HANDRAISER learns optimal interruption points in multi-agent LLM communication using estimated future reward and cost, achieving 32.2% lower communication cost with comparable or better task results across games, scheduling, and debate.
Codi: Co-evolving contrastive diffusion models for mixed-type tabular synthesis
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Proposes three metrics for inter-column logical relationships in synthetic tabular data and reports that current generators often fail to preserve them on an industrial dataset.
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Learning to Interrupt in Language-based Multi-agent Communication
HANDRAISER learns optimal interruption points in multi-agent LLM communication using estimated future reward and cost, achieving 32.2% lower communication cost with comparable or better task results across games, scheduling, and debate.
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Evaluating Inter-Column Logical Relationships in Synthetic Tabular Data Generation
Proposes three metrics for inter-column logical relationships in synthetic tabular data and reports that current generators often fail to preserve them on an industrial dataset.