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This paper proposes eCNNTO, a CNN with residual connections to accelerate density-based topology optimization by predicting near-optimal densities from early iteration histories, achieving up to 97% reduction in iterations and strong generalization across different boundary conditions, geometries, and mesh resolutions.
This paper introduces TacoMAS, a framework for test-time co-evolution of agent capabilities and communication topology in LLM-based multi-agent systems. It demonstrates that jointly adapting fast capability loops and slow topology loops improves performance and stability over existing baselines.