[
    {
        "id": "osp-19691",
        "type": "article-journal",
        "title": "Inherit-MAS: Test-Time Evolution of Multi-Agent Systems through Workflow and Execution Inheritance",
        "author": [
            {
                "family": "Wei",
                "given": "Songtao"
            },
            {
                "family": "Li",
                "given": "Yi"
            },
            {
                "family": "Guo",
                "given": "Zhichun"
            },
            {
                "family": "Li",
                "given": "Bingzhe"
            }
        ],
        "URL": "https://omanscience.com/ar/articles/inherit-mas-test-time-evolution-of-multi-agent-systems-through-workflow-and-execution-inheritance",
        "language": "en",
        "issued": {
            "date-parts": [
                [
                    2026
                ]
            ]
        },
        "abstract": "Multi-agent systems (MAS) built from large language models coordinate specialized agents to tackle complex tasks, but effective workflows are difficult to design in advance. Test-time evolution refines workflows using execution feedback, yet broad revisions can disturb useful components, while re-executing unchanged requests can incur redundant computation. Inspired by the interplay of inheritance and selection in biological evolution, we introduce Inherit-MAS, which makes inheritance explicit at the workflow and execution levels. A meta-model first synthesizes a workflow of worker agents with declared roles, communication inputs, and tool permissions, and a separately prompted judge scores each executed candidate and diagnoses its deficiencies. In ordinary refinement rounds, \\emph{workflow inheritance} starts from the latest completed candidate, may discard removable nodes judged unhelpful, and applies a validated edit to address the diagnosed deficiency. When the new candidate executes, \\emph{execution inheritance} inherits eligible stored results only if the complete resolved request and execution context match, avoiding redundant model and tool calls. With GPT-4o-mini workers, Inherit-MAS achieves 55.4\\% completion on WorkBench and 49.7\\% joint F1 on HotpotQA FullWiki, outperforming EvoAgent, EvoMAS, and TacoMAS. With Qwen3-32B workers, it also exceeds these evolving-MAS baselines on both benchmarks. Compared with rerunning the same controller with execution inheritance disabled, execution inheritance reduces worker-token usage by 29.1\\% on WorkBench and 34.6\\% on HotpotQA, and total token usage by 5.3\\% and 18.1\\%."
    }
]