Large language models show great potential in unstructured data understanding, but still face significant challenges with graphs due to their structural hallucination. Existing approaches mainly either verbalize graphs into natural language, which leads to excessive token consumption and scattered attention, or transform graphs into trainable continuous embeddings (i.e., soft prompt), but exhibit severe misalignment with original text tokens. To solve this problem, we propose to incorporate one special token <SOG_k> to fully represent the Structure Of Graph within a unified token space, facilitating explicit topology input and structural information sharing. Specifically, we propose a topology-aware structural tokenizer that maps each graph topology into a highly selective single token. Afterwards, we construct a set of hybrid structure Question-Answering corpora to align new structural tokens with existing text tokens. With this approach, <SOG_k> empowers LLMs to understand, generate, and reason in a concise and accurate manner. Extensive experiments on five graph-level benchmarks demonstrate the superiority of our method, achieving a performance improvement of 9.9% to 41.4% compared to the baselines while exhibiting interpretability and consistency. Furthermore, our method provides a flexible extension to node-level tasks, enabling both global and local structural understanding. The codebase is publicly available at https://github.com/Jingyao-Wu/SOG.
Scaling-Aware Adapter for Structure-Grounded LLM Reasoning
Department of Computer Science, Western University, London, Canada（加拿大伦敦西方大学计算机科学系）；Department of Biochemistry, Western University, London, Canada（加拿大伦敦西方大学生物化学系）
GraSAME: Injecting Token-Level Structural Information to Pretrained Language Models via Graph-guided Self-Attention Mechanism
Karlsruhe Institute of Technology（卡尔斯鲁厄理工学院）；Center for Scalable Data Analytics and Artificial Intelligence (ScaDS.AI)（可扩展数据分析与人工智能中心（ScaDS.AI））；TU Dresden（德累斯顿工业大学）
Overthinking Loops in Agents: A Structural Risk via MCP Tools
Yonsei University（延世大学）；Department of Data Science, Ewha Womans University（成均馆大学数据科学系）；Division of Language and AI, Hankuk University of Foreign Studies (HUFS)（韩国外国语大学语言与人工智能系）
Attention Meets Reachability: Structural Equivalence and Efficiency in Grammar-Constrained LLM Decoding
Department of Computer Engineering, Bahçeşehir University, Istanbul, Turkey（计算机工程系，巴切希尔大学，伊斯坦布尔，土耳其）；Department of Industrial Engineering, Bahçeşehir University, Istanbul, Turkey（工业工程系，巴切希尔大学，伊斯坦布尔，土耳其）
StructKV: Preserving the Structural Skeleton for Scalable Long-Context Inference
UCAS-Terminus AI Lab, University of Chinese Academy of Sciences, China（中国科学院大学UCAS-Terminus AI实验室）；National Engineering Research Center for Software Engineering, Peking University（北京大学国家软件工程研究中心）
On the Token Value Inequality in Efficient Reasoning
Purdue University（普渡大学）；Rochester Institute of Technology（罗切斯特理工学院）；MIT-IBM Watson AI Lab（MIT-IBM沃森人工智能实验室）；University of Missouri-Kansas City（密苏里大学堪萨斯城分校）；Rutgers University（罗格斯大学）；Meta AI
DiffuTester: Accelerating Unit Test Generation for Diffusion LLMs via Mining Structural Pattern
College of AI, Tsinghua University（清华大学人工智能学院）；School of Software, Beihang University（北航软件学院）；School of Computer Science and Engineering, Beihang University（北航计算机科学与工程学院）
AtomDisc: An Atom-level Tokenizer that Boosts Molecular LLMs and Reveals Structure--Property Associations
The Thrust of Artificial Intelligence, The Hong Kong University of Science and Technology (Guangzhou)（人工智能动力部，香港科学与技术大学（广州））；The College of Computer Science and Technology, The Nanjing University of Aeronautics and Astronautics（计算机科学与技术学院，南京航空航天大学）
