Graph Large Language Models (GraphLLMs) enable powerful semantic understanding and reasoning over Text-Attributed Graphs (TAGs), while their semantic reasoning may exhibit vulnerabilities to perturbations in node text and graph structure under adversarial attack evaluation. Existing attacks typically construct textual...
Wen-Da Li, Tong-Ya Zheng, Shunyu Liu et al.· 0 citations
Memory ranking is the stage in personalized Large Language Models (LLMs) that decides which of the candidate memories returned by a retriever enter the limited context, so that the answer is grounded in the right parts of the user's history. Existing memory rankers operate in a single pass, scoring each candidate once...
Peng-Yue Jia, Shi-Da Yan, Shunyu Liu et al.· 0 citations
This work proposes ChainSleuth, a graph-tool-augmented agent that iteratively reasons, invokes graph tools, and updates its tracing trajectory, enabling adaptive information acquisition and recovery from erroneous paths in multi-hop fund tracing.
Ru-Nang He, Tong-Ya Zheng, Jing-Yao Cao et al.· 0 citations
Graph agents extend large language models (LLMs) with the ability to actively explore and reason over knowledge graphs through multi-step interactions with graph tools. However, training capable graph agents typically requires large collections of question-answer pairs and reasoning trajectories, whose manual construct...
Wei Jiang, Yu-Chen Ying, Rui Wang et al.· 0 citations
SpatialCLI is proposed, a framework that teaches VLMs to reason with spatial tools and progressively internalize the specialist perceptual capabilities they provide and introduces SpatialCLI-Bench, a 516-example benchmark for compositional perception across localization, segmentation, depth, and pose.
Yang Zhou, Zixuan Huang, Sunzhu Li et al.· arXiv.org· 0 citations
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