Print Join the Discussion View in the ACM Digital Library The mathematical reasoning performed by LLMs is fundamentally different from the rule-based symbolic methods in traditional formal reasoning.

Axiom says its AI found solutions to several long-standing math problems, a sign of the technology’s steadily advancing reasoning capabilities.

Chain-of-Thought (CoT) prompting has enhanced the performance of Large Language Models (LLMs) across various reasoning tasks.

AxiomProver solved a real open math conjecture using formal verification, signaling a shift from AI that assists research to AI that discovers new truths.

Engineers at the University of California San Diego have developed a new way to train artificial intelligence systems to ...

Choose appropriate methods or models for a given problem, using information from observation or knowledge of the system being studied. Employ quantitative methods, mathematical models, statistics, and ...

Top artificial intelligence systems now ace many textbook-style math questions, yet they still fall apart on genuinely new ...

Mathematicians excel at handling complexity and uncertainty. Mathematical reasoning strategies aren't just useful for dilemmas involving numbers. We can apply math mindsets to improve our approach to ...

As a mathematics education researcher, I study how math instruction impacts students' learning, from following standard math procedures to understanding mathematical concepts. Focusing on the latter, ...

A national nonprofit that aims to improve math outcomes for students in pre-K-5 found there are four key elements to ...

Math often feels disconnected from the real lives of students. They learn the steps, solve equations and check their work, ...