The article argues that recent executive overreach and politicization have severely undermined established norms of U.S. civil-military relations. To restore professional standards, the incoming Congress must actively exercise its oversight powers, moving beyond mere criticism to demanding transparency in defense policy and personnel decisions. Key reforms include enforcing meritocratic promotion processes, requiring documented justification for senior officer removals, and preventing the partisan use of the military. These actions are crucial for bolstering public trust and ensuring the military remains a nonpartisan instrument of civilian control.
Open-Weight AI Models May Increase Biological Misuse Risks: Assessing Anti-Refusal Tampering, Capability Enhancement, and Publicly Available Uncensored Models
English Summary
The RAND report argues that open-weight Large Language Models (LLMs) significantly increase biological misuse risks because their public availability allows for unauthorized modification and removal of safety safeguards. Key evidence shows that moderately skilled actors can cheaply and reliably tamper with these models—primarily by removing refusal mechanisms—thereby making existing dual-use biological knowledge freely accessible without developer controls. This presents a critical policy challenge: balancing the substantial benefits of open research and transparency against the risk of unrestricted redistribution in sensitive domains like biosecurity. Policymakers must therefore develop strategies to mitigate the threat of tampering and misuse while preserving the scientific and economic advantages of open-source AI.
中文摘要
RAND報告指出，開放權重大型語言模型（LLMs）顯著增加了生物濫用風險，因為其公開可用性允許未經授權的修改和移除安全防護措施。關鍵證據顯示，即使是具備中等技能的行為者，也能低成本且可靠地篡改這些模型——主要方式是移除其拒絕機制——從而使現有的兩用生物知識在缺乏開發者控制的情況下自由流通。這提出了重大的政策挑戰：如何在平衡開放研究和透明度的巨大益處與敏感領域（如生物安全）不受限制的再分配風險之間取得平衡。因此，政策制定者必須制定策略，在維護開源人工智慧的科學和經濟優勢的同時，減輕篡改和濫用威脅。
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