Multimodal modeling represents a vital step from modality-agnostic reasoning toward world modeling. While early approaches predominantly rely on late-fusion that assembles encoders and frozen language backbones with output heads, recent efforts have shifted the paradigm toward native multimodal modeling (NMM) with the intrinsic integration of modalities for superior multimodal performance. Despite its potential, the design space of native architectures remains insufficiently defined. In this paper, we present the community with a formalized roadmap for this transition. Specifically, we formally define the architectural nativity, distinguishing mid-fusion and early-fusion from non-native paradigms. We further organize the existing native models through the lens of input-output duality into three categories: (i) Multi-to-Text for cross-modal comprehension with text-only output; (ii) Multi-to-Target for scenario-oriented generation e.g., image, audio and video generation, and (iii) Multi-to-Multi for unified modeling with symmetric input-output. We systematically unpack the end-to-end pipeline from industrial perspectives from: architectural coordination, massive data curation, to full-stack training recipes, inference deployment, and evaluation. Finally, based on our assessment of native multimodal development trends, we provide a forward-looking perspective on its future trajectory.

Rather than merely to retain previously acquired generalization, achieving synergistic improvements between generalization and domain specialization in foundation models remains a significant challenge in both pre-training and post-training. As an alternative, we propose a test-time cross-domain knowledge integration method, Mixture of Multi-domain Agents (MMA), which dynamically combines the outputs of general-purpose and domain-specific models to enhance their performance on complex, domain‐specific tasks. MMA formulates the integration process as a search problem, using Monte Carlo Tree Search (MCTS) to find the path that optimally harmonizes the respective strengths of different models in generalization and domain-specific knowledge. In addition, We design specific action spaces to control the knowledge integration between multiple models, and cross-inspection reward is introduced to fairly score strategies in different domains. Experiments in diverse domains show that MMA can effectively combine the strengths of different models to enhance their performance. For instance, in legal tests, the average performance of all tasks increased from 42.57% to 53.68%. In financial tests, it improved from 56.01% to 62.68%.