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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.

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

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

The method has two main features: it evaluates how AI models reason through problems instead of just checking whether their ...

The race is on to develop an artificial intelligence that can do pure mathematics, and top mathematicians just threw down the gauntlet with an exam of actual, unsolved problems that are relevant to ...

Frustrated by the AI industry’s claims of proving math results without offering transparency, a team of leading academics has ...

Do you stare at a math word problem and feel completely stuck? You're not alone. These problems mix reading comprehension ...

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

These low-floor, high-ceiling problems support differentiation, challenging all students by encouraging flexible thinking and allowing for multiple solution paths.

Four simple strategies—beginning with an image, previewing vocabulary, omitting the numbers, and offering number sets—can have a big impact on learning.