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.

Large language model (LLM) evaluation is increasingly confounded by *variant contamination*: training corpora contain semantically equivalent but lexically or syntactically altered test items. These variants can evade detectors based on sampling consistency or perplexity, inflating benchmark scores through memorization rather than reasoning. We formalize this problem and introduce **DVD** (**D**etection via **V**ariance of generation **D**istribution), a detector requiring only the test item, without external reference corpora or paired contaminated/clean sets. DVD models the local output distribution through repeated temperature sampling. Our key insight is that contaminated items alternate between *memory-adherence* and *perturbation-drift* states, producing unusually high variance in the synthetic difficulty of low-probability tokens, whereas uncontaminated items remain in drift with comparatively stable difficulty values. We construct the first dedicated variant-contamination benchmark across Omni-MATH and SuperGPQA by generating and filtering semantically equivalent variants, and simulate contamination by fine-tuning Qwen2.5 and Llama3.1 models of different scales. In most evaluated settings across datasets, model scales, architectures, and training configurations, **DVD** outperforms perplexity-based, Min-*k*%++, edit-distance (CDD), and embedding-similarity baselines, while showing strong robustness to hyperparameters. These results support generation-distribution variance as a principled and practical signal for detecting variant contamination, helping improve the reliability of LLM evaluation.