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20232026
most citedHow Do Large Language Models Capture the Ever-changing World Knowledge? A Review of Recent Advances

2 citations · 6 across the 10 of their papers we have counts for

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cs.AI2026

Bilevel Coordinated Reflection: A Game-Theoretic Approach to Multi-Agent LLM Systems

Yihang Chen, Yuxiang Chen, Yuxuan Huang +3

Multi-agent LLM systems commonly use an orchestrator to decompose a task for a team of workers and then improve through textual reflection. Despite strong empirical results, these…

cs.AI2026

Web2BigTable: A Bi-Level Multi-Agent LLM System for Internet-Scale Information Search and Extraction

Yuxuan Huang, Yihang Chen, Zhiyuan He +6

Agentic web search increasingly faces two distinct demands: deep reasoning over a single target, and structured aggregation across many entities and heterogeneous sources. Current…

cs.AI2026

From Skills to Talent: Organising Heterogeneous Agents as a Real-World Company

Zhengxu Yu, Yu Fu, Zhiyuan He +5

Individual agent capabilities have advanced rapidly through modular skills and tool integrations, yet multi-agent systems remain constrained by fixed team structures, tightly coupl…

cs.AI2026

InfoSeeker: A Scalable Hierarchical Parallel Agent Framework for Web Information Seeking

Ka Yiu Lee, Yuxuan Huang, Zhiyuan He +5

Recent agentic search systems have made substantial progress by emphasising deep, multi-step reasoning. However, this focus often overlooks the challenges of wide-scale information…

cs.CL2026

Vision-Language Reasoning for Geolocalization: A Reinforcement Learning Approach

Biao Wu, Meng Fang, Ling Chen +3

Recent advances in vision-language models have opened up new possibilities for reasoning-driven image geolocalization. However, existing approaches often rely on synthetic reasonin…