4 papers
GUI-C: Coarse-to-Fine GUI Grounding via Difficulty-Aware Reinforcement Learning
Junlong Li, Chao Hao, Lap-Pui Chau +1
Existing agentic reinforcement learning methods for GUI grounding have limitations at two levels. At the data level, current approaches typically treat all training samples equally…
Seg-Agent: Test-Time Multimodal Reasoning for Training-Free Language-Guided Segmentation
Chao Hao, Jun Xu, Ji Du +6
Language-guided segmentation transcends the scope limitations of traditional semantic segmentation, enabling models to segment arbitrary target regions based on natural language in…
Dynamic Collaboration of Multi-Language Models based on Minimal Complete Semantic Units
Chao Hao, Zezheng Wang, Yanhua Huang +4
This paper investigates the enhancement of reasoning capabilities in language models through token-level multi-model collaboration. Our approach selects the optimal tokens from the…
LangTime: A Language-Guided Unified Model for Time Series Forecasting with Proximal Policy Optimization
Wenzhe Niu, Zongxia Xie, Yanru Sun +3
Recent research has shown an increasing interest in utilizing pre-trained large language models (LLMs) for a variety of time series applications. However, there are three main chal…