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The paper introduces Inherit-MAS, a test-time evolution framework for LLM multi-agent systems that makes workflow and execution inheritance explicit, using a meta-model to synthesize workflows and a judge to refine them. With GPT-4o-mini and Qwen3-32B workers, it outperforms EvoAgent, EvoMAS, and TacoMAS on WorkBench and HotpotQA while cutting worker-token usage by up to 34.6%.
FlowEvo is a training-free framework that enables large language model agents to co-evolve reusable skills and workflows at inference time, achieving state-of-the-art accuracy and efficiency across benchmarks like ALFWorld, HumanEval, and GSM8K.