Scaling Laws for Embedding Dimension in Information Retrieval (arXiv:2602.05062)

Overview

This entry discusses an arXiv paper: "Scaling Laws for Embedding Dimension in Information Retrieval" (February 2026).

• Authors: Julian Killingback, Mahta Rafiee, Madine Manas, Hamed Zamani
• Link: https://arxiv.org/abs/2602.05062
• Topic area: Information retrieval, embedding models, scaling laws

Context

Embedding-based retrieval is central to modern search, recommendation, and RAG systems. Embedding dimension directly determines index size, memory footprint, ANN search latency, and GPU/memory cost, so understanding how performance scales with dimension is practically important. This paper addresses scaling laws for embedding dimension in information retrieval.

Important caveat: The original forum post is largely a template entry and does not reproduce the paper's abstract, method details, or quantitative results. The technical content below reflects general background around the topic rather than verified claims from the paper. For precise findings, formulas, and benchmark numbers, consult the original PDF at the arXiv link above.

Related Discussion Points from the Post

Typical considerations in this research area include:

• Architecture: cascaded retrieval + reranking + generation remains mainstream; representation learning choices (dense vs. sparse vs. late interaction) trade off efficiency and accuracy.
• Evaluation: common benchmarks include MS MARCO and BEIR, with metrics such as nDCG@10, MRR, and Recall@k.
• Engineering constraints: latency budgets (p99), embedding version management, caching, and per-query cost are hard requirements in production.
• Open problems: evaluation trustworthiness, cost/latency, cross-lingual and multimodal generalization.

Related Entries

• DRAMA: Diverse Augmentation from Large Language Models to Smaller Dense Retrievers (arXiv:2502.18460)
• Fine-Tuning LLaMA for Multi-Stage Text Retrieval (arXiv:2310.08319)
• How Does Generative Retrieval Scale to Millions of Passages? (arXiv:2305.11841)

Reference

• Original paper: Scaling Laws for Embedding Dimension in Information Retrieval. arXiv:2602.05062, https://arxiv.org/abs/2602.05062