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LLMs predict test outputs far more reliably when they can vote between running buggy code and "reasoning" over pseudocode.
LLMs can achieve significantly higher accuracy and verifiable reasoning paths by explicitly grounding each step in a knowledge graph and dynamically correcting errors during inference.
