Native Multimodal AI Embeds Modalities in Shared Vector Space
Native multimodal AI tokenizes text, images, and video into a shared vector space for joint reasoning, outperforming feature fusion by preserving details and enabling any-to-any generation.
Feature-Level Fusion Loses Detail in Modular Pipelines
Early multimodal systems combine separate models, like a text-only LLM with a vision encoder (e.g., CLIP-based), to handle inputs beyond text, such as images alongside prompts. The vision encoder extracts a numerical feature vector from an image—essentially a summarized array—and injects it into the LLM's processing stream. This works for enterprise tasks because it's cheaper and allows swapping components, but it discards raw signal: the LLM processes only compressed features, not the original image. For example, querying a tiny icon in a phone screenshot loses precision since the encoder compresses before knowing the question, risking overlooked details.
Shared Vector Spaces Enable Joint Reasoning Across Modalities
Native multimodal AI overcomes this by embedding all inputs—text, images, audio, LiDAR, thermal—into a single high-dimensional shared vector space. Text tokenizes into word/subword vectors (e.g., 'cat' as a point). Images divide into patches (e.g., 16x16 pixels), each embedded as a vector near semantically similar text like 'cat' for a cat image. Audio and others chunk similarly. The model reasons over everything simultaneously, attending to relevant parts based on the full context. This beats fusion: no pre-compression loss, direct cross-modal alignment (image patches stay close to descriptive text), and precise focus, like spotting a corner icon while processing a text query about a phone issue.
Spatio-Temporal Tokens Capture Video Motion for Any-to-Any Output
Video adds a time dimension, which early systems mishandle by sampling frames for static processing—missing actions like picking up vs. setting down a water bottle from a single frame. Native models use 3D spatio-temporal patches (e.g., cubes spanning 8 frames), baking motion directly into tokens rather than inferring from separate images. This preserves sequence. Outputs extend to any-to-any generation: input any modality mix (text + image), generate any mix (text steps + video clip of tying a tie), all coherent in the shared space. Result: models that ingest text/images/video and respond across modalities without translation overhead.