Recent parameter-efficient fine-tuning (PEFT) methods effectively adapt Vision-Language Models (VLMs) to seen tasks, yet often exhibit limited generalization to unseen novel classes. To better understand this phenomenon, we analyze adaptation dynamics from the perspective of cross-modal mutual information (MI). Our empirical analyses reveal a consistent pattern across existing methods: during the adaptation process, cross-modal MI for novel classes frequently encounters early plateaus or declines, indicating weakened cross-modal semantic dependency. Motivated by this observation, we propose Retrieval-Augmented Dynamic Adaptation (RADA), a retrieval-enhanced adaptation framework that continuously injects rich instance-specific semantic priors through an LLM-enhanced dual-modality memory bank and a lightweight modality-agnostic token generator. By facilitating fine-grained cross-modal alignment while preserving pretrained zero-shot ability, RADA achieves more transferable adaptation across diverse scenarios. Extensive experiments across multiple widely used benchmarks demonstrate that RADA consistently achieves state-of-the-art performance while maintaining strong zero-shot capability. In addition, our empirical analyses show that RADA preserves more stable cross-modal dependency trajectories throughout adaptation, offering further insights into the relationship between adaptation dynamics and generalization in VLMs.

End-to-end manga generation is a structured visual storytelling task that requires story decomposition, recurring character and scene grounding, page layout design, panel rendering, page composition, and lettering. However, existing generative models often perform direct page synthesis, entangling these factors in a single visual output and limiting precise control over layout geometry, visual references, and cross-panel consistency. To address these limitations, we propose MangaFlow, an agentic framework for controllable long-form manga generation that decomposes manga creation into planning, grounding, layout construction, reference-conditioned rendering, composition, and text placement. By treating layout and visual references as explicit intermediate variables, MangaFlow enables both simple text-to-manga generation and more precise user-controlled manga creation. This design exposes layout, visual assets, and lettering as editable intermediate controls for refining panel geometry, references, and text placement. To support long-form consistency, MangaFlow introduces a story section memory that links section descriptions with corresponding character, scene, and object references for reuse across panels. We further present a meta-benchmark for evaluating layout controllability, visual consistency, and generation quality. Experiments show that MangaFlow improves layout adherence and cross-panel consistency over direct generation baselines while supporting flexible human control.