6 papers
Bridging Body and Brain: Gene-Driven Morphology--Control Co-Design
Fu Feng, Ruixiao Shi, Yucheng Xie +2
Morphology--control co-design jointly optimizes an agent's body structure and control policy as an integrated embodied system. However, existing methods typically model morphology…
SafeGene: Reusable Adapters for Transferable Safety Alignment
Yanghan Wang, Zhiqiang Kou, Fu Feng +2
Open-weight LLMs are increasingly fine-tuned into customized assistants, but downstream fine-tuning can weaken safety alignment and make models more vulnerable to malicious prompts…
Constraint-based Pre-training: From Structured Constraints to Scalable Model Initialization
Fu Feng, Yucheng Xie, Ruixiao Shi +2
The pre-training and fine-tuning paradigm has become the dominant approach for model adaptation. However, conventional pre-training typically yields models at a fixed scale, wherea…
A Creative Agent is Worth a 64-Token Template
Ruixiao Shi, Fu Feng, Yucheng Xie +3
Text-to-image (T2I) models have substantially improved image fidelity and prompt adherence, yet their creativity remains constrained by reliance on discrete natural language prompt…
Self-Supervised Weight Templates for Scalable Vision Model Initialization
Yucheng Xie, Fu Feng, Ruixiao Shi +3
The increasing scale and complexity of modern model parameters underscore the importance of pre-trained models. However, deployment often demands architectures of varying sizes, ex…
Towards Understanding Feature Learning in Parameter Transfer
Hua Yuan, Xuran Meng, Qiufeng Wang +6
Parameter transfer is a central paradigm in transfer learning, enabling knowledge reuse across tasks and domains by sharing model parameters between upstream and downstream models.…