๐ค AI Summary
This work addresses the scalability limitations of existing large language modelโbased multi-agent systems, where fully connected communication incurs near-quadratic growth in communication overhead with agent count, while fixed sparse topologies lack adaptability to dynamic task demands. The authors propose DySCo, a novel dynamic trust-aware sparse communication mechanism that, for each reasoning round, evaluates the utility of potential communication edges based on agent reliability, answer divergence, and task relevance. Under a communication budget constraint, DySCo selectively activates high-value edges for on-demand interaction and aggregates responses using dynamically adjusted trust weights, terminating discussions early upon achieving consensus stability. Experiments demonstrate that DySCo substantially reduces communication overhead and latency across mathematical reasoning, logical reasoning, and factual question-answering tasks, while maintaining or even improving accuracy and consensus stability.
๐ Abstract
Large language model-driven multi-agent systems enhance the reliability of complex reasoning tasks through multi-round deliberation, role specialization, and cross-validation. However, existing multi-agent debate and collaboration frameworks typically adopt fully connected communication, causing the number of messages, token costs, and end-to-end latency to grow approximately quadratically with the number of agents; although fixed sparse topologies reduce overhead, they cannot adapt communication relationships to different task instances or intermediate reasoning states, making them prone either to preserving low-value interactions or to losing critical error-correction information. To address this problem, this paper proposes DySCo (Dynamic Sparse Consensus), a dynamic trust-aware sparse consensus mechanism. In each round of reasoning, DySCo estimates the value of communication edges based on agent reliability, answer divergence, and task relevance, and selects a small number of high-value edges for message exchange under budget constraints; it then aggregates the answers of different agents through dynamic trust weights and terminates the discussion early once consensus stabilizes. This mechanism replaces universal broadcasting with on-demand communication, thereby reducing communication overhead while preserving essential cross-validation information. We further present analyses of communication complexity and consensus stability, and evaluate the performance of DySCo on mathematical reasoning, logical reasoning, and factual question-answering tasks.