A Universal Avoidance Method for Diverse Multi-branch Generation
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arXiv
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| Main Authors: | , , |
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| Format: | Preprint |
| Published: |
2026
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| _version_ | 1866918454655713280 |
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| author | Park, Kyeongman Jhang, Minha Jung, Kyomin |
| author_facet | Park, Kyeongman Jhang, Minha Jung, Kyomin |
| contents | 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/. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2604_17323 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | A Universal Avoidance Method for Diverse Multi-branch Generation Park, Kyeongman Jhang, Minha Jung, Kyomin Computation and Language Machine Learning 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/. |
| title | A Universal Avoidance Method for Diverse Multi-branch Generation |
| topic | Computation and Language Machine Learning |
| url | https://arxiv.org/abs/2604.17323 |