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.
A gent D ropout: Dynamic agent elimination for token-efficient and high-performance LLM -based multi-agent collaboration
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MASPO jointly optimizes prompts in multi-agent LLM systems via downstream-success evaluation and evolutionary beam search, delivering 2.9 average accuracy gains over prior methods across six tasks.
CONCAT introduces a consensus- and confidence-driven ad hoc teaming method that reduces communication overhead in LLM-based multi-agent systems by up to 50% latency while improving efficiency ratio without any training.
citing papers explorer
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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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MASPO: Joint Prompt Optimization for LLM-based Multi-Agent Systems
MASPO jointly optimizes prompts in multi-agent LLM systems via downstream-success evaluation and evolutionary beam search, delivering 2.9 average accuracy gains over prior methods across six tasks.
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CONCAT: Consensus- and Confidence-Driven Ad Hoc Teaming for Efficient LLM-Based Multi-Agent Systems
CONCAT introduces a consensus- and confidence-driven ad hoc teaming method that reduces communication overhead in LLM-based multi-agent systems by up to 50% latency while improving efficiency ratio without any training.