ChemAgent: Self-updating Library in Large Language Models Improves Chemical Reasoning
- Published
- Source
- arXiv
- Paper number
- 021
- Field
- Scientific AI / Chemistry
- arXiv ID
- 2501.06590
Key points
- LLMs struggle with complex chemical reasoning tasks that require precise computation.
- Existing models lack the ability to learn from and reuse past problem-solving experience.
- Current approaches rely heavily on human-curated knowledge or fixed workflows.
- Even small mistakes can cascade into failure in chemical reasoning.
- We developed ChemAgent, a framework with a dynamic, self-updating library system.
- It implements three types of memory: Planning Memory, Execution Memory, and Knowledge Memory.
Paper links
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