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Hallucinated citations and logical inconsistencies in AI-generated scientific introductions can be significantly reduced by explicitly modeling and optimizing a logic-reasoning graph.
By iteratively challenging LLMs with adaptively generated test cases, this low-code platform co-optimizes code and tests, bypassing the need for manual prompt engineering and boosting reliability in AI-for-Science workflows.
