Math Reasoning in LLMs is Organized by Approach, Not Topic
cs.AI
Submitted: 2026-09-22
Updated: 2026-09-24
Terminology
Sources
- Discovering Latent Knowledge in Language Models Without Supervision
- Program of Thoughts Prompting: Disentangling Computation from Reasoning for Numerical Reasoning Tasks
- Training Verifiers to Solve Math Word Problems
- Sparse Autoencoders Find Highly Interpretable Features in Language Models
- Measuring Mathematical Problem Solving With the MATH Dataset
- Measuring Faithfulness in Chain-of-Thought Reasoning
- GSM-Symbolic: Understanding the Limitations of Mathematical Reasoning in Large Language Models
- Show Your Work: Scratchpads for Intermediate Computation with Language Models
- Analysing Mathematical Reasoning Abilities of Neural Models
- DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models
- Deep Inside Convolutional Networks: Visualising Image Classification Models and Saliency Maps
- Base Models Know How to Reason, Thinking Models Learn When
- Interpretability in the Wild: a Circuit for Indirect Object Identification in GPT-2 small
- Self-Consistency Improves Chain of Thought Reasoning in Language Models
- Least-to-Most Prompting Enables Complex Reasoning in Large Language Models
- Representation Engineering: A Top-Down Approach to AI Transparency
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