Unified Multi-Dimensional Benchmark for Complex Graph Reasoning in Large Language Models

arXiv:2608.12391 · cs.CL, cs.AI, cs.LG · Submitted 2026-08-02 · Read on arXiv

Fali Wang, Ali Al-Lawati, Iliyas Bektas, Jinxuan Fang, Alek Melenski, Tianxiang Zhao, Yao Ma, Suhang Wang

cs.CL, cs.AI, cs.LG

Submitted: 2026-08-02

Updated: 2026-08-14

Comments: Under review

Code: https://github.com/openai/tiktoken

License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/

Terminology

• Benchmark — 157× in this paper · explained in 2 episodes
• Graph Loading — 59× in this paper
• Task Description — 54× in this paper
• Exact Match — 27× in this paper
• GNNs — 25× in this paper · explained in 2 episodes
• Fine-Tuning — 21× in this paper · explained in 5 episodes
• Partial Credit — 18× in this paper
• Task Complexity — 16× in this paper
• Accuracy — 12× in this paper · explained in 1 episode
• Clustering — 12× in this paper · explained in 2 episodes
• LLM judge — 11× in this paper · explained in 2 episodes
• Instruction Tuning — 9× in this paper · explained in 2 episodes
• Generalization — 8× in this paper · explained in 13 episodes
• Graph Extraction — 8× in this paper
• Online Assessment — 8× in this paper
• RAG — 7× in this paper · explained in 3 episodes
• Robustness — 6× in this paper · explained in 5 episodes
• Benchmarking — 5× in this paper · explained in 3 episodes
• Consistency — 5× in this paper · explained in 3 episodes
• Adaptation — 4× in this paper · explained in 1 episode
• Core — 4× in this paper · explained in 2 episodes
• Large Language Models (LLMs) — 4× in this paper · explained in 6 episodes
• Adaptability — 3× in this paper · explained in 1 episode
• Adaptive Graph — 3× in this paper · explained in 1 episode
• Embeddings — 3× in this paper · explained in 2 episodes
• Graph Edit Distance — 3× in this paper
• Minimum Spanning Tree — 3× in this paper
• Self-consistency — 3× in this paper · explained in 2 episodes
• Trace — 3× in this paper · explained in 2 episodes
• Verification — 3× in this paper · explained in 2 episodes
• Aggregation — 2× in this paper · explained in 1 episode
• Alignment — 2× in this paper · explained in 1 episode
• Chain-of-Thought Prompting — 2× in this paper · explained in 2 episodes
• Multi-agent Collaboration — 2× in this paper · explained in 2 episodes
• Reinforcement Learning — 2× in this paper · explained in 4 episodes
• Semantic Understanding — 2× in this paper · explained in 2 episodes
• Structural Constraints — 2× in this paper · explained in 2 episodes
• Tokenization — 2× in this paper · explained in 2 episodes
• Transferability — 2× in this paper · explained in 4 episodes
• Abstraction — 1× in this paper · explained in 1 episode
• Adam — 1× in this paper · explained in 2 episodes
• Adjacency Matrix — 1× in this paper · explained in 1 episode
• Cosine Similarity — 1× in this paper · explained in 3 episodes
• Failure Modes — 1× in this paper · explained in 3 episodes
• Fidelity — 1× in this paper · explained in 2 episodes
• Generative Model — 1× in this paper · explained in 2 episodes
• Heterogeneous Graph — 1× in this paper · explained in 2 episodes
• Knowledge Graph — 1× in this paper · explained in 7 episodes
• Knowledge graphs — 1× in this paper · explained in 2 episodes
• LoRA Fine-Tuning — 1× in this paper · explained in 3 episodes
• Model-Agnostic — 1× in this paper · explained in 2 episodes
• Pruning — 1× in this paper · explained in 4 episodes
• Reasoning Traces — 1× in this paper · explained in 3 episodes
• Representation Learning — 1× in this paper · explained in 2 episodes
• Sparsity — 1× in this paper · explained in 4 episodes
• Transformer — 1× in this paper · explained in 2 episodes
• Verifier — 1× in this paper · explained in 2 episodes

Sources

• Qwen Technical Report
• NVIDIA Nemotron 3: Efficient and Open Intelligence
• Training Verifiers to Solve Math Word Problems
• The Llama 3 Herd of Models
• DeepSeek-R1: Incentivizing Reasoning Capability in LLMs via Reinforcement Learning
• GPT4Graph: Can Large Language Models Understand Graph Structured Data ? An Empirical Evaluation and Benchmarking
• Rethinking and Benchmarking Large Language Models for Graph Reasoning
• GraphTeam: Facilitating Large Language Model-based Graph Analysis via Multi-Agent Collaboration
• DeepSeek-V3 Technical Report
• Evaluating Large Language Models on Graphs: Performance Insights and Comparative Analysis
• GraphSkill: Documentation-Guided Hierarchical Retrieval-Augmented Coding for Complex Graph Reasoning
• Benchmarking Benchmark Leakage in Large Language Models
• Qwen3 Technical Report

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