A Universal Avoidance Method for Diverse Multi-branch Generation

arXiv cs.CL / 4/21/2026

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Key Points

The paper introduces UAG (Universal Avoidance Generation), a model-agnostic method designed to improve multi-branch diversity in generative outputs, where many modern models still fall short of human-level creativity.
UAG works by penalizing similarity between newly generated results and previously produced outputs, helping diversify multiple branches without relying heavily on specific model architecture.
The method is computationally efficient and adds minimal extra overhead while being applicable to both diffusion models and transformer-based generative models.
Experimental results report up to 1.9× higher diversity, 4.4× faster runtime, and only 1/64 of the FLOPs versus state-of-the-art alternatives.
The authors provide full code via the linked repository, enabling reproducibility and further experimentation.

Abstract

Modern generative models still lack human-level creativity, particularly in multi-branch diversity. Prior approaches to address this problem often incur heavy computation or strong dependency on model architecture. Therefore, we introduce UAG(Universal Avoidance Generation), a model-agnostic and computationally efficient generation strategy that penalizes similarity among previously generated outputs. Thus, UAG can enhance multi-branch diversity across both diffusion and transformer models, with minimal additional computation. In experiments, our method achieves up to 1.9 times higher diversity, runs 4.4 times faster, and requires only 1/64 of the FLOPs compared to state-of-the-art methods. The full code is https://anonymous.4open.science/r/2026_ACL_Universal/.