47 citations · 54 across the 6 of their papers we have counts for
6 papers
CoME: Empowering Channel-of-Mobile-Experts with Informative Hybrid-Capabilities Reasoning
Yuxuan Liu, Weikai Xu, Kun Huang +9
Mobile Agents can autonomously execute user instructions, which requires hybrid-capabilities reasoning, including screen summary, subtask planning, action decision and action funct…
Towards Effective and Efficient Continual Pre-training of Large Language Models
Jie Chen, Zhipeng Chen, Jiapeng Wang +16
Continual pre-training (CPT) has been an important approach for adapting language models to specific domains or tasks. To make the CPT approach more traceable, this paper presents…
Very Large-Scale Multi-Agent Simulation in AgentScope
Xuchen Pan, Dawei Gao, Yuexiang Xie +6
Recent advances in large language models (LLMs) have opened new avenues for applying multi-agent systems in very large-scale simulations. However, there remain several challenges w…
Revisiting Local Computation of PageRank: Simple and Optimal
Hanzhi Wang, Zhewei Wei, Ji-Rong Wen +1
We revisit the classic local graph exploration algorithm ApproxContributions proposed by Andersen, Borgs, Chayes, Hopcroft, Mirrokni, and Teng (WAW '07, Internet Math. '08) for com…
Efficient Algorithms for Personalized PageRank Computation: A Survey
Mingji Yang, Hanzhi Wang, Zhewei Wei +2
Personalized PageRank (PPR) is a traditional measure for node proximity on large graphs. For a pair of nodes and , the PPR value equals the probability that an -…
Approximating Single-Source Personalized PageRank with Absolute Error Guarantees
Zhewei Wei, Ji-Rong Wen, Mingji Yang
Personalized PageRank (PPR) is an extensively studied and applied node proximity measure in graphs. For a pair of nodes and on a graph , the PPR value is…