Native Vision-Language Models

• Native Vision-Language Models are unified architectures that jointly map images and text into a shared semantic space, eliminating the need for separate encoders.
• They employ modality-aware mechanisms like Native-RoPE to flexibly encode spatial sequences, facilitating robust bidirectional cross-modal interaction.
• These models offer efficient end-to-end training with reduced misalignment issues and scalable performance, as demonstrated by implementations like the NEO model family.

Native Vision-LLMs (VLMs) are end-to-end architectures that align images and natural language within a unified semantic space, directly integrating visual and linguistic representations without the modular separation of traditional systems. By constructing models from first principles—eschewing the division between pretrained vision encoders and LLMs—native VLMs aim to deeply fuse perception and reasoning at all layers, support bidirectional cross-modal interaction, and enable scalable multimodal learning pipelines. These models are designed to resolve intrinsic limitations of standard modular VLMs, such as misalignment between pixel and word features, high training complexity, and inflexible adaptation across tasks (Diao et al., 16 Oct 2025).

1. Native vs. Modular VLM Architectures

Traditional modular VLMs combine a separately pretrained visual encoder (e.g., ViT, ResNet) and a LLM via connecting adapters or cross-attention modules, resulting in a two-stage or multi-stage pipeline. In contrast, native VLMs are monolithic neural architectures jointly trained from pixels to words, integrating both visual and linguistic competencies from the scratch or with minimal component separation. This end-to-end unification eliminates the need for post hoc alignment, dedicated adapter layers, or inflexible visual positional encodings, and aims to encode both images and text in a shared semantic embedding space through a single, densely-connected Transformer backbone (Diao et al., 16 Oct 2025).

Key architectural differences:

Aspect	Modular VLMs	Native VLMs
Visual encoder	Fixed, pretrained	Learned jointly from scratch
Alignment	Through adapters/cross-attn	Intrinsic in core layers
Positional encoding	Predefined, modality-specific	Unified (Native-RoPE, flexible)
Training pipeline	Multi-stage	End-to-end

Native VLMs are also characterized by modality-aware design features. For instance, they employ disentangled rotary position embeddings (Native-RoPE) that assign different base frequencies and channel allocations for temporal (T), height (H), and width (W) dimensions, supporting fine-grained spatial–sequential encoding with minimal cross-modal interference (Diao et al., 16 Oct 2025). Cross-modal attention mechanisms are crafted to handle both bidirectional spatial dependencies (for images) and autoregressive causal flows (for text) within shared Transformer blocks.

2. Guiding Principles and Native Primitives

The primary design tenets underlying native VLMs are:

1. Unified Representation Alignment: Simultaneously learn to map pixel and word representations into a common semantic space throughout all model layers.
2. Early Cross-Modal Fusion: Integrate vision and language at the earliest stages to reduce inductive bias and training overhead characteristic of multi-stage modular methods.
3. Modality-Aware Mechanisms: Disentangle spatial and sequential indices through flexible position encoding, and design attention mechanisms that natively support dense spatial reasoning and causal token generation.
4. End-to-End Scalability: Support efficient scaling with parameter size and training data, fostering reusability and extensibility across vision-language benchmarks and applications (Diao et al., 16 Oct 2025).

To realize these principles, the NEO model family is introduced as a reference implementation. The architecture begins with lightweight patch (for images) and token (for words) embedding layers, both of which feed into a dense decoder-only Transformer built out of native VLM primitives. Early blocks (pre-Buffer) emphasize spatial–temporal alignment, while later blocks (post-LLM) inherit autoregressive reasoning capabilities from LLM initialization. This division is gradually dissolved during mid-training and fine-tuning, yielding a homogeneous network where bidirectional (spatial) and causal (sequential) interactions co-exist (Diao et al., 16 Oct 2025).

The Native-RoPE mechanism is formalized as:

$$\Theta_T = \left\{ \beta^{-2k/d_T} \mid k \in [0, d/2)\right\},\quad \Theta_H = \left\{ \beta^{-4i/d_H} \mid i \in [0, d/4)\right\},\quad \Theta_W = \left\{ \beta^{-4j/d_W} \mid j \in [0, d/4)\right\}$$

Here, $\beta$ is the frequency base, $d_T$, $d_H$, $d_W$ are dimension splits, and the exponents decouple temporal (T), row (H), and column (W) positional signals.

3. Overcoming Foundational Constraints

A central challenge for native VLMs is the inherent misalignment between visual and linguistic modalities when attempting to fuse separately pretrained modules. Modular systems often suffer from rigid inductive biases, non-interoperable positional encodings, and cumbersome multi-stage pipelines, resulting in inefficiencies and brittle transfer across domains (Diao et al., 16 Oct 2025).

Native VLMs address these barriers through:

• Intrinsic cross-modal fusion: By embedding both pixels and words at all layers, the model robustly co-learns correspondence and high-level reasoning.
• Separation and later unification: Temporarily partitioning the Transformer into pre-Buffer and post-LLM components during early training allows vision-focused updates to propagate without disrupting language priors, before gradually integrating these into a single stack.
• Component reusability: Recurrent use of native primitives (e.g., cross-modal attention, flexible RoPE) offers a modular ecosystem where improvements to foundational blocks propagate across architectures, reducing total system complexity.

These strategies jointly yield competitive performance with less data and reduced computational expense relative to multi-stage modular counterparts; for example, NEO achieves strong results rivaling modular VLMs using only 345M image–text pairs in the pretraining phase (Diao et al., 16 Oct 2025).

4. Cross-Modal Encoding and Unified Reasoning

Native VLMs are designed to natively encode, align, and reason over images and text through deeply unified representations:

• Shared attention: Within each multi-head native attention block, image tokens interact fully bidirectionally to capture spatial relationships, while text tokens interact causally to preserve language generation order. This supports both spatial (region-to-region) and sequential (token-to-token) dependencies.
• Unified positional indices: Image tokens are assigned multi-dimensional indices (T, H, W), enabling accurate spatio-temporal alignment, especially in video or multi-frame scenarios; text tokens remain single-indexed.
• Layerwise integration: Cross-modal fusion is present throughout the model stack rather than being relegated to adapters, resulting in higher cross-modal interaction granularity and fewer alignment bottlenecks.

Such properties enable native VLMs to perform fine-grained semantic matching (e.g., phrase grounding), compositional reasoning, and multi-turn dialogue within a single, scalable Transformer lattice.

5. Scalability, Reproducibility, and Ecosystem

A key motivation for native VLMs is to democratize and accelerate the field by providing scalable, reproducible building blocks and reusable training assets (Diao et al., 16 Oct 2025):

• Parameter and data scaling: NEO and similar models demonstrate competitive accuracy at the 2.2B scale, with design headroom for efficient scaling up to 8B+ parameters without necessitating redesign or excessive overhead.
• Reusable components: Primitives such as the pre-Buffer block and flexible cross-modal attention modules are explicitly designed for transferability, accelerating experimentation and fine-tuning.
• Cost-effective training: Effective pixel–word alignment achieved with modest datasets reduces the computational barrier to entry, supporting a breadth of research groups and applications.

This extensibility is intended to foster a rich ecosystem in which principled adjustments to architectural primitives or training paradigms can be rapidly evaluated and propagated across the native VLM landscape.

6. Comparative Capabilities and Implications

Empirically, native VLMs match or surpass state-of-the-art modular systems on benchmarks covering open-ended VQA, localization, visual reasoning, and semantic retrieval. Key findings include:

• Alignment and reasoning: End-to-end fusion naturally aligns spatial and linguistic features at all layers, minimizing the need for ad hoc adapters.
• Efficiency and robustness: Fewer training stages, lower parameter count, and dense integration result in improved efficiency and robustness to varied real-world tasks.
• Transfer and extensibility: Native primitives generalize across tasks, facilitating fine-tuning and transfer to new domains without wholesale re-engineering.

These advantages position native VLMs as not only technical achievements in cross-modal modeling but as strategic foundations for the next generation of scalable, integrated AI systems.

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In summary, native vision–LLMs are monolithic, end-to-end architectures that collapse the distinction between vision and language components through unified primitives, deeply fused cross-modal attention, and flexible position encoding schemes. By solving foundational misalignment and training complexity found in modular systems, native VLMs enable reproducible, scalable, and robust multimodal reasoning, with emerging model families such as NEO exemplifying best practices and providing reusable components tailored for cost-effective, extensible research (Diao et al., 16 Oct 2025).