[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-83725-en":3,"doc-seo-83725-105":29,"detail-sidebar-cat-0-en-105":91},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":20,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":13,"seo_description":14,"update_tm":27,"read_time":28},83725,549758252649,"Ivy","https://ap-avatar.wpscdn.com/avatar/8000253669c5317157?_k=1778319167496531819",8,"Research & Report","Macro-Prudential AI Governance A Two-Layer Early Warning and Response System for Frontier AI","Frontier-AI governance faces a structural challenge analogous to pre-2008 banking risk regulation: identifying dangers does not guarantee timely action, and review of individual models fails to manage correlated sector-wide buildup. Building on Basel III and U.S. financial-stability ideas, a Macro-Prudential Early Warning and Response System (MEWRS) is proposed for internal frontier-AI workflows. Layer A routes structured dual-use, autonomy, and security findings via a clearinghouse; Layer B triggers escalating operational safeguards through ECAR, CRTH, and ARS buffers.","Macro-Prudential AI Governance: A Two-Layer Early Warning and Response System for Frontier AI  \nPranav Mehta 1  \narXiv :2607 .03542v 1 [ cs .CY] 3 Jul 2026  \nAbstract  \nFrontier-AI governance today faces a problem structurally analogous to the one banking regulation faced pre-2008, and which post-2008 reforms (Basel III, Dodd–Frank) have since addressed. Two gaps recur: discovering a risk isnot tantamount to acting on it, and individualmodel review is unlike managing correlated buildup across the sector. Drawing on the Basel III [framework and the U.S. financial-stability archi](framework and the U.S. financial-stability archi)tecture, I propose a macro-prudential early warning and response system (”MEWRS”) for internal frontier AI. These are systems deployed for labs’own internal research, testing, and production workflows, as distinct from externally released products. Layer A adapts the finder-coordinatordefender early-warning model to route structured reports on dual-use capabilities, autonomy indicators, and security compromises through a government clearinghouse to domain-specific defender working groups. Layer B calibrates operational controls via three quantitative buffer metrics, namely Effective Compute-at-Risk (ECAR), Cumulative Red-Team Hours (CRTH), and an Alignment Robustness Score (ARS), so that faster capability scaling automatically triggers stronger safeguards, analogously to how risk-weighted assets drive capital ratios under Basel III. I outline the reporting schema, map six Basel III mechanisms onto AI-governance analogues, identify seven failure modes with concrete mitigations, and sketch an exercise-based validation plan. MEWRS is designed to detect correlated risk build-ups across the frontier-AI sector and create pre-committed off-ramps before a cascade unfolds.  \n1MATS Research, Berkeley, CA, USA. Correspondence to: Pranav Mehta \u003C[pranav.mehta@columbia.edu](pranav.mehta@columbia.edu) >.  \nProceedings of the 43 rd International Conference on Machine Learning, Seoul, South Korea. PMLR 306, 2026 . Copyright 2026 by the author(s) .  \n1. Introduction  \nPost-2008 financial regulation solved an adjacent pair of problems with two families of mechanisms. Microprudential tools (capital buffers, stress testing, disclosure) strengthen individual institutions. Macro-prudential tools (counter-cyclical buffers, systemic-institution tiering, resolution “living wills”) target correlated exposures and selfreinforcing dynamics across the sector (Basel Committee, 2017 ; FSB, 2011) . Current frontier-AI governance work, including dangerous-capability evaluations (Shevlane et al., 2023), responsible scaling policies (Anthropic, 2026 ; OpenAI, 2025), and structured frontier-regulation proposals (Anderljung et al., 2023), maps cleanly onto the microprudential side: each targets the safety of individual models or individual release decisions. The macro-prudential toolkit, designed for correlated risk build-up across institutions, is underdeveloped, leaving the sector exposed to sector-level failure modes. For example, a jailbreak technique discovered against one lab’s agentic system may generalize to structurally similar systems at other labs before any single lab’s internal review cycle can respond (see §6) .  \nA parallel gap exists within the development pipeline. A growing body of research has developed tools for model evaluations, red-teaming, and early-warning indicators (Shevlane et al., 2023 ; Anderljung et al., 2023), though their formalisation in regulation remains limited—the principal exception being the EU AI Act’s systemic-risk obligations for general-purpose AI models (model evaluation, adversarial testing, and serious-incident reporting under Art. 55), operationalised through the General-Purpose AI Code of Practice finalised in July 2025 (European Union, 2024 ; European Commission, 2025) . These tools have become more sophisticated, even as the underlying task of reliable capability elicitation has become harde","cbCaidLSuIuvHakc","https://ap.wps.com/l/cbCaidLSuIuvHakc","pdf",344013,1,14,"English","en",105,"# Introduction\n## Contribution\n# Macro-Prudential Early Warning and Response System (MEWRS)","[{\"question\":\"What problem does MEWRS aim to solve in frontier-AI governance?\",\"answer\":\"MEWRS targets two gaps: discovery is not equivalent to effective action, and individual model review does not address correlated risk buildup across the sector.\"},{\"question\":\"How does Layer A of MEWRS function?\",\"answer\":\"Layer A adapts a finder–coordinator–defender early-warning structure to route structured reports on dual-use capabilities, autonomy indicators, and security compromises to domain-specific defender working groups via a government clearinghouse.\"},{\"question\":\"How does Layer B determine when to strengthen safeguards?\",\"answer\":\"Layer B uses three quantitative buffer metrics—Effective Compute-at-Risk (ECAR), Cumulative Red-Team Hours (CRTH), and an Alignment Robustness Score (ARS)—so faster capability scaling automatically triggers stronger operational 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problem does MEWRS aim to solve in frontier-AI governance?","Question",{"text":75,"@type":76},"MEWRS targets two gaps: discovery is not equivalent to effective action, and individual model review does not address correlated risk buildup across the sector.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"How does Layer A of MEWRS function?",{"text":80,"@type":76},"Layer A adapts a finder–coordinator–defender early-warning structure to route structured reports on dual-use capabilities, autonomy indicators, and security compromises to domain-specific defender working groups via a government clearinghouse.",{"name":82,"@type":73,"acceptedAnswer":83},"How does Layer B determine when to strengthen safeguards?",{"text":84,"@type":76},"Layer B uses three quantitative buffer metrics—Effective Compute-at-Risk (ECAR), Cumulative Red-Team Hours (CRTH), and an Alignment Robustness Score (ARS)—so faster capability scaling automatically triggers stronger operational 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