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Decentralized models can learn to route queries to specialized neighbors at inference time, unlocking significant performance gains without a central orchestrator.
LLMs struggle with complex SPICE netlist tasks, achieving only 41% accuracy on device addition despite near-perfect performance on simpler edits.
Feature bagging can dramatically enhance model stability, achieving near-optimal performance with just a few rounds of aggregation.