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Multi-agent LLM training falters when updates are decoupled from joint state transitions; grouping interacting agent outputs into cardinality-normalized set actions solves credit assignment across both static and dynamically routed systems.
Charge-CIM slashes ADC energy consumption by 91.7% while doubling throughput, reshaping the landscape of energy-efficient deep learning accelerators.
