2 citations · 6 across the 10 of their papers we have counts for
10 papers
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…
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…
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…
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…
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…
Spiral of Silence in Large Language Model Agents
Mingze Zhong, Meng Fang, Zijing Shi +5
The Spiral of Silence (SoS) theory holds that individuals with minority views often refrain from speaking out for fear of social isolation, enabling majority positions to dominate…
Distill Not Only Data but Also Rewards: Can Smaller Language Models Surpass Larger Ones?
Yudi Zhang, Lu Wang, Meng Fang +8
Distilling large language models (LLMs) typically involves transferring the teacher model's responses through supervised fine-tuning (SFT). However, this approach neglects the pote…
Learning to Discuss Strategically: A Case Study on One Night Ultimate Werewolf
Xuanfa Jin, Ziyan Wang, Yali Du +3
Communication is a fundamental aspect of human society, facilitating the exchange of information and beliefs among people. Despite the advancements in large language models (LLMs),…
Large Language Models Are Neurosymbolic Reasoners
Meng Fang, Shilong Deng, Yudi Zhang +4
A wide range of real-world applications is characterized by their symbolic nature, necessitating a strong capability for symbolic reasoning. This paper investigates the potential a…
How Do Large Language Models Capture the Ever-changing World Knowledge? A Review of Recent Advances
Zihan Zhang, Meng Fang, Ling Chen +2
Although large language models (LLMs) are impressive in solving various tasks, they can quickly be outdated after deployment. Maintaining their up-to-date status is a pressing conc…