Kai Guo

According to our database, Kai Guo authored at least 23 papers between 2018 and 2026.

Collaborative distances:

• Dijkstra number of four.
• Erdős number of four.

2026

When Do Hallucinations Arise? A Graph Perspective on the Evolution of Path Reuse and Path Compression.

Xinnan Dai, Kai Yang, Cheng Luo, Shenglai Zeng, Kai Guo, Jiliang Tang

CoRR, April, 2026

Fix Before Search: Benchmarking Agentic Query Visual Pre-processing in Multimodal Retrieval-augmented Generation.

Jiankun Zhang, Shenglai Zeng, Kai Guo, Xinnan Dai, Hui Liu, Jiliang Tang, Yi Chang

CoRR, February, 2026

Attn-GS: Attention-Guided Context Compression for Efficient Personalized LLMs.

Shenglai Zeng, Tianqi Zheng, Chuan Tian, Dante Everaert, Yau-Shian Wang, Yupin Huang, Michael J. Morais, Rohit Patki, Jinjin Tian, Xinnan Dai, Kai Guo, Monica Xiao Cheng, Hui Liu

CoRR, February, 2026

Reasoning by Exploration: A Unified Approach to Retrieval and Generation over Graphs.

Haoyu Han, Kai Guo, Harry Shomer, Yu Wang, Yucheng Chu, Hang Li, Li Ma, Jiliang Tang

Proceedings of the ACM Web Conference 2026, 2026

2025

GraphGhost: Tracing Structures Behind Large Language Models.

Xinnan Dai, Kai Guo, Chung-Hsiang Lo, Shenglai Zeng, Jiayuan Ding, Dongsheng Luo, Subhabrata Mukherjee, Jiliang Tang

CoRR, October, 2025

Beyond Static Retrieval: Opportunities and Pitfalls of Iterative Retrieval in GraphRAG.

Kai Guo, Xinnan Dai, Shenglai Zeng, Harry Shomer, Haoyu Han, Yu Wang, Jiliang Tang

CoRR, September, 2025

Uncovering Graph Reasoning in Decoder-only Transformers with Circuit Tracing.

Xinnan Dai, Chung-Hsiang Lo, Kai Guo, Shenglai Zeng, Dongsheng Luo, Jiliang Tang

CoRR, September, 2025

From Sequence to Structure: Uncovering Substructure Reasoning in Transformers.

Xinnan Dai, Kai Yang, Jay Revolinsky, Kai Guo, Aoran Wang, Bohang Zhang, Jiliang Tang

CoRR, July, 2025

RAG vs. GraphRAG: A Systematic Evaluation and Key Insights.

Haoyu Han, Harry Shomer, Yu Wang, Yongjia Lei, Kai Guo, Zhigang Hua, Bo Long, Hui Liu, Jiliang Tang

CoRR, February, 2025

Retrieval-Augmented Generation with Graphs (GraphRAG).

Haoyu Han, Yu Wang, Harry Shomer, Kai Guo, Jiayuan Ding, Yongjia Lei, Mahantesh Halappanavar, Ryan A. Rossi, Subhabrata Mukherjee, Xianfeng Tang, Qi He, Zhigang Hua, Bo Long, Tong Zhao, Neil Shah, Amin Javari, Yinglong Xia, Jiliang Tang

CoRR, January, 2025

Aligning large language models and geometric deep models for protein representation.

Dong Shu, Bingbing Duan, Kai Guo, Kaixiong Zhou, Jiliang Tang, Mengnan Du

Patterns, 2025

Empowering GraphRAG with Knowledge Filtering and Integration.

Kai Guo, Harry Shomer, Shenglai Zeng, Haoyu Han, Yu Wang, Jiliang Tang

Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing, 2025

Towards Context-Robust LLMs: A Gated Representation Fine-tuning Approach.

Shenglai Zeng, Pengfei He, Kai Guo, Tianqi Zheng, Hanqing Lu, Yue Xing, Hui Liu

Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), 2025

2024

Breaking the curse of dimensional collapse in graph contrastive learning: A whitening perspective.

Yang Tao, Kai Guo, Yizhen Zheng, Shirui Pan, Xiaofeng Cao, Yi Chang

Inf. Sci., February, 2024

Exploring the Alignment Landscape: LLMs and Geometric Deep Models in Protein Representation.

Dong Shu, Bingbing Duan, Kai Guo, Kaixiong Zhou, Jiliang Tang, Mengnan Du

CoRR, 2024

Learning on Graphs with Large Language Models(LLMs): A Deep Dive into Model Robustness.

Kai Guo, Zewen Liu, Zhikai Chen, Hongzhi Wen, Wei Jin, Jiliang Tang, Yi Chang

CoRR, 2024

Investigating Out-of-Distribution Generalization of GNNs: An Architecture Perspective.

Kai Guo, Hongzhi Wen, Wei Jin, Yaming Guo, Jiliang Tang, Yi Chang

Proceedings of the 30th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, 2024

2023

Taming over-smoothing representation on heterophilic graphs.

Kai Guo, Xiaofeng Cao, Zhining Liu, Yi Chang

Inf. Sci., November, 2023

Out-of-Distribution Generalization of Federated Learning via Implicit Invariant Relationships.

Yaming Guo, Kai Guo, Xiaofeng Cao, Tieru Wu, Yi Chang

Proceedings of the International Conference on Machine Learning, 2023

2022

Orthogonal Graph Neural Networks.

Kai Guo, Kaixiong Zhou, Xia Hu, Yu Li, Yi Chang, Xin Wang

Proceedings of the Thirty-Sixth AAAI Conference on Artificial Intelligence, 2022

2021

IMBENS: Ensemble Class-imbalanced Learning in Python.

Zhining Liu, Zhepei Wei, Erxin Yu, Qiang Huang, Kai Guo, Boyang Yu, Zhaonian Cai, Hangting Ye, Wei Cao, Jiang Bian, Pengfei Wei, Jing Jiang, Yi Chang

CoRR, 2021

CAP: Co-Adversarial Perturbation on Weights and Features for Improving Generalization of Graph Neural Networks.

Haotian Xue, Kaixiong Zhou, Tianlong Chen, Kai Guo, Xia Hu, Yi Chang, Xin Wang

CoRR, 2021

2018

Cloud-ocean computing: a new scheme of marine data processing.

Jun Liu, Kai Guo, Jun-Hong Cui

Proceedings of the Thirteenth ACM International Conference on Underwater Networks & Systems, 2018