# Streamliners for Answer Set Programming

arXiv: https://arxiv.org/abs/2604.19251 · HTML: https://mlsanity.com/paper/2604.19251/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 19 of its 42 references that are in the map.
- First posted on arXiv 21 Apr 2026.

## Builds on (most cited first, 19 of 19)

- [Chain-of-Thought Prompting Elicits Reasoning in Large Language Models](https://mlsanity.com/paper/2201.11903/) (2022)
- [Multi-shot ASP solving with clingo](https://mlsanity.com/paper/1705.09811/) (2017)
- [NeurASP: Embracing Neural Networks into Answer Set Programming](https://mlsanity.com/paper/2307.07700/) (2023)
- [ASP-Core-2 Input Language Format](https://mlsanity.com/paper/1911.04326/) (2019)
- [Leveraging Large Language Models to Generate Answer Set Programs](https://mlsanity.com/paper/2307.07699/) (2023)
- [Conflict-driven Inductive Logic Programming](https://mlsanity.com/paper/2101.00058/) (2020)
- [LLASP: Fine-tuning Large Language Models for Answer Set Programming](https://mlsanity.com/paper/2407.18723/) (2024)
- [Solver Scheduling via Answer Set Programming](https://mlsanity.com/paper/1401.1024/) (2014)
- [On Local Domain Symmetry for Model Expansion](https://mlsanity.com/paper/1608.02688/) (2016)
- [Towards Automatic Composition of ASP Programs from Natural Language Specifications](https://mlsanity.com/paper/2403.04541/) (2024)
- [Generating Streamlining Constraints with Large Language Models](https://mlsanity.com/paper/2408.10268/) (2024)
- [A Multi-Engine Approach to Answer Set Programming](https://mlsanity.com/paper/1306.4925/) (2013)
- [Answer Set Networks: Casting Answer Set Programming into Deep Learning](https://mlsanity.com/paper/2412.14814/) (2024)
- [Lifting Symmetry Breaking Constraints with Inductive Logic Programming](https://mlsanity.com/paper/2112.11806/) (2021)
- [BreakID: Static Symmetry Breaking for ASP (System Description)](https://mlsanity.com/paper/1608.08447/) (2016)
- [Conflict Generalisation in ASP: Learning Correct and Effective Non-Ground Constraints](https://mlsanity.com/paper/2008.03100/) (2020)
- [Efficient lifting of symmetry breaking constraints for complex combinatorial problems](https://mlsanity.com/paper/2205.07129/) (2022)
- [Logic-Based Benders Decomposition in Answer Set Programming for Chronic Outpatients Scheduling](https://mlsanity.com/paper/2305.11969/) (2023)
- [On the generalization of learned constraints for ASP solving in temporal domains](https://mlsanity.com/paper/2401.16124/) (2024)

## Abstract (arXiv)

Streamliner constraints reduce the search space of combinatorial problems by ruling out portions of the solution space. We adapt the StreamLLM approach, which uses Large Language Models (LLMs) to generate streamliners for Constraint Programming, to Answer Set Programming (ASP). Given an ASP encoding and a few small training instances, we prompt multiple LLMs to propose candidate constraints. Candidates that cause syntax errors, render satisfiable instances unsatisfiable, or degrade performance on all training instances are discarded. The surviving streamliners are evaluated together with the original encoding, and we report results for a virtual best encoding (VBE) that, for each instance, selects the fastest among the original encoding and its streamlined variants. On three ASP competition benchmarks (Partner Units Problem, Sokoban, Towers of Hanoi), the VBE achieves speedups of up to 4 to 5 times over the original encoding.