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 "name": "Streaming Quantum Ai Legal Intelligence For Regulatory Compliance Data Sovereignty 6872D5599A",
 "text": "Quantum–AI Legal Intelligence for Regulatory Compliance, Data Sovereignty, and Algorithmic Governance presents the following source proposition: It argues that contemporary legal systems increasingly confront decision environments shaped by automated models, platform infrastructures, cross-border data flows, post-quantum security risks and institutional demands for transparency. This proposition is pertinent to Kaal's source-bound claim that Taken together, the transparency, decentralized decision making, and automated real time response properties of the proposed model make decentralized governance superior to AI driven supervision for secure, compliant, and efficient execution of AI agent transactions. The proposed response is an extension: the relationship should remain limited to the retrieved source proposition and the mapped Kaal claim unless fuller source review supports a broader conclusion.",
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  "identifier": "https://orcid.org/0009-0008-7840-1847"
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  "Mapping review tier: moderate-confidence claim review.",
  "Primary mapping confidence: 0.4069.",
  "The source-to-claim mapping remains explicitly ambiguous and is published with that limitation."
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  "name": "Quantum–AI Legal Intelligence for Regulatory Compliance, Data Sovereignty, and Algorithmic Governance",
  "url": "https://doi.org/10.62311/nesx/rb5m-978-81-687299-5-7"
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  "citation": "Wulf A. Kaal, How can we Best Monitor AI Agents (2025). SSRN: https://ssrn.com/abstract=5245185",
  "paper": "Wulf A. Kaal, How can we Best Monitor AI Agents",
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