提出超轻量级提示调优方法，高效提升视频生成模型性能，无需繁重计算资源。
arXiv:2607.14595v1 Announce Type: new Abstract: Large-scale video diffusion models (VDMs) deliver strong generation performance, but full fine-tuning …
用对抗提示调优与生成破解单域泛化难题，CVPR 2025新思路不容错过。
arXiv:2606.21736v1 Announce Type: new Abstract: Single domain generalization (SDG) aims to learn a robust model, which could perform well on many unse…
提出GRID框架，实现连续提示调优中任务无关推理的规模化，解决增量学习场景下的模型适应难题，理论创新与实验验证兼具。
arXiv:2507.14725v4 Announce Type: replace-cross Abstract: Prompt-based continual learning (CL) offers a parameter-efficient way to adapt large languag…
通过函数调用实现反思性提示调优，让大模型动态优化自身提示，提升对话质量与适应性。
arXiv:2605.21781v1 Announce Type: new Abstract: Large language models (LLMs) have become increasingly capable of following instructions and complex re…