1 citations · 1 across the 12 of their papers we have counts for
12 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…
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…
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…
Position: How can Graphs Help Large Language Models?
Xiyuan Wang, Yi Hu, Yanbo Wang +2
With the rapid advancement of large language models (LLMs), classic graph learning tasks have greatly benefited from LLMs, including improved encoding of textual features, more eff…
KAT-Coder-V2 Technical Report
Fengxiang Li, Han Zhang, Haoyang Huang +43
We present KAT-Coder-V2, an agentic coding model developed by the KwaiKAT team at Kuaishou. KAT-Coder-V2 adopts a "Specialize-then-Unify" paradigm that decomposes agentic coding in…
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…
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…
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…
Griffin: Towards a Graph-Centric Relational Database Foundation Model
Yanbo Wang, Xiyuan Wang, Quan Gan +4
We introduce Griffin, the first foundation model attemptation designed specifically for Relational Databases (RDBs). Unlike previous smaller models focused on single RDB tasks, Gri…
Reconsidering the Performance of GAE in Link Prediction
Weishuo Ma, Yanbo Wang, Xiyuan Wang +1
Recent advancements in graph neural networks (GNNs) for link prediction have introduced sophisticated training techniques and model architectures. However, reliance on outdated bas…
Efficient Neural Common Neighbor for Temporal Graph Link Prediction
Xiaohui Zhang, Yanbo Wang, Xiyuan Wang +1
Temporal graphs are widespread in real-world applications such as social networks, as well as trade and transportation networks. Predicting dynamic links within these evolving grap…
4DBInfer: A 4D Benchmarking Toolbox for Graph-Centric Predictive Modeling on Relational DBs
Minjie Wang, Quan Gan, David Wipf +17
Although RDBs store vast amounts of rich, informative data spread across interconnected tables, the progress of predictive machine learning models as applied to such tasks arguably…