40 citations · 93 across the 25 of their papers we have counts for
8 papers · 1 filter
A Responsibility-Centered Coordination Mechanism for Decentralized Multi-Agent Systems
Thi-Nhung Nguyen, Viet-Thanh Pham, Linhao Luo +2
Centralized orchestration provides comprehensive multi-agent coordination but creates bottlenecks in scalability, adaptability, and collective performance. Decentralized approaches…
Graph Engineering in the Era of LLM Agents: From Individual Intelligence to System Intelligence
Yuyuan Feng, Zhishang Xiang, Chaobin Yang +32
LLMs have evolved from language generators to autonomous agents capable of complex, long-horizon tasks. This evolution has produced paradigms including Prompt Engineering to elicit…
E-Orch: Towards Effective, Efficient, and Extensible Agentic Orchestration with Reinforcement Learning
Junnan Liu, Linhao Luo, Thuy-Trang Vu +1
Agentic orchestration enables multiple autonomous agents to solve complex tasks through adaptive decomposition, delegation, and execution. However, existing orchestrators often rel…
TriAlign: Towards Universal Truth Consistency in Personalized LLM Alignment
Thi-Nhung Nguyen, Linhao Luo, Rollin Omari +3
Personalized large language models adapt responses to users' preferences and social attributes, but can introduce substantial universal truth inconsistencies across social groups,…
Toward Native Multimodal Modeling: A Roadmap
Siyu An, Junru Lu, Junnan Dong +18
Multimodal modeling represents a vital step from modality-agnostic reasoning toward world modeling. While early approaches predominantly rely on late-fusion that assembles encoders…
MATO: Multi-objective Personalized Alignment with Test-time Optimization for Large Language Models
Linhao Luo, Thuy-Trang Vu, Van-Anh Nguyen +3
Aligning large language models (LLMs) with diverse and multifaceted user preferences is a fundamental challenge in personalized AI systems. Existing multi-objective alignment metho…
AIPO: Learning to Reason from Active Interaction
Junnan Liu, Linhao Luo, Thuy-Trang Vu +1
Recent advances in LLMs have demonstrated strong reasoning capabilities, largely stimulated by RLVR. However, the exploration of existing RLVR algorithms remains largely constraine…
CARD: Towards Conditional Design of Multi-agent Topological Structures
Tongtong Wu, Yanming Li, Ziye Tang +5
Large language model (LLM)-based multi-agent systems have shown strong capabilities in tasks such as code generation and collaborative reasoning. However, the effectiveness and rob…