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
Uncovering Latent Chain of Thought Vectors in Language Models
Jason Zhang, Scott Viteri
StateAct: State Tracking and Reasoning for Acting and Planning with Large Language Models
Nikolai Rozanov, Marek Rei
BrainDreamer: Reasoning-Coherent and Controllable Image Generation from EEG Brain Signals via Language Guidance
Ling Wang, Chen Wu, Lin Wang
Benchmarking VLMs' Reasoning About Persuasive Atypical Images
Sina Malakouti, Aysan Aghazadeh, Ashmit Khandelwal, Adriana Kovashka
NEUSIS: A Compositional Neuro-Symbolic Framework for Autonomous Perception, Reasoning, and Planning in Complex UAV Search Missions
Zhixi Cai, Cristian Rojas Cardenas, Kevin Leo, Chenyuan Zhang, Kal Backman, Hanbing Li, Boying Li, Mahsa Ghorbanali, Stavya Datta, Lizhen Qu, Julian Gutierrez Santiago, Alexey Ignatiev, Yuan-Fang Li, Mor Vered, Peter J Stuckey, Maria Garcia de la Banda, Hamid Rezatofighi
Knowing When to Ask -- Bridging Large Language Models and Data
Prashanth Radhakrishnan, Jennifer Chen, Bo Xu, Prem Ramaswami, Hannah Pho, Adriana Olmos, James Manyika, R. V. Guha
E2LLM: Encoder Elongated Large Language Models for Long-Context Understanding and Reasoning
Zihan Liao, Jun Wang, Hang Yu, Lingxiao Wei, Jianguo Li, Jun Wang, Wei Zhang