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cs.AI2026

Improving LLM Reasoning with Homophily-aware Structural and Semantic Text-Attributed Graph Compression

Zijun Di, Bin Lu, Huquan Kang +5

Large language models (LLMs) have demonstrated promising capabilities in Text-Attributed Graph (TAG) understanding. Recent studies typically focus on verbalizing the graph structur…

cs.AI2026

Compass: Navigating Global Marine Lead Data Integration through Expert-Guided LLM Agent

Yiming Liu, Bin Lu, Meng Jin +6

Marine lead (Pb) and its isotopes are critical tracers for ocean circulation and anthropogenic pollution, yet in-situ observations remain costly and sparse. While vast historical r…

cs.AI2026

SLASH the Sink: Sharpening Structural Attention Inside LLMs

Yiming Liu, Bin Lu, Xinbing Wang +2

Large Language Models (LLMs) show remarkable semantic understanding but often struggle with structural understanding when processing graph topologies in a serialized format. Existi…

cs.AI2026

Inductive Reasoning for Temporal Knowledge Graphs with Emerging Entities

Ze Zhao, Yuhui He, Lyuwen Wu +6

Reasoning on Temporal Knowledge Graphs (TKGs) is essential for predicting future events and time-aware facts. While existing methods are effective at capturing relational dynamics,…

cs.AI2025

CHAINSFORMER: Numerical Reasoning on Knowledge Graphs from a Chain Perspective

Ze Zhao, Bin Lu, Xiaoying Gan +3

Reasoning over Knowledge Graphs (KGs) plays a pivotal role in knowledge graph completion or question answering systems, providing richer and more accurate triples and attributes. A…