AI RESEARCH
The Path Not Taken: Duality in Reasoning about Program Execution
arXiv CS.LG
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ArXi:2604.20917v1 Announce Type: new Large language models (LLMs) have shown remarkable capabilities across diverse coding tasks. However, their adoption requires a true understanding of program execution rather than relying on surface-level patterns. Existing benchmarks primarily focus on predicting program properties tied to specific inputs (e.g., code coverage, program outputs). As a result, they provide a narrow view of dynamic code reasoning and are prone to data contamination.