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LLM-based heuristic design framework RAISE improves robustness to distribution shifts

By PulseAugur Editorial · [2 sources] ·

Researchers have developed RAISE, a new framework for designing heuristics using Large Language Models (LLMs) that is more robust to real-world distribution shifts. Unlike previous methods that optimize for fixed training sets, RAISE integrates a constrained adversarial instance search into its evolutionary loop. This approach allows the system to identify difficult instances within a defined range of the training distribution, ensuring consistent performance across various scenarios. Experiments on Online Bin Packing, Online Job Shop Scheduling, and Online Vehicle Routing demonstrated that RAISE significantly outperforms existing LLM-based methods, which can degrade substantially under distribution shifts. AI

IMPACT Enhances the reliability of LLM-generated heuristics in real-world applications by improving their resilience to distributional shifts.

RANK_REASON The cluster contains a research paper detailing a new framework for automated heuristic design.

Read on arXiv cs.AI →

AI-generated summary · Google Gemini · from 2 sources. How we write summaries →

LLM-based heuristic design framework RAISE improves robustness to distribution shifts

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The cluster contains a research paper detailing a new framework for automated heuristic design.
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COVERAGE [2]

  1. arXiv cs.AI TIER_1 English(EN) · Fei Liu, Alessio Figalli, Patrick Owen, Nicola Serra ·

    RAISE: LLM-based Automated Heuristic Design with Robust Adversary Instance Search

    arXiv:2606.31801v1 Announce Type: new Abstract: Automated Heuristic Design (AHD) with Large Language Models (LLMs) has shown remarkable progress in discovering high-quality heuristics. However, existing LLM-based AHD methods optimize heuristics for a fixed training instance set a…

  2. arXiv cs.AI TIER_1 English(EN) · Nicola Serra ·

    RAISE: LLM-based Automated Heuristic Design with Robust Adversary Instance Search

    Automated Heuristic Design (AHD) with Large Language Models (LLMs) has shown remarkable progress in discovering high-quality heuristics. However, existing LLM-based AHD methods optimize heuristics for a fixed training instance set and may fail catastrophically when deployed under…

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