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LLM-guided program discovery uses multi-task evolution

By PulseAugur Editorial · [2 sources] ·

Researchers have developed a new framework called Evolutionary Multi-Task Optimization (EMO) for program discovery guided by large language models. The EMO-STA method first evolves a shared archive of programs across related tasks and then adapts these to specific target tasks. This approach demonstrates improvements over single-task evolution, particularly in transferring knowledge to unseen tasks and mitigating overfitting in scenarios with limited data. AI

IMPACT Introduces a novel method for improving program discovery efficiency and generalization using LLMs and evolutionary algorithms.

RANK_REASON The cluster contains an academic paper detailing a new method for program discovery.

Read on arXiv cs.LG →

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

COVERAGE [2]

  1. arXiv cs.LG TIER_1 English(EN) · Halil Alperen Gozeten, Xuechen Zhang, Emrullah Ildiz, Ege Onur Taga, Tara Javidi, Samet Oymak ·

    Evolutionary Multi-Task Optimization for LLM-Guided Program Discovery

    arXiv:2605.22613v1 Announce Type: new Abstract: Recent LLM-guided evolutionary search methods have shown that iterative program mutation can discover strong algorithms, but they typically optimize each task independently, even when related tasks share reusable structure. We intro…

  2. arXiv cs.LG TIER_1 English(EN) · Samet Oymak ·

    Evolutionary Multi-Task Optimization for LLM-Guided Program Discovery

    Recent LLM-guided evolutionary search methods have shown that iterative program mutation can discover strong algorithms, but they typically optimize each task independently, even when related tasks share reusable structure. We introduce Evolutionary Multi-Task Optimization (EMO) …

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