[
    {
        "id": "osp-21535",
        "type": "article-journal",
        "title": "Exploring Heterogeneous Model Merging Approach for Complex Knowledge Transfer",
        "author": [
            {
                "family": "Fan",
                "given": "Jiahe"
            },
            {
                "family": "Chen",
                "given": "Si"
            },
            {
                "family": "Hou",
                "given": "Yinghao"
            },
            {
                "family": "Xia",
                "given": "Wenbo"
            },
            {
                "family": "Xu",
                "given": "Ke"
            },
            {
                "family": "Xie",
                "given": "Hong"
            },
            {
                "family": "Chen",
                "given": "Enhong"
            }
        ],
        "URL": "https://omanscience.com/en/articles/exploring-heterogeneous-model-merging-approach-for-complex-knowledge-transfer",
        "language": "en",
        "issued": {
            "date-parts": [
                [
                    2026
                ]
            ]
        },
        "abstract": "Specialized models encode task-oriented behavior, but transferring that behavior to a general language model usually requires training, distillation, or representation alignment. We study whether such ability can instead be transferred directly at the parameter level. We apply two existing training-free heterogeneous merging methods, previously shown to transfer knowledge between general language models, to specialist-to-general transfer, projecting a specialist donor into the recipient's shape and interpolating backbone parameters without gradient updates or semantic alignment. Intersection-Merge (IM) injects a prefix-aligned donor slice matching the recipient shape, while Activate-Prune-Merge (APM) uses forward-pass activation statistics to select which donor dimensions to retain before injection. Across embedding, reranking, reward modeling, and MoE code-specialist transfer, both methods improve the general recipient, showing that simple heterogeneous merging can move capabilities across diverse specialist roles."
    }
]