用缩放敏感损失景观让模型无法被随意合并，保护版权与安全。
Making Models Unmergeable via Scaling-Sensitive Loss Landscape
- 通过权重重缩放机制，在微调时嵌入不可逆保护
- 释放的模型独立使用效果不变，合并时性能显著下降
- 适用于各类架构，对适配器和完整模型都有效
模型枢纽的兴起使模型组件可复用，模型合并成为组合能力的实用工具。但这种模块化也带来治理空白：下游用户可将发布的权重重组为未经许可的混合模型，绕过安全对齐或许可证限制。现有防御多为事后补救且依赖架构，实际中对不同架构和发布格式保护不一致。为此，我们提出Trap$^2$，一种架构无关的保护框架，将保护编码进微调过程中的更新，无论以适配器还是完整模型形式发布。不依赖架构特定方法，Trap$^2$以权重重缩放作为合并过程的简单代理。它保持发布权重在独立使用时的有效性，但在合并中常见的重缩放下性能退化，从而破坏未经授权的重组。
原文摘要 · Abstract (English)
The rise of model hubs has made it easier to access reusable model components, making model merging a practical tool for combining capabilities. Yet, this modularity also creates a governance gap: downstream users can recompose released weights into unauthorized mixtures that bypass safety alignment or licensing terms. Because existing defenses are largely post-hoc and architecture-specific, they provide inconsistent protection across diverse architectures and release formats in practice. To close this gap, we propose Trap$^2$, an architecture-agnostic protection framework that encodes protection into updates during fine-tuning, regardless of whether they are released as adapters or full models. Instead of relying on architecture-dependent approaches, Trap$^2$ uses weight re-scaling as a simple proxy for the merging process. It keeps released weights effective in standalone use, but degrades them under re-scaling that often arises in merging, undermining unauthorized recomposition.
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