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 "name": "Streaming Reinforcement Learning For Adaptive Traffic Rule Compliance In Autonomou 851Fa5Da0A",
 "text": "Reinforcement Learning for Adaptive Traffic Rule Compliance in Autonomous Driving Systems: A Multi-agent Framework for Dynamic Regulatory Adaptation presents the following source proposition: This article proposes a novel multi-agent reinforcement learning framework that enables self-driving vehicles to dynamically adjust their behavior to different traffic rules while optimizing for safety, efficiency, and legal compliance. This proposition is pertinent to Kaal's source-bound claim that WDAGs allow new regulatory and ethical standards to be integrated into existing AI systems without overhauling the entire model architecture, which is what makes rapid legal adaptation feasible in sectors such as public safety and healthcare. 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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  "The source-to-claim mapping remains explicitly ambiguous and is published with that limitation."
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