PRISM: Representative Instance Selection for LLM-Driven Automated Algorithm Design

A major bottleneck in LLM-based Automated Algorithm Design (AAD) lies in repeatedly evaluating candidate algorithms across a broad instance suite to guide search. However, evaluating candidate algorithms on a full, static suite often creates a major efficiency bottleneck: it incurs heavy computational costs while introducing evaluation noise and structural redundancy that can misguide search. In this paper, we show that full-suite evaluations contain substantial performance redundancy, which can be effectively uncovered by probing with a small set of reference algorithms. Inspired by this observation, we propose PRISM (Probe-guided Representative Instance Selection with Metric-aware feedback), a versatile plug-in for AAD. PRISM operates in two key steps: first, it uses initial algorithm responses as probes to evaluate instance representativeness and select a compact subset that generalizes to unseen algorithms. Second, it adapts the subset evaluations to downstream selection rules via metric-aware feedback, maintaining high selection resolution without requiring extra instance runs. Extensive experiments across diverse combinatorial optimization problems demonstrate that PRISM reduces physical algorithm-instance evaluations by a factor of up to 20 while preserving—and under scalar selection, often improving—search performance compared to full-suite evaluations.