11 papers
No Time Like the Present: Agentic Test-Time Training for LLM Agents
Yanbo Wang, Jinhua Hao, Yuze Shi +2
LLM agents often degrade over long episodes: as trajectories grow, they revisit explored states, repeat failed actions, and lose strategies that previously worked. Test-time traini…
GILT: An LLM-Free, Tuning-Free Graph Foundational Model for In-Context Learning
Weishuo Ma, Yanbo Wang, Xiyuan Wang +2
Graph Neural Networks (GNNs) are powerful tools for processing relational data but often struggle to generalize to unseen graphs, giving rise to the development of Graph Foundation…
Relational In-Context Learning via Synthetic Pre-training with Structural Prior
Yanbo Wang, Jiaxuan You, Chuan Shi +1
Relational Databases (RDBs) are the backbone of modern business, yet they lack foundation models comparable to those in text or vision. A key obstacle is that high-quality RDBs are…
SAGE: A Self-Evolving Agentic Graph-Memory Engine for Structure-Aware Associative Memory
Juntong Wang, Haoyue Zhao, guanghui Pan +4
Long-term memory is becoming a central bottleneck for language agents. Exsting RAG and GraphRAG systems largely treat memory graphs as static retrieval middleware, which limits the…
TabDLM: Free-Form Tabular Data Generation via Joint Numerical-Language Diffusion
Donghong Cai, Jiarui Feng, Yanbo Wang +3
Synthetic tabular data generation has attracted growing attention due to its importance for data augmentation, foundation models, and privacy. However, real-world tabular datasets…
Breaking the Impasse: Dual-Scale Evolutionary Policy Training for Social Language Agents
Minzheng Wang, Run Luo, Yanbo Wang +6
While Reinforcement Learning with Verifiable Rewards (RLVR) has proven effective for closed-ended tasks, extending it to open-ended social language games via self-play reveals a cr…