Modality-Spec. Adapters

Modality-Specific Adapters extend a frozen multi-modal model's capabilities to new modalities or improve its performance on existing ones by inserting lightweight PEFT-style (Parametric Update) modules (Modular) within each modality's dedicated processing pathway, without modifying the shared model backbone or cross-modal bridge ^[191]. In contrast to Cross-Modal Alignment, which optimizes the interface between representations to enable semantic integration, Modality-Specific Adapters refine within-modality processing, making them complementary adaptation layers rather than alternatives. Compared to standard PEFT, which applies within a single modality's processing pathway, Modality-Specific Adapters are architecturally instantiated per modality, creating a collection of independently versioned parametric modules whose composition defines the system's active multi-modal capability profile. Their classification strictly at MLLM reflects the architectural prerequisite: independent per-modality adapter injection inherently requires a multi-stream architecture featuring distinct, unimodal computational pathways. The Modular ensures that modality-specific adaptation artifacts can be independently versioned, tested, and rolled back. From a system engineering and validation standpoint, this structural isolation profoundly simplifies configuration management: it mathematically guarantees that high-frequency updates to one modality's adapter (e.g., integrating a new visual encoding standard) can be executed and validated completely independent of the other (e.g., text generation), systematically bypassing the computationally expensive need for end-to-end, multi-stream regression testing.