Logic & Formal Reasoning
"I think the best hope for human-level AI is logical AI, based on the formalizing of commonsense knowledge and reasoning in mathematical logic. Formalizing common sense requires extensions to mathematical logic including nonmonotonic reasoning and extensive reification, e.g., of concepts and also contexts. The reifications require appropriate reflection schemas."
– from The Future of AI—A Manifesto by John McCarthy. AI Magazine 26(4), (2005).
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Enhancing Reasoning Capabilities of LLMs via Principled Synthetic Logic Corpus
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Training (AL T), which aims to enhance LLMs' reasoning capabilities by program-generated logical reasoning samples.
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Abductive Reasoning in Logical Credal Networks
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Logical Credal Networks or LCNs were recently introduced as a powerful probabilistic logic framework for representing and reasoning with imprecise knowledge.
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Autoformalizing Mathematical Statements by Symbolic Equivalence and Semantic Consistency Zenan Li1 Yifan Wu2 Zhaoyu Li3 Xinming Wei 2
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This work was partially done during Zenan's and Yifan's internships at MSRA. 38th Conference on Neural Information Processing Systems (NeurIPS 2024).
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Learning Better Representations From Less Data For Propositional Satisfiability
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Boolean satisfiability (SA T) is a fundamental problem in computer science.
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