RAPO++ is a three-stage prompt optimization framework combining retrieval-augmented refinement, closed-loop test-time scaling, and LLM fine-tuning to enhance text-to-video generation quality.
The family that plays together stays together
2 Pith papers cite this work. Polarity classification is still indexing.
2
Pith papers citing it
verdicts
UNVERDICTED 2representative citing papers
The paper introduces a classifier-guided multi-objective evolutionary algorithm for prompt evolution in generative AI that uses the model's stochastic generation as implicit mutations to create Pareto-optimized images aligned with user preferences.
citing papers explorer
-
RAPO++: Cross-Stage Prompt Optimization for Text-to-Video Generation via Data Alignment and Test-Time Scaling
RAPO++ is a three-stage prompt optimization framework combining retrieval-augmented refinement, closed-loop test-time scaling, and LLM fine-tuning to enhance text-to-video generation quality.
-
Prompt Evolution for Generative AI: A Classifier-Guided Approach
The paper introduces a classifier-guided multi-objective evolutionary algorithm for prompt evolution in generative AI that uses the model's stochastic generation as implicit mutations to create Pareto-optimized images aligned with user preferences.