Large language model (LLM)-based multi-agent systems (MAS) improve reasoning through specialized collaboration, but dense multi-round communication can increase prompt cost and expose agents to outdated, redundant, or noisy context. A prominent line of MAS communication-efficiency work reduces communication through agent-, edge-, or topology-level structural decisions, which operate on graph structure rather than the realized text entering a specific receiver prompt. Such structural decisions are too coarse for runtime communication: messages sent over the same edge may carry useful evidence in one query, redundant content in another, or only a few useful spans. We introduce MessageSieve, a training-free runtime message pruning policy for fixed-graph MAS that operates on message instances rather than agents or edges. Before prompt assembly, it uses lightweight state cards and local message comparisons to preserve original text, retain compact evidence, or omit messages, without a trained selector or additional LLM call. We evaluate MessageSieve on two backbone models and five benchmarks covering expert QA, math reasoning, and code generation. Experiments show that MessageSieve improves the macro-average score over dense Full MAS by 4.10 points on Qwen3-8B and 2.59 points on Gemini 3.1 Flash-Lite, while reducing evaluation tokens by 19–49%.

In this paper, we aim to evaluate multi-agent systems against complex dependencies, including spatial, causal, and temporal constraints. First, we construct a new benchmark, named VillagerBench, within the Minecraft environment. VillagerBench comprises diverse tasks crafted to test various aspects of multi-agent collaboration, from workload distribution to dynamic adaptation and synchronized task execution. Second, we introduce a Directed Acyclic Graph Multi-Agent Framework (VillagerAgent) to resolve complex inter-agent dependencies and enhance collaborative efficiency. This solution incorporates a task decomposer that creates a directed acyclic graph (DAG) for structured task management, an agent controller for task distribution, and a state manager for tracking environmental and agent data.Our empirical evaluation on VillagerBench demonstrates that VillagerAgentoutperforms the existing AgentVerse model, reducing hallucinations and improving task decomposition efficacy. The results underscore VillagerAgent’s potential in advancing multi-agent collaboration, offering a scalable and generalizable solution in dynamic environments. Source code is open-source on GitHub.