High Level
High-level reasoning in artificial intelligence focuses on enabling systems to understand and act upon complex, abstract instructions and concepts, rather than solely reacting to low-level inputs. Current research emphasizes hierarchical approaches, often incorporating large language models (LLMs) for high-level planning and reasoning, coupled with lower-level controllers or specialized algorithms for execution and feedback. This work is significant for improving the robustness, explainability, and generalizability of AI systems across diverse applications, from robotics and autonomous driving to user interfaces and data analysis.
Papers
Knowledge Transfer Across Modalities with Natural Language Supervision
Carlo Alberto Barbano, Luca Molinaro, Emanuele Aiello, Marco Grangetto
Traditional Chinese Medicine Case Analysis System for High-Level Semantic Abstraction: Optimized with Prompt and RAG
Peng Xu, Hongjin Wu, Jinle Wang, Rongjia Lin, Liwei Tan
TACT: Advancing Complex Aggregative Reasoning with Information Extraction Tools
Avi Caciularu, Alon Jacovi, Eyal Ben-David, Sasha Goldshtein, Tal Schuster, Jonathan Herzig, Gal Elidan, Amir Globerson
AD-H: Autonomous Driving with Hierarchical Agents
Zaibin Zhang, Shiyu Tang, Yuanhang Zhang, Talas Fu, Yifan Wang, Yang Liu, Dong Wang, Jing Shao, Lijun Wang, Huchuan Lu