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
On the Self-Verification Limitations of Large Language Models on Reasoning and Planning Tasks
Kaya Stechly, Karthik Valmeekam, Subbarao Kambhampati
Beyond LLMs: Advancing the Landscape of Complex Reasoning
Jennifer Chu-Carroll, Andrew Beck, Greg Burnham, David OS Melville, David Nachman, A. Erdem Özcan, David Ferrucci
Diffusion of Thoughts: Chain-of-Thought Reasoning in Diffusion Language Models
Jiacheng Ye, Shansan Gong, Liheng Chen, Lin Zheng, Jiahui Gao, Han Shi, Chuan Wu, Zhenguo Li, Wei Bi, Lingpeng Kong
Training Large Language Models for Reasoning through Reverse Curriculum Reinforcement Learning
Zhiheng Xi, Wenxiang Chen, Boyang Hong, Senjie Jin, Rui Zheng, Wei He, Yiwen Ding, Shichun Liu, Xin Guo, Junzhe Wang, Honglin Guo, Wei Shen, Xiaoran Fan, Yuhao Zhou, Shihan Dou, Xiao Wang, Xinbo Zhang, Peng Sun, Tao Gui, Qi Zhang, Xuanjing Huang
Zero-Shot Chain-of-Thought Reasoning Guided by Evolutionary Algorithms in Large Language Models
Feihu Jin, Yifan Liu, Ying Tan
Are Machines Better at Complex Reasoning? Unveiling Human-Machine Inference Gaps in Entailment Verification
Soumya Sanyal, Tianyi Xiao, Jiacheng Liu, Wenya Wang, Xiang Ren
Large Language Models as an Indirect Reasoner: Contrapositive and Contradiction for Automated Reasoning
Yanfang Zhang, Yiliu Sun, Yibing Zhan, Dapeng Tao, Dacheng Tao, Chen Gong
Neural networks for abstraction and reasoning: Towards broad generalization in machines
Mikel Bober-Irizar, Soumya Banerjee
Understanding Reasoning Ability of Language Models From the Perspective of Reasoning Paths Aggregation
Xinyi Wang, Alfonso Amayuelas, Kexun Zhang, Liangming Pan, Wenhu Chen, William Yang Wang
The Role of Foundation Models in Neuro-Symbolic Learning and Reasoning
Daniel Cunnington, Mark Law, Jorge Lobo, Alessandra Russo
Foundation Model Sherpas: Guiding Foundation Models through Knowledge and Reasoning
Debarun Bhattacharjya, Junkyu Lee, Don Joven Agravante, Balaji Ganesan, Radu Marinescu
Reasoning Capacity in Multi-Agent Systems: Limitations, Challenges and Human-Centered Solutions
Pouya Pezeshkpour, Eser Kandogan, Nikita Bhutani, Sajjadur Rahman, Tom Mitchell, Estevam Hruschka
PathMMU: A Massive Multimodal Expert-Level Benchmark for Understanding and Reasoning in Pathology
Yuxuan Sun, Hao Wu, Chenglu Zhu, Sunyi Zheng, Qizi Chen, Kai Zhang, Yunlong Zhang, Dan Wan, Xiaoxiao Lan, Mengyue Zheng, Jingxiong Li, Xinheng Lyu, Tao Lin, Lin Yang
The Reasoning Under Uncertainty Trap: A Structural AI Risk
Toby D. Pilditch