182 citations · 574 across the 34 of their papers we have counts for
9 papers · 1 filter
Doing More with Less Tokens: Hierarchical Reinforcement Learning for Efficient Coding Agents
Haobin Li, Liang Jiang, Zhenyu Huang +2
Recently, coding agents have emerged as a dominant paradigm for real-world software engineering (SWE) scenarios, which solve complex tasks through multi-turn interactions with deve…
NeuCME: Toward Dynamic Multimodal Continual Learning via Neural Combinatorics of Multiple Experts
Kai Guo, Chuanbin Liu, Peng Hu +2
Multimodal continual learning has recently shown great potential for developing agents with human-like intelligence by continuously learning new tasks across multiple modalities. H…
Imagine Before Acting: Prospective Skill Identification for LLM Agents
Shuo Liu, Yutong Yang, Haohao Xiao +2
Effective skill identification is crucial for LLM agents to reuse task-solving experience for complex task execution, which however remains challenging as agents must accurately id…
RA-CAD: Learning Post-Execution Critique for State-Aware Text-to-CAD Generation
Shuhao Yan, Changhao He, Peng Hu +1
Text-to-CAD generation translates natural-language design intent into editable and executable parametric computer-aided design (CAD) codes, reducing the expertise and effort requir…
Active-SWE: Benchmarking Coding Agents for Proactive Bug Fixing without Issue Reports
Haobin Li, Ping Deng, Weizhong Qian +4
Coding agents powered by large language models (LLMs) are increasingly adopted in software engineering (SWE) scenarios, capable of fixing a specific bug in large-scale codebase. Ho…
Robust Multi-view Clustering against Imperfect Information
Zhichao Huang, Haochen Zhou, Hao Wang +2
Real-world multi-view data always suffer from imperfect information problem, where the view-specific observations are absent (i.e., Incomplete Views, IV) and cross-view corresponde…
Robust Fuzzy Multi-view Learning under View Conflict
Siyuan Duan, Yuan Sun, Dezhong Peng +3
Trusted multi-view classification aims to deliver reliable fusion for accurate predictions and has recently attracted substantial attention in both academia and industry. However,…
Learning Subspace-Preserving Sparse Attention Graphs from Heterogeneous Multiview Data
Jie Chen, Yuanbiao Gou, Chuanbin Liu +2
The high-dimensional features extracted from large-scale unlabeled data via various pretrained models with diverse architectures are referred to as heterogeneous multiview data. Mo…
Attention Transfer Is Not Universally Effective for Vision Transformers
Huaiyuan Qin, Muli Yang, Gabriel James Goenawan +4
A recent work shows that Attention Transfer, which transfers only the attention patterns from a pre-trained teacher Vision Transformer (ViT) to a randomly initialized standard stud…