GTA: Graph Theory Agent and Benchmark for Algorithmic Graph Reasoning with LLMs
Large Language Models (LLMs) are increasingly asked to reason over structured data such as graphs, yet how reliably they can carry out multi-step graph algorithms in language remains unclear. Existing evaluations tend to use simple tasks on small graphs, to score code generation rather than reasoning over the graph itself, or to fix a single input format. We introduce Graph Theory Bench (GT Bench), a benchmark covering 24 classical graph problems in 44 task-structure settings, with over 100,000 examples across four representations: natural language, structured language, adjacency list, and adjacency matrix. Evaluating eight LLMs on GT Bench shows that accuracy is strongly tied to the inpu...
| Authors | Zixiang Xu, Yanbo Wang, Chenxi Wang et al. |
|---|---|
| Primary category | cs.AI |
| Published | 10 September 2026 |
| Last updated at source | 10 September 2026 |
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