DGTE: Disagreement-Gated Test-Time Evolution of Roles and Topologies in Multi-Agent Systems

Large language model-driven multi-agent systems (MAS) use role specialization and communication to solve complex tasks, yet their test-time evolution typically relies on validation performance, task rewards, or executable feedback. For new tasks where task labels or trusted correctness scores are unavailable during generation, existing methods lack a reliable internal signal for deciding whether to continue evolving and how to modify the system. We therefore propose Disagreement-Gated Test-Time Evolution of Roles and Topologies (DGTE). For each task, DGTE starts from a frozen base system, runs multiple independent solver groups, and separately measures semantic disagreement among complete answers and operational disagreement caused by incomplete outputs. Their maximum forms a disagreement gate that determines whether to stop early, allocate another evolution round, or select a historical system state when the budget is exhausted. Execution traces, tool outputs, self-tests, and failure diagnostics guide bounded modifications to prompts, roles, helper agents, and communication topology. We validate DGTE on four benchmarks, including Humanity's Last Exam-Math, DeepResearchBench, BrowseComp-Plus, and Plancraft, and achieve SOTA performance on multiple benchmarks. The results show that DGTE achieves competitive performance across diverse task types; further analysis indicates that disagreement improves process completion, allocates additional computation, and helps repair incomplete outputs. DGTE has a modular design that can be seamlessly integrated into existing multi-agent methods without retraining the base model, improving system performance and process reliability with controlled overhead.