Algorithm Selection with Zero Domain Knowledge via Text Embeddings
Quick summary
arXiv: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 a
Key takeaways
- arXiv: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.
Why it matters
This model development creates a new option for users and a new testing obligation for developers. A fixed evaluation set comparing quality, cost and failure behavior is more useful than launch claims.
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