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Sparse subnetworks in relational GNNs can maintain expressivity, challenging the notion that larger models are always necessary for performance.
Multi-agent LLM systems beat single agents not just through diverse opinions, but by dynamically weighting agents based on input-specific competence, turning deliberation into a learned routing mechanism.
