# Herb.jl: A Unifying Program Synthesis Library

arXiv: https://arxiv.org/abs/2510.09726 · HTML: https://mlsanity.com/paper/2510.09726/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 10 of its 31 references that are in the map.
- First posted on arXiv 10 Oct 2025.

## Builds on (most cited first, 10 of 10)

- [DeepCoder: Learning to Write Programs](https://mlsanity.com/paper/1611.01989/) (2016)
- [Program Synthesis using Conflict-Driven Learning](https://mlsanity.com/paper/1711.08029/) (2017)
- [Just-in-Time Learning for Bottom-Up Enumerative Synthesis](https://mlsanity.com/paper/2010.08663/) (2020)
- [CrossBeam: Learning to Search in Bottom-Up Program Synthesis](https://mlsanity.com/paper/2203.10452/) (2022)
- [FrAngel: Component-Based Synthesis with Control Structures](https://mlsanity.com/paper/1811.05175/) (2018)
- [Program Synthesis with Best-First Bottom-Up Search](https://mlsanity.com/paper/2310.04327/) (2023)
- [Semantics-Guided Synthesis](https://mlsanity.com/paper/2008.09836/) (2020)
- [Structuring the Synthesis of Heap-Manipulating Programs - Extended Version](https://mlsanity.com/paper/1807.07022/) (2018)
- [AbstractBeam: Enhancing Bottom-Up Program Synthesis using Library Learning](https://mlsanity.com/paper/2405.17514/) (2024)
- [Modelling Program Spaces in Program Synthesis with Constraints](https://mlsanity.com/paper/2508.00005/) (2025)

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

- [Narcissus: Program Synthesis Using Context-Aware LLM Approximations](https://mlsanity.com/paper/2608.25657/) (2026)

## Abstract (arXiv)

Program synthesis -- the automatic generation of code given a specification -- is one of the most fundamental tasks in artificial intelligence (AI) and the dream of many programmers. Numerous synthesizers have been developed for program synthesis, offering different approaches to the exponentially growing program space. Although such state-of-the-art tools exist, reusing and adapting them remains tedious and time-consuming. We propose Herb.jl, a unifying program synthesis library written in Julia, to address these issues. Since current methods share similar building blocks, we aim to break down the underlying algorithms into extendable, reusable subcomponents. To demonstrate the benefits of using Herb.jl, we show how to implement a simple problem and grammar, and how to solve it with just a few lines of code.