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Automated Reasoning

Encyclopedia of Big Data Technologies, 2019
Jeff Z. Pan, Jianfeng Du
semanticscholar   +2 more sources

Automated reasoning

Intelligenza Artificiale: The international journal of the AIxIA, 2013
Knowledge representation and automated reasoning are two of the pillars of Artificial Intelligence but, differently from other pillars, they are strictly intertwined. Depending on how knowledge is represented, different types of reasoning can be applied and, on the other hand, new developments in the automated reasoning column fosters new ideas on the ...
GAVANELLI, Marco, Toni Mancini
openaire   +3 more sources

Automated Reasoning

2004
Proceedings of IJCAR 2004.
Basin, David, Rusinowitch, Michaël
openaire   +3 more sources

Automated Reasoning

Lecture Notes in Computer Science, 2018
Stephane Demri, D. Kapur, Christoph Weidenbach   +2 more
semanticscholar   +2 more sources

Seed-Prover: Deep and Broad Reasoning for Automated Theorem Proving

arXiv.org
LLMs have demonstrated strong mathematical reasoning abilities by leveraging reinforcement learning with long chain-of-thought, yet they continue to struggle with theorem proving due to the lack of clear supervision signals when solely using natural ...
Luoxin Chen, Jinming Gu, Liankai Huang, Wenhao Huang, Zhicheng Jiang, Allan Jie, Xiaoran Jin, Xing Jin, Chenggang Li, Kaijing Ma, Cheng Ren, Jiawei Shen, Wenlei Shi, Tong Sun, He Sun, Jiahui Wang, Siran Wang, Zhihong Wang, Chenrui Wei, Shufa Wei, Yong-Xu Wu, Yuchen Wu, Yihang Xia, Hua Xin, Fan Yang, Huaiyuan Ying, Hongyi Yuan, Zheng Yuan, Tianyang Zhan, Chi Zhang, Yue Zhang, Ge-Hang Zhang, Tianyun Zhao, Jianqiu Zhao, Yichi Zhou, Thomas (Hanwen) Zhu   +35 more
semanticscholar   +1 more source

Rewarding Progress: Scaling Automated Process Verifiers for LLM Reasoning

International Conference on Learning Representations
A promising approach for improving reasoning in large language models is to use process reward models (PRMs). PRMs provide feedback at each step of a multi-step reasoning trace, potentially improving credit assignment over outcome reward models (ORMs ...
Amrith Rajagopal Setlur, Chirag Nagpal, Adam Fisch, Xinyang Geng, Jacob Eisenstein, Rishabh Agarwal, Alekh Agarwal, Jonathan Berant, Aviral Kumar   +8 more
semanticscholar   +1 more source

DS-Agent: Automated Data Science by Empowering Large Language Models with Case-Based Reasoning

International Conference on Machine Learning
In this work, we investigate the potential of large language models (LLMs) based agents to automate data science tasks, with the goal of comprehending task requirements, then building and training the best-fit machine learning models.
Siyuan Guo, Cheng Deng, Ying Wen, Hechang Chen, Yi Chang, Jun Wang   +5 more
semanticscholar   +1 more source

Beyond Examples: High-level Automated Reasoning Paradigm in In-Context Learning via MCTS

arXiv.org
In-context learning (ICL) enables large language models (LLMs) to perform downstream tasks through advanced prompting and high-quality demonstrations.
Jinyang Wu, Mingkuan Feng, Shuai Zhang, Feihu Che, Zengqi Wen, Jianhua Tao   +5 more
semanticscholar   +1 more source

Automating Automated Reasoning

2019
The vision of automated support for the investigation of logics, proposed decades ago, has been implemented in many forms, producing numerous tools that analyze various logical properties (e.g., cut-elimination, semantics, and more). However, full ‘automation of automated reasoning’ in the sense of automatic generation of efficient provers has remained
Zohar, Yoni, Tishkovsky, Dmitry, Schmidt, Renate A., Zamansky, Anna   +3 more
openaire   +2 more sources

A Case Study of LLM for Automated Vulnerability Repair: Assessing Impact of Reasoning and Patch Validation Feedback

AIware
Recent work in automated program repair (APR) proposes the use of reasoning and patch validation feedback to reduce the semantic gap between the LLMs and the code under analysis.
Ummay Kulsum, Haotian Zhu, Bowen Xu, Marcelo d’Amorim   +3 more
semanticscholar   +1 more source