# Efficient and Effective In-place Graph-based Vector Index Updates

arXiv: https://arxiv.org/abs/2607.15576 · HTML: https://mlsanity.com/paper/2607.15576/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 17 Jul 2026.

## Built on by (most cited first, 1 of 1)

- [Semantics Delivery Network: Rethinking Web Retrieval Infrastructure for LLM Agents](https://mlsanity.com/paper/2609.22486/) (2026)

## Abstract (arXiv)

In the era of Large Language Models (LLMs), efficient vector updates are critical for capturing real-time information from rapidly evolving data. However, it is not trivial to process frequent vector insert and delete updates and maintain a high recall of the search results simultaneously. Specifically, the cluster-based vector indexing methods have high update throughput but low search result quality. Existing out-of-place graph-based vector indexing update approaches suffer from poor update throughput due to the need to periodically merge update batches into the underlying graph index. Building a vector data system that supports efficient and effective in-place updates is inherently challenging. In this work, we propose Yi to achieve it. In particular, Yi supports in-place graph-based vector indexing updates with consistently high update throughput and good search result quality. The key idea of Yi is decomposition facilitates consolidation. In particular, we introduce a vector-level update mechanism and architect Yi with three core components: (i) a tasklet-based execution engine, (ii) an asynchronous buffer manager, and (iii) a vector file system. Experimental results demonstrate that Yi achieves 1.75x higher update throughput and 1.8x higher concurrent search throughput than the state-of-the-art systems on the 800M dataset, while using only 73% of the peak memory and fewer CPU cores.