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Data-Efficient Agentic Graph Domain Adaptation via Reliability-Aware Prototype Learning

arXiv · AI, language, vision and robotics · article · Sep 12, 2026 · UTC

Agentic learning systems are often required to adapt after deployment by observing new data and reusing prior knowledge under limited supervision or feedback. For graph-structured prediction, Graph Domain Adaptation (GDA) naturally instantiates this setting by transferring knowledge from labeled source graphs to unlabeled target graphs under distribution shifts. However, most GDA methods assume sufficient labeled source graphs, which becomes restrictive in data-efficient agentic settings where only limited source evidence can be retained. Under such constraints, source semantics become unrelia

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