3 papers
cs.AI2026
SL-CBM: Enhancing Concept Bottleneck Models with Semantic Locality for Better Interpretability
Hanwei Zhang, Luo Cheng, Rui Wen +3
Explainable AI (XAI) is crucial for building transparent and trustworthy machine learning systems, especially in high-stakes domains. Concept Bottleneck Models (CBMs) have emerged…
cs.RO2026
FocusNav: Spatial Selective Attention with Waypoint Guidance for Humanoid Local Navigation
Yang Zhang, Jianming Ma, Liyun Yan +4
Robust local navigation in unstructured and dynamic environments remains a significant challenge for humanoid robots, requiring a delicate balance between long-range navigation tar…
cs.CL2026
Don't Start Over: A Cost-Effective Framework for Migrating Personalized Prompts Between LLMs
Ziyi Zhao, Chongming Gao, Yang Zhang +5
Personalization in Large Language Models (LLMs) often relies on user-specific soft prompts. However, these prompts become obsolete when the foundation model is upgraded, necessitat…