Algorithm Selection with Zero Domain Knowledge via Text Embeddings

תקציר מקורי באנגליתarXiv:2604.19753v3 Announce Type: replace Abstract: 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, oft