most citedFake Artificial Intelligence Generated Contents (FAIGC): A Survey of Theories, Detection Methods, and Opportunities

5 citations · 12 across the 4 of their papers we have counts for

collaborators

9 papers

cs.CL2025

MemOS: A Memory OS for AI System

Zhiyu Li, Chenyang Xi, Chunyu Li +36

Large Language Models (LLMs) have become an essential infrastructure for Artificial General Intelligence (AGI), yet their lack of well-defined memory management systems hinders the…

cs.CL20251 cited

MemOS: An Operating System for Memory-Augmented Generation (MAG) in Large Language Models

Zhiyu Li, Shichao Song, Hanyu Wang +19

Large Language Models (LLMs) have emerged as foundational infrastructure in the pursuit of Artificial General Intelligence (AGI). Despite their remarkable capabilities in language…

cs.CL2025

SEAP: Training-free Sparse Expert Activation Pruning Unlock the Brainpower of Large Language Models

Xun Liang, Hanyu Wang, Huayi Lai +7

Large Language Models have achieved remarkable success across various natural language processing tasks, yet their high computational cost during inference remains a major bottlene…

cs.CL2025

SurveyX: Academic Survey Automation via Large Language Models

Xun Liang, Jiawei Yang, Yezhaohui Wang +11

Large Language Models (LLMs) have demonstrated exceptional comprehension capabilities and a vast knowledge base, suggesting that LLMs can serve as efficient tools for automated sur…

cs.CR20251 cited

SafeRAG: Benchmarking Security in Retrieval-Augmented Generation of Large Language Model

Xun Liang, Simin Niu, Zhiyu Li +8

The indexing-retrieval-generation paradigm of retrieval-augmented generation (RAG) has been highly successful in solving knowledge-intensive tasks by integrating external knowledge…

cs.CL2024

TurtleBench: Evaluating Top Language Models via Real-World Yes/No Puzzles

Qingchen Yu, Shichao Song, Ke Fang +5

As the application of Large Language Models (LLMs) expands, the demand for reliable evaluations increases. Existing LLM evaluation benchmarks primarily rely on static datasets, mak…