[
    {
        "id": "osp-15561",
        "type": "article-journal",
        "title": "CoDe-LoRA: Mitigating the Orthogonality Dilemma in Continual Learning of LLMs via Knowledge Consolidation and Decoupling",
        "author": [
            {
                "family": "Liu",
                "given": "Maoqi"
            },
            {
                "family": "Fang",
                "given": "Quan"
            },
            {
                "family": "He",
                "given": "Yufei"
            }
        ],
        "URL": "https://omanscience.com/en/articles/code-lora-mitigating-the-orthogonality-dilemma-in-continual-learning-of-llms-via-knowledge-consolidation-and-decoupling",
        "language": "en",
        "issued": {
            "date-parts": [
                [
                    2026
                ]
            ]
        },
        "abstract": "Continual learning (CL) is essential for Large Language Models (LLMs) to sequentially adapt to evolving tasks. To mitigate catastrophic forgetting, recent advances implement low-rank adaptation with orthogonal projections (e.g., O-LoRA) to isolate task parameters. However, we reveal that such strict geometric constraints trigger an \"Orthogonality Dilemma\": rigid parameter isolation impedes the transfer and accumulation of shared representations across semantically related tasks. In this work, we propose a new replay-free method, called Consolidation and Decoupling LoRA (CoDe-LoRA), for CL of LLMs. CoDe-LoRA disentangles the learning process into Consolidating Universal Knowledge and Decoupling Task-Specific Knowledge. To achieve this, CoDe-LoRA leverages an adaptive null space projection mechanism and semantic routing to balance knowledge accumulation with task-specific adaptation. Experimental results across four backbones and three CL benchmarks show that CoDe-LoRA achieves the best average accuracy. Our code is available at https://github.com/Estrellajer/CoDe-LoRA."
    }
]