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A Survey of Deep Learning for Mathematical Reasoning

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arxiv 2212.10535 v2 pith:SSOE3XGN submitted 2022-12-20 cs.AI cs.CLcs.CVcs.LG

classification cs.AIcs.CLcs.CVcs.LG
keywords learningreasoningdeepmathematicaladvancesbenchmarksfieldsintelligence
verification ladder T0 review T1 audit T2 compute T3 formal
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Mathematical reasoning is a fundamental aspect of human intelligence and is applicable in various fields, including science, engineering, finance, and everyday life. The development of artificial intelligence (AI) systems capable of solving math problems and proving theorems has garnered significant interest in the fields of machine learning and natural language processing. For example, mathematics serves as a testbed for aspects of reasoning that are challenging for powerful deep learning models, driving new algorithmic and modeling advances. On the other hand, recent advances in large-scale neural language models have opened up new benchmarks and opportunities to use deep learning for mathematical reasoning. In this survey paper, we review the key tasks, datasets, and methods at the intersection of mathematical reasoning and deep learning over the past decade. We also evaluate existing benchmarks and methods, and discuss future research directions in this domain.

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Cited by 3 Pith papers

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    Augmenting VR speech transcripts with gaze and pointing cues improved GPT-4 coreference resolution from 40.6% to 67.1% accuracy.

  2. Arrows of Math Reasoning Data Synthesis for Large Language Models: Diversity, Complexity and Correctness

    cs.CL 2025-08 reject novelty 6.0 of 10

    A program-assisted pipeline generates 12.3 million math problem-solution pairs with execution-based verification, and fine-tuning on a 50k sample improves model scores on GSM8K, MATH, Minerva, and SVAMP.

  3. STORM-BORN: A Challenging Mathematical Derivations Dataset Curated via a Human-in-the-Loop Multi-Agent Framework

    cs.CL 2025-06 conditional novelty 6.0 of 10

    A 100-problem benchmark of difficult math derivations from academic papers, where the best LLMs score below 5% and small-scale fine-tuning yields modest gains on standard math benchmarks.

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