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• SCoUT introduces temporal grouping that resamples soft agent groups every K environment steps using Gumbel-Softmax during training.method 95%
• SCoUT uses a group-aware critic that predicts values for each agent group and maps them to per-agent baselines through soft assignments.method 93%
• SCoUT employs a three-headed policy for each agent: environment action, send decision, and recipient selection.method 94%
• SCoUT derives counterfactual communication advantages by analytically removing each sender's contribution from the recipient's aggregated messages.method 92%
• SCoUT enables precise credit assignment for both send and recipient-selection decisions through counterfactual computation.result 91%
• SCoUT preserves decentralized execution at inference time by discarding centralized training components.technical 96%
• SCoUT reduces critic complexity and variance through group-based value prediction and mapping.technical 88%
• Traditional MARL communication approaches struggle with learning when and who to communicate with due to many possible sender-recipient pairs and difficulty isolating message effects on future reward.limitation 89%

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