# Algorithm Selection with Zero Domain Knowledge via Text Embeddings

arXiv: https://arxiv.org/abs/2604.19753 · HTML: https://mlsanity.com/paper/2604.19753/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 8 of its 21 references that are in the map.
- First posted on arXiv 20 Mar 2026.

## Builds on (most cited first, 8 of 8)

- [Talk like a Graph: Encoding Graphs for Large Language Models](https://mlsanity.com/paper/2310.04560/) (2023)
- [ASlib: A Benchmark Library for Algorithm Selection](https://mlsanity.com/paper/1506.02465/) (2015)
- [Large Language Model-Enhanced Algorithm Selection: Towards Comprehensive Algorithm Representation](https://mlsanity.com/paper/2311.13184/) (2023)
- [Automated Algorithm Selection: from Feature-Based to Feature-Free Approaches](https://mlsanity.com/paper/2203.13392/) (2022)
- [The Algorithm Selection Competitions 2015 and 2017](https://mlsanity.com/paper/1805.01214/) (2018)
- [sunny-as2: Enhancing SUNNY for Algorithm Selection](https://mlsanity.com/paper/2009.03107/) (2020)
- [GraSS: Combining Graph Neural Networks with Expert Knowledge for SAT Solver Selection](https://mlsanity.com/paper/2405.11024/) (2024)
- [Neural Solver Selection for Combinatorial Optimization](https://mlsanity.com/paper/2410.09693/) (2024)

## Abstract (arXiv)

We propose ZeroFolio, a feature-free approach to algorithm selection that uses pretrained text embeddings instead of hand-crafted instance features. It reads the raw instance file as plain text, embeds it with a pretrained embedding model, and selects an algorithm via weighted k-nearest neighbors. Our approach is based on the observation that pretrained embeddings can distinguish problem instances without any domain knowledge or task-specific training. ZeroFolio applies to any problem domain with text-based instance formats. We evaluate our approach on 11 ASlib scenarios spanning 7 domains (SAT, MaxSAT, QBF, ASP, CSP, MIP, and graph problems). ZeroFolio outperforms a random forest trained on hand-crafted features in 9 of 11 scenarios, often substantially, and in 8 of them with every serialization seed. It wins 8 of 11 scenarios against a per-scenario-tuned random forest. On the three scenarios with published AutoFolio results from the 2015 ICON Challenge, ZeroFolio comes within a small margin of AutoFolio without any per-scenario tuning. Our ablation study on SAT12-ALL shows that inverse-distance weighting and line shuffling improve performance. We further analyze the sensitivity of our approach to the serialization seed. On the SAT12-ALL scenario, where the random forest is stronger, both methods can be combined via soft voting to achieve further improvements.