1 citations · 1 across the 7 of their papers we have counts for
8 papers
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…
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…
BalanceRAG: Joint Risk Calibration for Cascaded Retrieval-Augmented Generation
Zijun Jia, Yuanchang Ye, Sen Jia +6
Large language models (LLMs) can enhance factuality via retrieval-augmented generation (RAG), but applying RAG to every query is unnecessary when the model-only answer is reliable.…
ChordPrompt: Orchestrating Cross-Modal Prompt Synergy for Multi-Domain Incremental Learning in CLIP
Zhiyuan Wang, Bokui Chen
Continual learning (CL) empowers pre-trained vision-language models to adapt effectively to novel or previously underrepresented data distributions without comprehensive retraining…
ORMind: A Cognitive-Inspired End-to-End Reasoning Framework for Operations Research
Zhiyuan Wang, Bokui Chen, Yinya Huang +4
Operations research (OR) is widely deployed to solve critical decision-making problems with complex objectives and constraints, impacting manufacturing, logistics, finance, and hea…
Task-agnostic Decision Transformer for Multi-type Agent Control with Federated Split Training
Zhiyuan Wang, Bokui Chen, Xiaoyang Qu +3
With the rapid advancements in artificial intelligence, the development of knowledgeable and personalized agents has become increasingly prevalent. However, the inherent variabilit…
INCPrompt: Task-Aware incremental Prompting for Rehearsal-Free Class-incremental Learning
Zhiyuan Wang, Xiaoyang Qu, Jing Xiao +2
This paper introduces INCPrompt, an innovative continual learning solution that effectively addresses catastrophic forgetting. INCPrompt's key innovation lies in its use of adaptiv…
P2DT: Mitigating Forgetting in task-incremental Learning with progressive prompt Decision Transformer
Zhiyuan Wang, Xiaoyang Qu, Jing Xiao +2
Catastrophic forgetting poses a substantial challenge for managing intelligent agents controlled by a large model, causing performance degradation when these agents face new tasks.…