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CogMath: Assessing LLMs’ Authentic Mathematical Ability from a Human Cognitive Perspective
Published in Proceedings of the 42nd International Conference on Machine Learning (ICML 2025), 2025
A cognitively grounded framework for evaluating the authentic mathematical reasoning abilities of large language models.
Recommended citation: Liu, J., Huang, Z., Dai, W., Cheng, C., Wu, J., Sha, J., Li, S., Liu, Q., Wang, S., & Chen, E. (2025). "CogMath: Assessing LLMs’ Authentic Mathematical Ability from a Human Cognitive Perspective." Proceedings of the 42nd International Conference on Machine Learning, 38692–38707.
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Verifying Large Language Models’ Reasoning Paths via Correlation Matrix Rank
Published in arXiv preprint arXiv:2510.24299, 2025
A lightweight, self-contained method for estimating the correctness of large language model reasoning paths from internal correlation structure.
Recommended citation: Liu, J., Dai, W., Huang, Z., Miao, N., & Chen, E. (2025). "Verifying Large Language Models’ Reasoning Paths via Correlation Matrix Rank." arXiv preprint arXiv:2510.24299.
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