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Ziyang Xiao

5 accepted papers

2026

DeepOR: A Deep Reasoning Foundation Model for Optimization Modeling

AAAI 2026technical

Optimization modeling plays a critical role in supporting optimal decision-making across various domains. Previous works have demonstrated that large language models (LLMs) tailored for optimization modeling have significantly automated and simplified this process. However, these models typically em

Cited by 0SourcePDFScholar
2025

A Survey of Optimization Modeling Meets LLMs: Progress and Future Directions

IJCAI 2025

By virtue of its great utility in solving real-world problems, optimization modeling has been widely employed for optimal decision-making across various sectors, but it requires substantial expertise from operations research professionals. With the advent of large language models (LLMs), new opportu

Cited by 0SourcePDFScholar
2024

Chain-of-Experts: When LLMs Meet Complex Operations Research Problems

ICLR 2024poster

Large language models (LLMs) have emerged as powerful techniques for various NLP tasks, such as mathematical reasoning and plan generation. In this paper, we study automatic modeling and programming for complex operation research (OR) problems, so as to alleviate the heavy dependence on domain exper…

Cited by 50SourcePDFScholar
2024

Enhancing LLM Reasoning via Vision-Augmented Prompting

NeurIPS 2024spotlight

Verbal and visual-spatial information processing are two critical subsystems that activate different brain regions and often collaborate together for cognitive reasoning. Despite the rapid advancement of LLM-based reasoning, the mainstream frameworks, such as Chain-of-Thought (CoT) and its variants,…

Cited by 1SourcePDFScholar
2023

Neural TSP Solver with Progressive Distillation

AAAI 2023technical

Travelling salesman problem (TSP) is NP-Hard with exponential search space. Recently, the adoption of encoder-decoder models as neural TSP solvers has emerged as an attractive topic because they can instantly obtain near-optimal results for small-scale instances. Nevertheless, their training effici…

Cited by 13SourcePDFScholar