# Incorporating domain knowledge into neural-guided search

arXiv: https://arxiv.org/abs/2107.09182 · HTML: https://mlsanity.com/paper/2107.09182/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- Builds on 6 of its 20 references that are in the map.
- First posted on arXiv 19 Jul 2021.

## Builds on (most cited first, 6 of 6)

- [Neural Architecture Search with Reinforcement Learning](https://mlsanity.com/paper/1611.01578/) (2016)
- [Neural Combinatorial Optimization with Reinforcement Learning](https://mlsanity.com/paper/1611.09940/) (2016)
- [Deep symbolic regression: Recovering mathematical expressions from data via risk-seeking policy gradients](https://mlsanity.com/paper/1912.04871/) (2019)
- [MolecularRNN: Generating realistic molecular graphs with optimized properties](https://mlsanity.com/paper/1905.13372/) (2019)
- [Neural Program Synthesis with Priority Queue Training](https://mlsanity.com/paper/1801.03526/) (2018)
- [Distilling Wikipedia mathematical knowledge into neural network models](https://mlsanity.com/paper/2104.05930/) (2021)

## Built on by (most cited first, 6 of 6)

- [CoNSoLe: Convex Neural Symbolic Learning](https://mlsanity.com/paper/2206.00257/) (2022)
- [Deep Symbolic Optimization: Reinforcement Learning for Symbolic Mathematics](https://mlsanity.com/paper/2505.10762/) (2025)
- [Generalizing the SINDy approach with nested neural networks](https://mlsanity.com/paper/2404.15742/) (2024)
- [Neuro-Symbolic AI for Analytical Solutions of Differential Equations](https://mlsanity.com/paper/2502.01476/) (2025)
- [Parsing the Language of Expression: Enhancing Symbolic Regression with Domain-Aware Symbolic Priors](https://mlsanity.com/paper/2503.09592/) (2025)
- [FePySR: A Neural Feature Extraction Framework for Efficient and Scalable Symbolic Regression](https://mlsanity.com/paper/2605.12704/) (2026)

## Abstract (arXiv)

Many AutoML problems involve optimizing discrete objects under a black-box reward. Neural-guided search provides a flexible means of searching these combinatorial spaces using an autoregressive recurrent neural network. A major benefit of this approach is that builds up objects sequentially--this provides an opportunity to incorporate domain knowledge into the search by directly modifying the logits emitted during sampling. In this work, we formalize a framework for incorporating such in situ priors and constraints into neural-guided search, and provide sufficient conditions for enforcing constraints. We integrate several priors and constraints from existing works into this framework, propose several new ones, and demonstrate their efficacy in informing the task of symbolic regression.