Complex Reasoning
Complex reasoning in artificial intelligence focuses on developing models capable of multi-step, logical inference and problem-solving, mirroring human cognitive abilities. Current research emphasizes improving large language models (LLMs) through techniques like chain-of-thought prompting, retrieval-augmented generation (RAG), and the integration of symbolic reasoning with neural networks, often incorporating multi-modal data (e.g., visual and textual information). These advancements are significant for enhancing the reliability and applicability of AI systems across diverse fields, including autonomous driving, robotics, and scientific discovery, by enabling more robust and accurate decision-making in complex scenarios.
Papers
Reasoning3D -- Grounding and Reasoning in 3D: Fine-Grained Zero-Shot Open-Vocabulary 3D Reasoning Part Segmentation via Large Vision-Language Models
Tianrun Chen, Chunan Yu, Jing Li, Jianqi Zhang, Lanyun Zhu, Deyi Ji, Yong Zhang, Ying Zang, Zejian Li, Lingyun Sun
Learning from Litigation: Graphs and LLMs for Retrieval and Reasoning in eDiscovery
Sounak Lahiri, Sumit Pai, Tim Weninger, Sanmitra Bhattacharya