[
    {
        "id": "osp-20601",
        "type": "article-journal",
        "title": "V-Engram: Trigger-Indexed External Memory for Modular Text-to-Image Personalization",
        "author": [
            {
                "family": "He",
                "given": "Haoran"
            },
            {
                "family": "Cai",
                "given": "Runyuan"
            },
            {
                "family": "Wang",
                "given": "Yiming"
            },
            {
                "family": "Yu",
                "given": "Lin"
            },
            {
                "family": "Zeng",
                "given": "Xiaodong"
            }
        ],
        "URL": "https://omanscience.com/ar/articles/v-engram-trigger-indexed-external-memory-for-modular-text-to-image-personalization",
        "language": "en",
        "issued": {
            "date-parts": [
                [
                    2026
                ]
            ]
        },
        "abstract": "Pretrained text-to-image models contain broad visual knowledge, yet they cannot reliably acquire or refine a specific visual identity from only a few references while preserving compositional control. Token-embedding methods are compact but often underfit identity, whereas adapter-based methods improve fidelity through persistent weight updates that can be costly to store and interfere when concepts are composed. We introduce V-Engram, a trigger-indexed external memory mechanism for Stable Diffusion 3.5. Each concept is assigned an explicit trigger that retrieves concept-specific memory, whose gated directions enter frozen text-encoder and MMDiT context states as relative residuals. Separating this memory from backbone adaptation enables prompt-selective and multi-concept access without merging model updates. Experiments show that V-Engram broadly matches DreamBooth-LoRA in overall subject fidelity while showing advantages in settings such as contextual subject preservation. Prompt-matched loading retrieves only matched entries, reducing most additional adaptation-state loading for a single-concept query. Qualitative results further demonstrate paired-trigger composition and same-class separation, while prompts without registered entries retain the frozen model's base behavior. Together, these results establish trigger-indexed memory as a modular interface for adding targeted visual evidence without rewriting the generator."
    }
]