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20232026
most citedExploring the Potential of Large Language Models (LLMs) in Learning on Graphs

20 citations · 57 across the 22 of their papers we have counts for

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7 papers · 1 filter

cs.AI2026

MemTrace: Probing What Final Accuracy Misses in Long-Term Memory

Xianxuan Long, Zhikai Chen, Shenglai Zeng +3

LLM agents increasingly maintain long-term memory of user facts across sessions. Yet such memory is usually evaluated by aggregating accuracy over question rows or episodes. Becaus…

cs.LG2026

OpenRFM: Dissecting Relational In-Context Learning

Zhikai Chen, Junyu Yin, Jialiang Gu +5

Relational Foundation Models (RFMs) promise a single pre-trained predictor that, given any relational database, returns predictions in one forward pass via relational in-context le…

cs.AI2026

Exploring Cross-Scenario Generality of Agentic Memory Systems: Diagnostics and a Strong Baseline

Zhikai Chen, Jialiang Gu, Junyu Yin +6

LLM agents accumulate histories that outgrow their context windows, motivating a growing literature on memory systems. Yet most existing designs are tuned to a single scenario (mul…

cs.CL2026

Do Proactive Agents Need an LLM to Decide When to Act?

Xiaoze Liu, Ruowang Zhang, Amir H. Abdi +5

Proactive assistants continuously decide when to intervene and what context should support the intervention. Large language model (LLM) pipelines repeatedly interpret activity hist…

cs.LG2026

Relatron: Automating Relational Machine Learning over Relational Databases

Zhikai Chen, Han Xie, Jian Zhang +3

Predictive modeling over relational databases (RDBs) powers applications, yet remains challenging due to capturing both cross-table dependencies and complex feature interactions. R…

cs.AI2026

AgentDoG: A Diagnostic Guardrail Framework for AI Agent Safety and Security

Dongrui Liu, Qihan Ren, Chen Qian +40

The rise of AI agents introduces complex safety and security challenges arising from autonomous tool use and environmental interactions. Current guardrail models lack agentic risk…

cs.CL2026

YuFeng-XGuard: A Reasoning-Centric, Interpretable, and Flexible Guardrail Model for Large Language Models

Junyu Lin, Meizhen Liu, Xiufeng Huang +12

As large language models (LLMs) are increasingly deployed in real-world applications, safety guardrails are required to go beyond coarse-grained filtering and support fine-grained,…