Open-weight advanced AI models -- systems whose parameters are freely available for download and adaptation -- are reshaping the global AI landscape. As these models rapidly close the performance gap with closed alternatives, they enable breakthrough research and broaden access to powerful tools. However, once released, they cannot be recalled, and their built-in safeguards can be bypassed through fine-tuning or jailbreaking, posing risks that current governance frameworks are not equipped to address. This report moves beyond the binary framing of ``open'' versus ``closed'' AI. We assess the current landscape of open-weight advanced AI, examining technical capabilities, risk profiles, and regulatory responses across the European Union, United States, China, the United Kingdom, and international forums. We find significant disparities in safety practices across developers and jurisdictions, with no commonly adopted standards for determining when or how advanced models should be released openly. We propose a tiered, safety-anchored approach to model release, where openness is determined by rigorous risk assessment and demonstrated safety rather than ideology or commercial pressure. We outline actionable recommendations for developers, evaluators, standard-setters, and policymakers to enable responsible openness while investing in technical safeguards and societal preparedness.

翻译：开放权重先进人工智能模型——其参数可供自由下载和适配的系统——正在重塑全球人工智能格局。随着这些模型在性能上迅速逼近闭源替代品，它们不仅推动了突破性研究，也拓宽了强大工具的获取渠道。然而，此类模型一旦发布便无法撤回，其内置安全防护措施可能通过微调或越狱手段被绕过，由此产生的风险是当前治理框架尚无法有效应对的。本报告突破“开放”与“封闭”人工智能的二元对立框架，系统评估当前开放权重先进AI的发展态势，从技术能力、风险特征及监管应对等维度，考察欧盟、美国、中国、英国及国际论坛的实践现状。研究发现，不同开发主体与司法管辖区在安全实践层面存在显著差异，且尚未形成关于何时或以何种方式开放高级模型的公认标准。我们提出一种分层化、安全锚定的模型发布框架，其中开放程度应由严格的风险评估和已验证的安全性决定，而非受意识形态或商业压力驱动。报告最后为开发者、评估机构、标准制定者及政策制定者提出可操作建议，旨在实现负责任开放的同时，持续加强技术防护体系与社会应对能力的建设。