SEP partitions LLM code candidates into functional equivalence classes using symbolic execution on public examples and selects the dominant class, improving accuracy from 0.754 to 0.826 on HumanEval+ and 0.565 to 0.647 on LiveCodeBench at N=10.
Test Case Generation.CodeT generates synthetic test cases by prompting the same language model used for solution generation
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Inference-Time Code Selection via Symbolic Equivalence Partitioning
SEP partitions LLM code candidates into functional equivalence classes using symbolic execution on public examples and selects the dominant class, improving accuracy from 0.754 to 0.826 on HumanEval+ and 0.565 to 0.647 on LiveCodeBench at N=10.