<?xml version="1.0" encoding="UTF-8"?>
<!-- This sitemap was dynamically generated on May 16, 2026 at 4:29 am by All in One SEO v4.9.6.2 - the original SEO plugin for WordPress. -->

<?xml-stylesheet type="text/xsl" href="https://provide.ai/default-sitemap.xsl"?>

<rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom">
	<channel>
		<title>Provide.ai</title>
		<link><![CDATA[https://provide.ai]]></link>
		<description><![CDATA[Provide.ai]]></description>
		<lastBuildDate><![CDATA[Sat, 16 May 2026 04:00:00 +0000]]></lastBuildDate>
		<docs>https://validator.w3.org/feed/docs/rss2.html</docs>
		<atom:link href="https://provide.ai/sitemap.rss" rel="self" type="application/rss+xml" />
		<ttl><![CDATA[60]]></ttl>

		<item>
			<guid><![CDATA[https://provide.ai/know-when-to-fold-em-token-efficient-llm-synthetic-data-generation-via-multi-stage-in-flight-rejection/]]></guid>
			<link><![CDATA[https://provide.ai/know-when-to-fold-em-token-efficient-llm-synthetic-data-generation-via-multi-stage-in-flight-rejection/]]></link>
			<title>Know When To Fold &#8216;Em: Token-Efficient LLM Synthetic Data Generation via Multi-Stage In-Flight Rejection</title>
			<pubDate><![CDATA[Sat, 16 May 2026 04:00:00 +0000]]></pubDate>
		</item>
					<item>
			<guid><![CDATA[https://provide.ai/a-problem-oriented-taxonomy-of-evaluation-metrics-for-time-series-anomaly-detection/]]></guid>
			<link><![CDATA[https://provide.ai/a-problem-oriented-taxonomy-of-evaluation-metrics-for-time-series-anomaly-detection/]]></link>
			<title>A Problem-Oriented Taxonomy of Evaluation Metrics for Time Series Anomaly Detection</title>
			<pubDate><![CDATA[Sat, 16 May 2026 04:00:00 +0000]]></pubDate>
		</item>
					<item>
			<guid><![CDATA[https://provide.ai/sematune-semantic-aware-online-os-tuning-with-large-language-models/]]></guid>
			<link><![CDATA[https://provide.ai/sematune-semantic-aware-online-os-tuning-with-large-language-models/]]></link>
			<title>SemaTune: Semantic-Aware Online OS Tuning with Large Language Models</title>
			<pubDate><![CDATA[Sat, 16 May 2026 04:00:00 +0000]]></pubDate>
		</item>
					<item>
			<guid><![CDATA[https://provide.ai/krause-synchronization-transformers/]]></guid>
			<link><![CDATA[https://provide.ai/krause-synchronization-transformers/]]></link>
			<title>Krause Synchronization Transformers</title>
			<pubDate><![CDATA[Sat, 16 May 2026 04:00:00 +0000]]></pubDate>
		</item>
					<item>
			<guid><![CDATA[https://provide.ai/skillflow-flow-driven-recursive-skill-evolution-for-agentic-orchestration/]]></guid>
			<link><![CDATA[https://provide.ai/skillflow-flow-driven-recursive-skill-evolution-for-agentic-orchestration/]]></link>
			<title>SkillFlow: Flow-Driven Recursive Skill Evolution for Agentic Orchestration</title>
			<pubDate><![CDATA[Sat, 16 May 2026 04:00:00 +0000]]></pubDate>
		</item>
					<item>
			<guid><![CDATA[https://provide.ai/chromaflow-a-negative-ablation-study-of-orchestration-overhead-in-tool-augmented-agent-evaluation/]]></guid>
			<link><![CDATA[https://provide.ai/chromaflow-a-negative-ablation-study-of-orchestration-overhead-in-tool-augmented-agent-evaluation/]]></link>
			<title>ChromaFlow: A Negative Ablation Study of Orchestration Overhead in Tool-Augmented Agent Evaluation</title>
			<pubDate><![CDATA[Sat, 16 May 2026 04:00:00 +0000]]></pubDate>
		</item>
					<item>
			<guid><![CDATA[https://provide.ai/beyond-the-final-answer-evaluating-the-reasoning-trajectories-of-tool-augmented-agents/]]></guid>
			<link><![CDATA[https://provide.ai/beyond-the-final-answer-evaluating-the-reasoning-trajectories-of-tool-augmented-agents/]]></link>
			<title>Beyond the Final Answer: Evaluating the Reasoning Trajectories of Tool-Augmented Agents</title>
			<pubDate><![CDATA[Sat, 16 May 2026 04:00:00 +0000]]></pubDate>
		</item>
					<item>
			<guid><![CDATA[https://provide.ai/achieving-approximate-symmetry-is-exponentially-easier-than-exact-symmetry/]]></guid>
			<link><![CDATA[https://provide.ai/achieving-approximate-symmetry-is-exponentially-easier-than-exact-symmetry/]]></link>
			<title>Achieving Approximate Symmetry Is Exponentially Easier than Exact Symmetry</title>
			<pubDate><![CDATA[Sat, 16 May 2026 04:00:00 +0000]]></pubDate>
		</item>
					<item>
			<guid><![CDATA[https://provide.ai/modeling-bounded-rationality-in-drug-shortage-pharmacists-using-attention-guided-dynamic-decomposition/]]></guid>
			<link><![CDATA[https://provide.ai/modeling-bounded-rationality-in-drug-shortage-pharmacists-using-attention-guided-dynamic-decomposition/]]></link>
			<title>Modeling Bounded Rationality in Drug Shortage Pharmacists Using Attention-Guided Dynamic Decomposition</title>
			<pubDate><![CDATA[Sat, 16 May 2026 04:00:00 +0000]]></pubDate>
		</item>
					<item>
			<guid><![CDATA[https://provide.ai/catching-the-infection-before-it-spreads-foresight-guided-defense-in-multi-agent-systems-3/]]></guid>
			<link><![CDATA[https://provide.ai/catching-the-infection-before-it-spreads-foresight-guided-defense-in-multi-agent-systems-3/]]></link>
			<title>Catching the Infection Before It Spreads: Foresight-Guided Defense in Multi-Agent Systems</title>
			<pubDate><![CDATA[Sat, 16 May 2026 04:00:00 +0000]]></pubDate>
		</item>
					<item>
			<guid><![CDATA[https://provide.ai/clawforge-generating-executable-interactive-benchmarks-for-command-line-agents/]]></guid>
			<link><![CDATA[https://provide.ai/clawforge-generating-executable-interactive-benchmarks-for-command-line-agents/]]></link>
			<title>ClawForge: Generating Executable Interactive Benchmarks for Command-Line Agents</title>
			<pubDate><![CDATA[Sat, 16 May 2026 04:00:00 +0000]]></pubDate>
		</item>
					<item>
			<guid><![CDATA[https://provide.ai/sequential-resource-trading-using-comparison-based-gradient-estimation/]]></guid>
			<link><![CDATA[https://provide.ai/sequential-resource-trading-using-comparison-based-gradient-estimation/]]></link>
			<title>Sequential Resource Trading Using Comparison-Based Gradient Estimation</title>
			<pubDate><![CDATA[Sat, 16 May 2026 04:00:00 +0000]]></pubDate>
		</item>
					<item>
			<guid><![CDATA[https://provide.ai/distribution-aware-algorithm-design-with-llm-agents/]]></guid>
			<link><![CDATA[https://provide.ai/distribution-aware-algorithm-design-with-llm-agents/]]></link>
			<title>Distribution-Aware Algorithm Design with LLM Agents</title>
			<pubDate><![CDATA[Sat, 16 May 2026 04:00:00 +0000]]></pubDate>
		</item>
					<item>
			<guid><![CDATA[https://provide.ai/novel-dynamic-batch-sensitive-adam-optimiser-for-vehicular-accident-injury-severity-prediction/]]></guid>
			<link><![CDATA[https://provide.ai/novel-dynamic-batch-sensitive-adam-optimiser-for-vehicular-accident-injury-severity-prediction/]]></link>
			<title>Novel Dynamic Batch-Sensitive Adam Optimiser for Vehicular Accident Injury Severity Prediction</title>
			<pubDate><![CDATA[Sat, 16 May 2026 04:00:00 +0000]]></pubDate>
		</item>
					<item>
			<guid><![CDATA[https://provide.ai/agentic-systems-as-boosting-weak-reasoning-models/]]></guid>
			<link><![CDATA[https://provide.ai/agentic-systems-as-boosting-weak-reasoning-models/]]></link>
			<title>Agentic Systems as Boosting Weak Reasoning Models</title>
			<pubDate><![CDATA[Sat, 16 May 2026 04:00:00 +0000]]></pubDate>
		</item>
					<item>
			<guid><![CDATA[https://provide.ai/a-cross-species-neural-foundation-model-for-end-to-end-speech-decoding-2/]]></guid>
			<link><![CDATA[https://provide.ai/a-cross-species-neural-foundation-model-for-end-to-end-speech-decoding-2/]]></link>
			<title>A cross-species neural foundation model for end-to-end speech decoding</title>
			<pubDate><![CDATA[Sat, 16 May 2026 04:00:00 +0000]]></pubDate>
		</item>
					<item>
			<guid><![CDATA[https://provide.ai/unsteady-metrics-and-benchmarking-cultures-of-ai-model-builders/]]></guid>
			<link><![CDATA[https://provide.ai/unsteady-metrics-and-benchmarking-cultures-of-ai-model-builders/]]></link>
			<title>Unsteady Metrics and Benchmarking Cultures of AI Model Builders</title>
			<pubDate><![CDATA[Sat, 16 May 2026 04:00:00 +0000]]></pubDate>
		</item>
					<item>
			<guid><![CDATA[https://provide.ai/boosting-llm-reasoning-via-human-inspired-reward-shaping/]]></guid>
			<link><![CDATA[https://provide.ai/boosting-llm-reasoning-via-human-inspired-reward-shaping/]]></link>
			<title>Boosting LLM Reasoning via Human-Inspired Reward Shaping</title>
			<pubDate><![CDATA[Sat, 16 May 2026 04:00:00 +0000]]></pubDate>
		</item>
					<item>
			<guid><![CDATA[https://provide.ai/the-evaluation-trap-benchmark-design-as-theoretical-commitment/]]></guid>
			<link><![CDATA[https://provide.ai/the-evaluation-trap-benchmark-design-as-theoretical-commitment/]]></link>
			<title>The Evaluation Trap: Benchmark Design as Theoretical Commitment</title>
			<pubDate><![CDATA[Sat, 16 May 2026 04:00:00 +0000]]></pubDate>
		</item>
					<item>
			<guid><![CDATA[https://provide.ai/grounded-continuation-a-linear-time-runtime-verifier-for-llm-conversations/]]></guid>
			<link><![CDATA[https://provide.ai/grounded-continuation-a-linear-time-runtime-verifier-for-llm-conversations/]]></link>
			<title>Grounded Continuation: A Linear-Time Runtime Verifier for LLM Conversations</title>
			<pubDate><![CDATA[Sat, 16 May 2026 04:00:00 +0000]]></pubDate>
		</item>
					<item>
			<guid><![CDATA[https://provide.ai/quantifying-and-mitigating-self-preference-bias-of-llm-judges-3/]]></guid>
			<link><![CDATA[https://provide.ai/quantifying-and-mitigating-self-preference-bias-of-llm-judges-3/]]></link>
			<title>Quantifying and Mitigating Self-Preference Bias of LLM Judges</title>
			<pubDate><![CDATA[Sat, 16 May 2026 04:00:00 +0000]]></pubDate>
		</item>
					<item>
			<guid><![CDATA[https://provide.ai/simpersona-learning-discrete-buyer-personas-from-raw-clickstreams-for-grounded-e-commerce-agents/]]></guid>
			<link><![CDATA[https://provide.ai/simpersona-learning-discrete-buyer-personas-from-raw-clickstreams-for-grounded-e-commerce-agents/]]></link>
			<title>SimPersona: Learning Discrete Buyer Personas from Raw Clickstreams for Grounded E-Commerce Agents</title>
			<pubDate><![CDATA[Sat, 16 May 2026 04:00:00 +0000]]></pubDate>
		</item>
					<item>
			<guid><![CDATA[https://provide.ai/mpu-towards-secure-and-privacy-preserving-knowledge-unlearning-for-large-language-models/]]></guid>
			<link><![CDATA[https://provide.ai/mpu-towards-secure-and-privacy-preserving-knowledge-unlearning-for-large-language-models/]]></link>
			<title>MPU: Towards Secure and Privacy-Preserving Knowledge Unlearning for Large Language Models</title>
			<pubDate><![CDATA[Sat, 16 May 2026 04:00:00 +0000]]></pubDate>
		</item>
					<item>
			<guid><![CDATA[https://provide.ai/ash-agents-that-self-hone-via-embodied-learning/]]></guid>
			<link><![CDATA[https://provide.ai/ash-agents-that-self-hone-via-embodied-learning/]]></link>
			<title>ASH: Agents that Self-Hone via Embodied Learning</title>
			<pubDate><![CDATA[Sat, 16 May 2026 04:00:00 +0000]]></pubDate>
		</item>
					<item>
			<guid><![CDATA[https://provide.ai/vggt-edit-feed-forward-native-3d-scene-editing-with-residual-field-prediction/]]></guid>
			<link><![CDATA[https://provide.ai/vggt-edit-feed-forward-native-3d-scene-editing-with-residual-field-prediction/]]></link>
			<title>VGGT-Edit: Feed-forward Native 3D Scene Editing with Residual Field Prediction</title>
			<pubDate><![CDATA[Sat, 16 May 2026 04:00:00 +0000]]></pubDate>
		</item>
					<item>
			<guid><![CDATA[https://provide.ai/leansearch-v2-global-premise-retrieval-for-lean-4-theorem-proving-2/]]></guid>
			<link><![CDATA[https://provide.ai/leansearch-v2-global-premise-retrieval-for-lean-4-theorem-proving-2/]]></link>
			<title>LeanSearch v2: Global Premise Retrieval for Lean 4 Theorem Proving</title>
			<pubDate><![CDATA[Sat, 16 May 2026 04:00:00 +0000]]></pubDate>
		</item>
					<item>
			<guid><![CDATA[https://provide.ai/terminator-learning-optimal-exit-points-for-early-stopping-in-chain-of-thought-reasoning/]]></guid>
			<link><![CDATA[https://provide.ai/terminator-learning-optimal-exit-points-for-early-stopping-in-chain-of-thought-reasoning/]]></link>
			<title>TERMINATOR: Learning Optimal Exit Points for Early Stopping in Chain-of-Thought Reasoning</title>
			<pubDate><![CDATA[Sat, 16 May 2026 04:00:00 +0000]]></pubDate>
		</item>
					<item>
			<guid><![CDATA[https://provide.ai/metaagent-x-breaking-the-ceiling-of-automatic-multi-agent-systems-via-end-to-end-reinforcement-learning/]]></guid>
			<link><![CDATA[https://provide.ai/metaagent-x-breaking-the-ceiling-of-automatic-multi-agent-systems-via-end-to-end-reinforcement-learning/]]></link>
			<title>MetaAgent-X : Breaking the Ceiling of Automatic Multi-Agent Systems via End-to-End Reinforcement Learning</title>
			<pubDate><![CDATA[Sat, 16 May 2026 04:00:00 +0000]]></pubDate>
		</item>
					<item>
			<guid><![CDATA[https://provide.ai/logging-policy-design-for-off-policy-evaluation/]]></guid>
			<link><![CDATA[https://provide.ai/logging-policy-design-for-off-policy-evaluation/]]></link>
			<title>Logging Policy Design for Off-Policy Evaluation</title>
			<pubDate><![CDATA[Sat, 16 May 2026 04:00:00 +0000]]></pubDate>
		</item>
					<item>
			<guid><![CDATA[https://provide.ai/gencircuit-rl-reinforcement-learning-from-hierarchical-verification-for-genetic-circuit-design/]]></guid>
			<link><![CDATA[https://provide.ai/gencircuit-rl-reinforcement-learning-from-hierarchical-verification-for-genetic-circuit-design/]]></link>
			<title>GenCircuit-RL: Reinforcement Learning from Hierarchical Verification for Genetic Circuit Design</title>
			<pubDate><![CDATA[Sat, 16 May 2026 04:00:00 +0000]]></pubDate>
		</item>
					<item>
			<guid><![CDATA[https://provide.ai/graphs-of-research-citation-evolution-graphs-as-supervision-for-research-idea-generation/]]></guid>
			<link><![CDATA[https://provide.ai/graphs-of-research-citation-evolution-graphs-as-supervision-for-research-idea-generation/]]></link>
			<title>Graphs of Research: Citation Evolution Graphs as Supervision for Research Idea Generation</title>
			<pubDate><![CDATA[Sat, 16 May 2026 04:00:00 +0000]]></pubDate>
		</item>
					<item>
			<guid><![CDATA[https://provide.ai/fusion-fission-forecasts-when-ai-will-shift-to-undesirable-behavior/]]></guid>
			<link><![CDATA[https://provide.ai/fusion-fission-forecasts-when-ai-will-shift-to-undesirable-behavior/]]></link>
			<title>Fusion-fission forecasts when AI will shift to undesirable behavior</title>
			<pubDate><![CDATA[Sat, 16 May 2026 04:00:00 +0000]]></pubDate>
		</item>
					<item>
			<guid><![CDATA[https://provide.ai/towards-in-depth-root-cause-localization-for-microservices-with-multi-agent-recursion-of-thought/]]></guid>
			<link><![CDATA[https://provide.ai/towards-in-depth-root-cause-localization-for-microservices-with-multi-agent-recursion-of-thought/]]></link>
			<title>Towards In-Depth Root Cause Localization for Microservices with Multi-Agent Recursion-of-Thought</title>
			<pubDate><![CDATA[Sat, 16 May 2026 04:00:00 +0000]]></pubDate>
		</item>
					<item>
			<guid><![CDATA[https://provide.ai/good-to-go-the-loop-skill-engine-that-hits-99-success-and-slashes-token-usage-by-99-via-one-shot-recording-and-deterministic-replay/]]></guid>
			<link><![CDATA[https://provide.ai/good-to-go-the-loop-skill-engine-that-hits-99-success-and-slashes-token-usage-by-99-via-one-shot-recording-and-deterministic-replay/]]></link>
			<title>Good to Go: The LOOP Skill Engine That Hits 99% Success and Slashes Token Usage by 99% via One-Shot Recording and Deterministic Replay</title>
			<pubDate><![CDATA[Sat, 16 May 2026 04:00:00 +0000]]></pubDate>
		</item>
					<item>
			<guid><![CDATA[https://provide.ai/text-knows-what-tables-know-when-clinical-timeline-reconstruction-via-retrieval-augmented-multimodal-alignment/]]></guid>
			<link><![CDATA[https://provide.ai/text-knows-what-tables-know-when-clinical-timeline-reconstruction-via-retrieval-augmented-multimodal-alignment/]]></link>
			<title>Text Knows What, Tables Know When: Clinical Timeline Reconstruction via Retrieval-Augmented Multimodal Alignment</title>
			<pubDate><![CDATA[Sat, 16 May 2026 04:00:00 +0000]]></pubDate>
		</item>
					<item>
			<guid><![CDATA[https://provide.ai/hypergraph-enterprise-agentic-reasoner-over-heterogeneous-business-systems/]]></guid>
			<link><![CDATA[https://provide.ai/hypergraph-enterprise-agentic-reasoner-over-heterogeneous-business-systems/]]></link>
			<title>Hypergraph Enterprise Agentic Reasoner over Heterogeneous Business Systems</title>
			<pubDate><![CDATA[Sat, 16 May 2026 04:00:00 +0000]]></pubDate>
		</item>
					<item>
			<guid><![CDATA[https://provide.ai/quantifying-and-mitigating-premature-closure-in-frontier-llms/]]></guid>
			<link><![CDATA[https://provide.ai/quantifying-and-mitigating-premature-closure-in-frontier-llms/]]></link>
			<title>Quantifying and Mitigating Premature Closure in Frontier LLMs</title>
			<pubDate><![CDATA[Sat, 16 May 2026 04:00:00 +0000]]></pubDate>
		</item>
					<item>
			<guid><![CDATA[https://provide.ai/predict-then-diffuse-adaptive-response-length-for-compute-budgeted-inference-in-diffusion-llms-2/]]></guid>
			<link><![CDATA[https://provide.ai/predict-then-diffuse-adaptive-response-length-for-compute-budgeted-inference-in-diffusion-llms-2/]]></link>
			<title>Predict-then-Diffuse: Adaptive Response Length for Compute-Budgeted Inference in Diffusion LLMs</title>
			<pubDate><![CDATA[Sat, 16 May 2026 04:00:00 +0000]]></pubDate>
		</item>
					<item>
			<guid><![CDATA[https://provide.ai/heuristic-pathologies-and-further-variance-reduction-via-uncertainty-propagation-in-the-aivat-family-of-techniques/]]></guid>
			<link><![CDATA[https://provide.ai/heuristic-pathologies-and-further-variance-reduction-via-uncertainty-propagation-in-the-aivat-family-of-techniques/]]></link>
			<title>Heuristic Pathologies and Further Variance Reduction via Uncertainty Propagation in the AIVAT Family of Techniques</title>
			<pubDate><![CDATA[Sat, 16 May 2026 04:00:00 +0000]]></pubDate>
		</item>
					<item>
			<guid><![CDATA[https://provide.ai/agentic-ai-ecosystems-in-higher-education-a-perspective-on-ai-agents-to-emerging-inclusive-agentic-multi-agent-ai-framework-for-learning-teaching-and-institutional-intelligence/]]></guid>
			<link><![CDATA[https://provide.ai/agentic-ai-ecosystems-in-higher-education-a-perspective-on-ai-agents-to-emerging-inclusive-agentic-multi-agent-ai-framework-for-learning-teaching-and-institutional-intelligence/]]></link>
			<title>Agentic AI Ecosystems in Higher Education: A Perspective on AI Agents to Emerging Inclusive, Agentic Multi-Agent AI Framework for Learning, Teaching and Institutional Intelligence</title>
			<pubDate><![CDATA[Sat, 16 May 2026 04:00:00 +0000]]></pubDate>
		</item>
					<item>
			<guid><![CDATA[https://provide.ai/parallelizing-counterfactual-regret-minimization/]]></guid>
			<link><![CDATA[https://provide.ai/parallelizing-counterfactual-regret-minimization/]]></link>
			<title>Parallelizing Counterfactual Regret Minimization</title>
			<pubDate><![CDATA[Sat, 16 May 2026 04:00:00 +0000]]></pubDate>
		</item>
					<item>
			<guid><![CDATA[https://provide.ai/towards-label-free-single-cell-phenotyping-using-multi-task-learning/]]></guid>
			<link><![CDATA[https://provide.ai/towards-label-free-single-cell-phenotyping-using-multi-task-learning/]]></link>
			<title>Towards Label-Free Single-Cell Phenotyping Using Multi-Task Learning</title>
			<pubDate><![CDATA[Sat, 16 May 2026 04:00:00 +0000]]></pubDate>
		</item>
					<item>
			<guid><![CDATA[https://provide.ai/precise-verification-of-transformers-through-relu-catalyzed-abstraction-refinement/]]></guid>
			<link><![CDATA[https://provide.ai/precise-verification-of-transformers-through-relu-catalyzed-abstraction-refinement/]]></link>
			<title>Precise Verification of Transformers through ReLU-Catalyzed Abstraction Refinement</title>
			<pubDate><![CDATA[Sat, 16 May 2026 04:00:00 +0000]]></pubDate>
		</item>
					<item>
			<guid><![CDATA[https://provide.ai/gradient-iterated-temporal-difference-learning/]]></guid>
			<link><![CDATA[https://provide.ai/gradient-iterated-temporal-difference-learning/]]></link>
			<title>Gradient Iterated Temporal-Difference Learning</title>
			<pubDate><![CDATA[Sat, 16 May 2026 04:00:00 +0000]]></pubDate>
		</item>
					<item>
			<guid><![CDATA[https://provide.ai/geolaux-a-benchmark-for-evaluating-mllms-geometry-performance-on-long-step-problems-requiring-auxiliary-lines-2/]]></guid>
			<link><![CDATA[https://provide.ai/geolaux-a-benchmark-for-evaluating-mllms-geometry-performance-on-long-step-problems-requiring-auxiliary-lines-2/]]></link>
			<title>GeoLaux: A Benchmark for Evaluating MLLMs&#8217; Geometry Performance on Long-Step Problems Requiring Auxiliary Lines</title>
			<pubDate><![CDATA[Sat, 16 May 2026 04:00:00 +0000]]></pubDate>
		</item>
					<item>
			<guid><![CDATA[https://provide.ai/semantic-feature-segmentation-for-interpretable-predictive-maintenance-in-complex-systems/]]></guid>
			<link><![CDATA[https://provide.ai/semantic-feature-segmentation-for-interpretable-predictive-maintenance-in-complex-systems/]]></link>
			<title>Semantic Feature Segmentation for Interpretable Predictive Maintenance in Complex Systems</title>
			<pubDate><![CDATA[Sat, 16 May 2026 04:00:00 +0000]]></pubDate>
		</item>
					<item>
			<guid><![CDATA[https://provide.ai/e-mem-multi-agent-based-episodic-context-reconstruction-for-llm-agent-memory-3/]]></guid>
			<link><![CDATA[https://provide.ai/e-mem-multi-agent-based-episodic-context-reconstruction-for-llm-agent-memory-3/]]></link>
			<title>E-mem: Multi-agent based Episodic Context Reconstruction for LLM Agent Memory</title>
			<pubDate><![CDATA[Sat, 16 May 2026 04:00:00 +0000]]></pubDate>
		</item>
					<item>
			<guid><![CDATA[https://provide.ai/are-agents-ready-to-teach-a-multi-stage-benchmark-for-real-world-teaching-workflows/]]></guid>
			<link><![CDATA[https://provide.ai/are-agents-ready-to-teach-a-multi-stage-benchmark-for-real-world-teaching-workflows/]]></link>
			<title>Are Agents Ready to Teach? A Multi-Stage Benchmark for Real-World Teaching Workflows</title>
			<pubDate><![CDATA[Sat, 16 May 2026 04:00:00 +0000]]></pubDate>
		</item>
					<item>
			<guid><![CDATA[https://provide.ai/loka-low-precision-kernel-applications-for-recommendation-models-at-scale-2/]]></guid>
			<link><![CDATA[https://provide.ai/loka-low-precision-kernel-applications-for-recommendation-models-at-scale-2/]]></link>
			<title>LoKA: Low-precision Kernel Applications for Recommendation Models At Scale</title>
			<pubDate><![CDATA[Sat, 16 May 2026 04:00:00 +0000]]></pubDate>
		</item>
					<item>
			<guid><![CDATA[https://provide.ai/proactive-memory-for-ad-hoc-recall-over-streaming-dialogues/]]></guid>
			<link><![CDATA[https://provide.ai/proactive-memory-for-ad-hoc-recall-over-streaming-dialogues/]]></link>
			<title>Proactive Memory for Ad-Hoc Recall over Streaming Dialogues</title>
			<pubDate><![CDATA[Sat, 16 May 2026 04:00:00 +0000]]></pubDate>
		</item>
				</channel>
</rss>
