Author

Oguzhan Gungordu

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#artificial intelligence Preprint Oct 2026

EPOCH: Coverage-Aware Evolution with Experiential Preference Optimization for LLM-based Automated Heuristic Design

LLM-based automated heuristic design (AHD) discovers heuristics for combinatorial optimization problems (COPs), where an LLM generates candidates in an evolutionary search. Existing frameworks freeze the generator or fine-tune it on a scalar fitness signal hiding which instances each heuristic covers. Reflection-based...

Oguzhan Gungordu, Ayse Tursucular, Siheng Xiong et al. · 0 citations

Preprint Aug 2026

DIVE: Unlocking Self-Improvement in Frozen Language Models Through Diversity-Driven Skill Evolution

DIVE is introduced, a diversity-driven framework that enables frozen LLMs to improve by evolving persistent natural-language skills from task experience and verifier feedback and consistently outperforms existing reasoning methods, prompt-optimization approaches, skill-development frameworks, and memory-based baselines...

Siheng Xiong, Ali Payani, Oguzhan Gungordu et al. · 3 citations

Preprint Jul 2026

MOSAIC: Adversarial Co-evolution of Specialist Heuristics and Problem Instances for LLM-based Automated Heuristic Design

MOSAIC is proposed, a grid-based framework that adversarially co-evolves problem instances and specialist heuristics inside a Quality-Diversity archive indexed by structural instance features that consistently outperforms state-of-the-art LLM-based AHD methods.

Oguzhan Gungordu, Siheng Xiong, F. Fekri · 0 citations