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			<guid><![CDATA[https://provide.ai/application-of-deep-reinforcement-learning-to-event-triggered-control-for-networked-artificial-pancreas-systems-2/]]></guid>
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			<guid><![CDATA[https://provide.ai/unlearning-offline-stochastic-multi-armed-bandits/]]></guid>
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			<guid><![CDATA[https://provide.ai/batch-normalization-for-neural-networks-on-complex-domains/]]></guid>
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			<guid><![CDATA[https://provide.ai/knowing-when-to-trust-machine-learned-interatomic-potentials/]]></guid>
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			<title>Knowing when to trust machine-learned interatomic potentials</title>
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			<guid><![CDATA[https://provide.ai/from-unstructured-recall-to-schema-grounded-memory-reliable-ai-memory-via-iterative-schema-aware-extraction-2/]]></guid>
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			<title>From Unstructured Recall to Schema-Grounded Memory: Reliable AI Memory via Iterative, Schema-Aware Extraction</title>
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			<guid><![CDATA[https://provide.ai/splice-latent-diffusion-over-jepa-embeddings-for-conformal-time-series-inpainting/]]></guid>
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			<title>SPLICE: Latent Diffusion over JEPA Embeddings for Conformal Time-Series Inpainting</title>
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			<guid><![CDATA[https://provide.ai/hycop-hybrid-composition-operators-for-interpretable-learning-of-pdes/]]></guid>
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			<title>HyCOP: Hybrid Composition Operators for Interpretable Learning of PDEs</title>
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			<guid><![CDATA[https://provide.ai/cmta-leveraging-cross-modal-temporal-artifacts-for-generalizable-ai-generated-video-detection/]]></guid>
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			<title>CMTA: Leveraging Cross-Modal Temporal Artifacts for Generalizable AI-Generated Video Detection</title>
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			<title>Putting HUMANS first: Efficient LAM Evaluation with Human Preference Alignment</title>
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			<guid><![CDATA[https://provide.ai/randomized-subspace-nesterov-accelerated-gradient/]]></guid>
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			<guid><![CDATA[https://provide.ai/bridging-graph-drawing-and-dimensionality-reduction-with-stochastic-stress-optimization/]]></guid>
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			<guid><![CDATA[https://provide.ai/paired-cslidar-height-stratified-registration-for-cross-source-aerial-ground-lidar-pose-refinement/]]></guid>
			<link><![CDATA[https://provide.ai/paired-cslidar-height-stratified-registration-for-cross-source-aerial-ground-lidar-pose-refinement/]]></link>
			<title>Paired-CSLiDAR: Height-Stratified Registration for Cross-Source Aerial-Ground LiDAR Pose Refinement</title>
			<pubDate><![CDATA[Mon, 04 May 2026 04:00:00 +0000]]></pubDate>
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					<item>
			<guid><![CDATA[https://provide.ai/foundation-models-for-discovery-and-exploration-in-chemical-space/]]></guid>
			<link><![CDATA[https://provide.ai/foundation-models-for-discovery-and-exploration-in-chemical-space/]]></link>
			<title>Foundation Models for Discovery and Exploration in Chemical Space</title>
			<pubDate><![CDATA[Mon, 04 May 2026 04:00:00 +0000]]></pubDate>
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