10 papers
FORGE: Foundational Optimization Representations from Graph Embeddings
Zohair Shafi, Serdar Kadioglu
Combinatorial optimization problems are ubiquitous in science and engineering. Still, learning-based approaches to accelerate combinatorial optimization often require solving a lar…
Text2Model: Modeling Copilots for Text-to-Model Translation
Serdar Kadioglu, Karthik Uppuluri, Akash Singirikonda
There is growing interest in leveraging large language models (LLMs) for text-to-model translation and optimization tasks. This paper aims to advance this line of research by intro…
Learn2Zinc: Fine-tuning Small Language Models for Text-to-Model Translation in MiniZinc
Serdar Kadioglu, Karthik Uppuluri
Large language models excel at code generation for mainstream programming languages but struggle with rare, domain-specific languages such as MiniZinc, a constraint modeling langua…
BoolXLLM: LLM-Assisted Explainability for Boolean Models
Du Cheng, Serdar Kadioglu, Xin Wang
Interpretable machine learning aims to provide transparent models whose decision-making processes can be readily understood by humans. Recent advances in rule-based approaches, suc…
Transfer Learning from Foundational Optimization Embeddings to Unsupervised SAT Representations
Koyena Pal, Serdar Kadioglu
Foundational optimization embeddings have recently emerged as powerful pre-trained representations for mixed-integer programming (MIP) problems. These embeddings were shown to enab…
Gala: Global LLM Agents for Text-to-Model Translation
Junyang Cai, Serdar Kadioglu, Bistra Dilkina
Natural language descriptions of optimization or satisfaction problems are challenging to translate into correct MiniZinc models, as this process demands both logical reasoning and…