[
    {
        "id": "osp-17855",
        "type": "article-journal",
        "title": "CRT-HMAR: Causal Requirement Tracing-Guided Hierarchical Multi-Agent Regulation for Open-Task-Aware Infrared-Visible Image Fusion",
        "author": [
            {
                "family": "Yang",
                "given": "Zengyi"
            },
            {
                "family": "Yuan",
                "given": "Shuai"
            },
            {
                "family": "Wu",
                "given": "Zhong-Cheng"
            },
            {
                "family": "Cheng",
                "given": "Juan"
            },
            {
                "family": "Li",
                "given": "Huafeng"
            },
            {
                "family": "Liu",
                "given": "Yu"
            }
        ],
        "URL": "https://omanscience.com/en/articles/crt-hmar-causal-requirement-tracing-guided-hierarchical-multi-agent-regulation-for-open-task-aware-infrared-visible-image-fusion",
        "language": "en",
        "issued": {
            "date-parts": [
                [
                    2026
                ]
            ]
        },
        "abstract": "Infrared and visible (IR-VIS) image fusion integrates complementary multimodal information into a single fused image to support downstream vision tasks. However, existing methods are typically tailored to seen tasks within a fixed task set and struggle to generalize to unseen tasks, which restricts their applicability in real-world open-task scenarios. To address this issue, this paper proposes CRT-HMAR, a Causal Requirement Tracing-Guided Hierarchical Multi-Agent Regulation Framework for open-task-aware IR-VIS image fusion. CRT-HMAR introduces a Causal Requirement Tracing Task Localization mechanism, which actively intervenes in key image information and observes task-network response variations to map task-specific semantic preferences into image-level causal requirement maps. Based on these maps, a requirement analysis agent aggregates task-specific requirement knowledge to adaptively guide requirement-customized image fusion. Moreover, CRT-HMAR incorporates History-Analysis Multi-Objective Balancing and Task-Level-Correction Conflict Mitigation mechanisms, jointly constructing a hierarchical regulation chain of \"requirement interpretation - task balancing - conflict mitigation\". Through multiple collaborative agents, CRT-HMAR dynamically regulates key processes including open-task requirement modeling, multi-task balanced optimization, and gradient conflict mitigation. Extensive experiments on open-task scenarios involving five downstream tasks demonstrate that CRT-HMAR significantly improves generalization to unseen tasks while maintaining the performance and balance of seen tasks. Overall, CRT-HMAR shifts IR-VIS image fusion from task-oriented modeling toward requirement-oriented modeling, promoting its extension from closed-task settings to real-world open-task scenarios."
    }
]