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This paper introduces STRIVE, an LLM-based framework for jointly generating and evaluating controlled event sets for psycholinguistic plausibility judgments. Experiments show that adding a global reasoning scratchpad and evaluator-guided refinement substantially improves generation quality, though near-boundary events remain challenging.