)
• ExpandR: Teaching Dense Retrievers Beyond Queries with LLM Guidance
• Fine-Tuning LLaMA for Multi-Stage Text Retrieval (arXiv:2310.08319)
• How Does Generative Retrieval Scale to Millions of Passages? (Google Research, arXiv:2305.11841)

Takeaways

1. Embedding dimension scaling is a practical lever for trading off retrieval quality against index size and latency. 2. Offline metric gains do not always translate to online satisfaction; cost, latency, and safety constraints matter in production. 3. Verify quantitative claims against the original paper, since this forum entry is a stub without the actual abstract or results.