3 papers
cs.LG2026
Graph Optimization Foundation Model: Tokenizing Graph via A Language-Model Paradigm
Yunhao Liang, Pujun Zhang, Yuan Qu +3
The pretrain-transfer paradigm, which underpins the success of large language models (LLMs), has demonstrated the immense power of creating foundation models that learn generalizab…
cs.AI2026
From Business Events to Auditable Decisions: Ontology-Governed Graph Simulation for Enterprise AI
Hongyin Zhu, Jinming Liang, Mengjun Hou +5
Existing LLM-based agent systems share a common architectural failure: they answer from the unrestricted knowledge space without first simulating how active business scenarios resh…
cs.AI2025
Everyone Contributes! Incentivizing Strategic Cooperation in Multi-LLM Systems via Sequential Public Goods Games
Yunhao Liang, Yuan Qu, Jingyuan Yang +2
Coordinating multiple large language models (LLMs) to solve complex tasks collaboratively poses a fundamental trade-off between the computation costs and collective performance com…