# NeurDB: On the Design and Implementation of an AI-powered Autonomous Database

arXiv: https://arxiv.org/abs/2408.03013 · HTML: https://mlsanity.com/paper/2408.03013/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- Builds on 9 of its 28 references that are in the map.
- First posted on arXiv 6 Aug 2024.

## Builds on (most cited first, 9 of 9)

- [The Case for Learned Index Structures](https://mlsanity.com/paper/1712.01208/) (2017)
- [Database Meets Deep Learning: Challenges and Opportunities](https://mlsanity.com/paper/1906.08986/) (2019)
- [Lero: A Learning-to-Rank Query Optimizer](https://mlsanity.com/paper/2302.06873/) (2023)
- [ARM-Net: Adaptive Relation Modeling Network for Structured Data](https://mlsanity.com/paper/2107.01830/) (2021)
- [End-to-end Optimization of Machine Learning Prediction Queries](https://mlsanity.com/paper/2206.00136/) (2022)
- [Extending Relational Query Processing with ML Inference](https://mlsanity.com/paper/1911.00231/) (2019)
- [ALECE: An Attention-based Learned Cardinality Estimator for SPJ Queries on Dynamic Workloads (Extended)](https://mlsanity.com/paper/2310.05349/) (2023)
- [Detect, Distill and Update: Learned DB Systems Facing Out of Distribution Data](https://mlsanity.com/paper/2210.05508/) (2022)
- [Polyjuice: High-Performance Transactions via Learned Concurrency Control](https://mlsanity.com/paper/2105.10329/) (2021)

## Built on by (most cited first, 5 of 5)

- [Top Ten Challenges Towards Agentic Neural Graph Databases](https://mlsanity.com/paper/2501.14224/) (2025)
- [NeurStore: Efficient In-database Deep Learning Model Management System](https://mlsanity.com/paper/2509.03228/) (2025)
- [Can AI autonomously build, operate, and use the entire data stack?](https://mlsanity.com/paper/2512.07926/) (2025)
- [Agentic Control Center for Data Product Optimization](https://mlsanity.com/paper/2603.10133/) (2026)
- [VikingRAG: Accurate and Token-efficient Retrieval-augmented Generation over Structured Documents](https://mlsanity.com/paper/2609.11390/) (2026)

## Abstract (arXiv)

Databases are increasingly embracing AI to provide autonomous system optimization and intelligent in-database analytics, aiming to relieve end-user burdens across various industry sectors. Nonetheless, most existing approaches fail to account for the dynamic nature of databases, which renders them ineffective for real-world applications characterized by evolving data and workloads. This paper introduces NeurDB, an AI-powered autonomous database that deepens the fusion of AI and databases with adaptability to data and workload drift. NeurDB establishes a new in-database AI ecosystem that seamlessly integrates AI workflows within the database. This integration enables efficient and effective in-database AI analytics and fast-adaptive learned system components. Empirical evaluations demonstrate that NeurDB substantially outperforms existing solutions in managing AI analytics tasks, with the proposed learned components more effectively handling environmental dynamism than state-of-the-art approaches.