3 citations · 10 across the 9 of their papers we have counts for
9 papers
The Devil Behind Moltbook: Anthropic Safety is Always Vanishing in Self-Evolving AI Societies
Chenxu Wang, Chaozhuo Li, Songyang Liu +10
The emergence of multi-agent systems built from large language models (LLMs) offers a promising paradigm for scalable collective intelligence and self-evolution. Ideally, such syst…
Orchestrating Heterogeneous Experts: A Scalable MoE Framework with Anisotropy-Preserving Fusion
Ye Liu, Xu Chen, Wuji Chen +1
In cross-border e-commerce, search relevance modeling faces the dual challenge of extreme linguistic diversity and fine-grained semantic nuances. Existing approaches typically rely…
GraphPilot: GUI Task Automation with One-Step LLM Reasoning Powered by Knowledge Graph
Mingxian Yu, Siqi Luo, Xu Chen
Mobile graphical user interface (GUI) agents are designed to automate everyday tasks on smartphones. Recent advances in large language models (LLMs) have significantly enhanced the…
How Does Personalized Memory Shape LLM Behavior? Benchmarking Rational Preference Utilization in Personalized Assistants
Xueyang Feng, Weinan Gan, Xu Chen +2
Large language model (LLM)-powered assistants have recently integrated memory mechanisms that record user preferences, leading to more personalized and user-aligned responses. Howe…
Beyond Benchmarks: The Economics of AI Inference
Boqin Zhuang, Jiacheng Qiao, Mingqian Liu +8
The inference cost of Large Language Models (LLMs) has become a critical factor in determining their commercial viability and widespread adoption. This paper introduces a quantitat…
LLM-based Agents Suffer from Hallucinations: A Survey of Taxonomy, Methods, and Directions
Xixun Lin, Yucheng Ning, Jingwen Zhang +21
Driven by the rapid advancements of Large Language Models (LLMs), LLM-based agents have emerged as powerful intelligent systems capable of human-like cognition, reasoning, and inte…
WiNGPT-3.0 Technical Report
Boqin Zhuang, Chenxiao Song, Huitong Lu +10
Current Large Language Models (LLMs) exhibit significant limitations, notably in structured, interpretable, and verifiable medical reasoning, alongside practical deployment challen…
Model-in-the-Loop (MILO): Accelerating Multimodal AI Data Annotation with LLMs
Yifan Wang, David Stevens, Pranay Shah +10
The growing demand for AI training data has transformed data annotation into a global industry, but traditional approaches relying on human annotators are often time-consuming, lab…
TRANSOM: An Efficient Fault-Tolerant System for Training LLMs
Baodong Wu, Lei Xia, Qingping Li +6
Large language models (LLMs) with hundreds of billions or trillions of parameters, represented by chatGPT, have achieved profound impact on various fields. However, training LLMs w…