Algorithm Selection with Zero Domain Knowledge via Text Embeddings

2026-03-20

arXiv.org

AI Summary

This work addresses the challenge of general-purpose algorithm selection without requiring domain-specific knowledge. It proposes ZeroFolio, a novel framework that treats problem instances as raw text and leverages pretrained text embeddings to generate representations, thereby eliminating the need for handcrafted features or task-specific training. Algorithm selection is performed via a weighted k-nearest neighbors approach based on Manhattan distance. ZeroFolio constitutes the first fully feature-free and tuning-free algorithm selection method, applicable across diverse problem domains represented in textual form. Evaluated on 11 ASlib scenarios, ZeroFolio outperforms random forest models using handcrafted features in 9 cases and surpasses scenario-tuned random forests in 8, achieving performance comparable to AutoFolio—all without any configuration or hyperparameter tuning.

Abstract

We propose a feature-free approach to algorithm selection that replaces hand-crafted instance features with pretrained text embeddings. Our method, ZeroFolio, proceeds in three steps: 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. The key to our approach is the observation that pretrained embeddings produce representations that distinguish problem instances without any domain knowledge or task-specific training. This allows us to apply the same three-step pipeline (serialize, embed, select) across diverse problem domains 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). Our experiments show that this approach outperforms a random forest trained on hand-crafted features in 10 of 11 scenarios with a single fixed configuration, and in all 11 with two-seed voting; the margin is often substantial. Our ablation study shows that inverse-distance weighting, line shuffling, and Manhattan distance are the key design choices. On scenarios where both selectors are competitive, combining embeddings with hand-crafted features via soft voting yields further improvements.

Problem

• algorithm selection
• zero domain knowledge
• text embeddings
• instance features
• pretrained embeddings

Innovation

• algorithm selection
• text embeddings
• zero domain knowledge
• feature-free
• pretrained models