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Semantic conflicts in LLMs can dramatically reduce execution correctness, revealing that misleading cues often dominate model outputs.
Students skeptical of AI hype can be taught to think empirically by grounding abstract concepts in hands-on experience with AI coding assistants.
GPT-3.5's iterative code refactoring reveals a surprising "restructuring then stabilization" pattern, hinting at an inherent sense of optimal code readability.
