activity
20242026
most citedEvolutionary Reinforcement Learning: A Systematic Review and Future Directions

1 citations · 1 across the 4 of their papers we have counts for

collaborators

6 papers

cs.CV2026

X-Ragent: Tool-Augmented Reinforcement Learning for Evidence-Grounded Chest X-Ray Reasoning

Yi Wang, Zhenlong Yuan, Yuchan Guo +9

Chest X-ray (CXR) interpretation often requires integrating visual findings with task-specific evidence rather than relying on direct visual recognition alone. While tool-augmented…

cs.CV2026

VepAgent: Bridging Causal-Transition via Tool-Augmented Reinforcement Learning for Video Event Prediction

Qiutong Chen, Yuchan Guo, Zhenlong Yuan +10

Multimodal Large Language Models (MLLMs) have demonstrated remarkable potential in video understanding, yet their reliance on retrospective summarization and text-centric priors of…

cs.CV2026

IndusAgent: Agentic Reinforcement Learning for Cross-Category Industrial Anomaly Detection

Rongbin Tan*, Rongbin Tan, Fangfang Lin* +16

Small defects in unfamiliar product categories can be difficult to distinguish from benign appearance variations in a global view. Additional reasoning over the same view may leave…

cs.LG2026

Federated Nested Learning: Collaborative Training of Self-Referential Memories for Test-Time Adaptation

Hong Chen, Pengcheng Wu, Yuanguo Lin +4

We rethink Federated Learning (FL) from a nested learning perspective, framing the core challenge as how to collaboratively learn optimization rules, not just static models, to tac…

cs.CR2025

Data Security in Large Language Models: Risks, Defense, and Directions

Kang Chen, Xiuze Zhou, Yuanguo Lin +6

Large Language Models (LLMs), now a foundation in advancing natural language processing, power applications such as text generation, machine translation, and conversational systems…

cs.NE2024★ 1 cited

Evolutionary Reinforcement Learning: A Systematic Review and Future Directions

Yuanguo Lin, Fan Lin, Guorong Cai +3

In response to the limitations of reinforcement learning and evolutionary algorithms (EAs) in complex problem-solving, Evolutionary Reinforcement Learning (EvoRL) has emerged as a…