ISM:Self-Improving Strategy Memory for Continual Mathematical Reasoning
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Computer Science > Machine Learning
Title:ISM:Self-Improving Strategy Memory for Continual Mathematical Reasoning
Abstract:We propose Intelligent Schema Memory (ISM), a self-evolving memory-augmented system that improves mathematical reasoning for a frozen LLM under continual learning with hard episodic resets. ISM maintains a compact, self-refined bank of strategy schemas learned from both successful and failed episodes, with symbolic tools that check intermediate steps and certify this http URL updating model parameters, ISM outperforms passive, retrieval, and reflection baselines on MATH-Hard and OlympiadBench, using 64% and 86% fewer schemas respectively than the strongest passive baseline. These results show that small, actively maintained, and verified strategy memories can support reliable continual mathematical reasoning under strict episodic isolation.
| Comments: | 3rd AI for Math Workshop at ICML 2026 Forty-Third International Conference on Machine Learning |
| Subjects: | Machine Learning (cs.LG) |
| Cite as: | arXiv:2606.31191 [cs.LG] |
| (or arXiv:2606.31191v1 [cs.LG] for this version) | |
| https://doi.org/10.48550/arXiv.2606.31191
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