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This paper presents a graph-learning-aided optimization approach for designing active tether-net systems to capture space debris, using a GNN to recommend candidate designs and reduce mixed-combinatorial nonlinear programming to standard NLP problems, achieving faster convergence.
This paper introduces GraphReAct, a framework that extends reasoning-acting paradigms to graph-structured data for multi-step inference. It combines topological and semantic retrieval with context refinement to improve performance on graph learning benchmarks.