most citedGraph Machine Learning in the Era of Large Language Models (LLMs)

5 citations · 5 across the 4 of their papers we have counts for

collaborators

6 papers

cs.CL2026

Measuring Maximum Activations in Open Large Language Models

Luxuan Chen, Han Tian, Xinran Chen +9

The dynamic range of activations is a first-order constraint for low-bit quantization, activation scaling, and stable LLM inference. Prior work characterized outlier features and m…

cs.CL2026

EndPrompt: Efficient Long-Context Extension via Terminal Anchoring

Han Tian, Luxuan Chen, Xinran Chen +10

Extending the context window of large language models typically requires training on sequences at the target length, incurring quadratic memory and computational costs that make lo…

cs.LG20265 cited

Graph Machine Learning in the Era of Large Language Models (LLMs)

Shijie Wang, Jiani Huang, Zhikai Chen +8

Graphs play an important role in representing complex relationships in various domains like social networks, knowledge graphs, and molecular discovery. With the advent of deep lear…

cs.IR2026

RAG-Enhanced Large Language Models for Dynamic Content Expiration Prediction in Web Search

Tingyu Chen, Wenkai Zhang, Li Gao +4

In commercial web search, aligning content freshness with user intent remains challenging due to the highly varied lifespans of information. Traditional industrial approaches rely…

cs.IR2026

Towards Next-Generation Recommender Systems: A Benchmark for Personalized Recommendation Assistant with LLMs

Jiani Huang, Shijie Wang, Liang-bo Ning +4

Recommender systems (RecSys) are widely used across various modern digital platforms and have garnered significant attention. Traditional recommender systems usually focus only on…

cs.IR2025

Knowledge Graph Retrieval-Augmented Generation for LLM-based Recommendation

Shijie Wang, Wenqi Fan, Yue Feng +4

Recommender systems have become increasingly vital in our daily lives, helping to alleviate the problem of information overload across various user-oriented online services. The em…