3 accepted papers
Chain-of-Thought (CoT) prompting has shown promise in enhancing the reasoning capabilities of large language models (LLMs) on text-attributed graphs (TAGs). This work reframes CoT-based graph learning through the principle of clustering as reasoning, offering a $k$-means interpretation of how iterat…
Dual-arm manipulation of fabrics is important and complex in the field of embodied intelligence for robots. Previous work has mostly focused on extracting the geometric features of fabrics, but has neglected the knowledge and experience constraints on grasp postures like humans, which greatly affect
Most existing graph clustering methods primarily focus on exploiting topological structure, often neglecting the "missing-half" node feature information, especially how these features can enhance clustering performance. This issue is further compounded by the challenges associated with high-dimensio…