Error as a Lens: Probing LLM Reasoning through Synthetic Misconception Generation

In three linesFramework to generate targeted synthetic errors with LLMs aligned to cognitive taxonomy (revised Bloom's). A Generation Agent drafts erroneous solutions, an Examination Agent validates consistency with specified error mode. Tested on TheoremQA, shows generating authentic errors is substantially harder than producing arbitrary wrong answers.

Summary generated by Claude — human-verified