AGENTIC-SYSTEMSApril 23, 20261 min read6 views

Algorithm Selection with Zero Domain Knowledge via Text Embeddings

Researchers propose ZeroFolio, a feature-free approach that uses pretrained text embeddings to select optimal algorithms without manual feature engineering. Experiments show it outperforms traditional models across 11 scenarios, offering a domain-agnostic solution for complex problem-solving.

Computer Science > Artificial Intelligence

Title: Algorithm Selection with Zero Domain Knowledge via Text Embeddings

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.

Source: arXiv cs.AI Recent

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