We are building foundation models that seamlessly integrate text, vision, audio, and video understanding in a unified architecture. Our research focuses on achieving true omni-modal reasoning with minimal latency, enabling real-time interactions across all modalities.
Our omni-modal models employ a unified transformer architecture that processes all modalities through a shared representation space. This approach enables emergent cross-modal reasoning capabilities that surpass modality-specific models.
By learning joint embeddings across text, images, audio, and video, our models can perform zero-shot cross-modal tasks and demonstrate sophisticated understanding of relationships between different sensory inputs.
We have developed specialized techniques for reducing inference latency in multi-modal scenarios. Through optimized attention mechanisms, efficient token representations, and hardware-aware design, our models achieve near-instantaneous responses.
Our streaming architecture allows for real-time processing of audio and video inputs without waiting for complete sequences, enabling truly interactive multi-modal experiences.
Unlike conventional approaches that rely heavily on Vision Transformers (ViT), our research explores alternative architectures for visual understanding. We investigate efficient alternatives that maintain or exceed ViT performance while reducing computational requirements.
This approach enables more flexible model designs and opens new possibilities for edge deployment and real-time applications.
We have developed novel pre-training strategies that efficiently scale to billions of parameters while maintaining training stability. Our methods combine self-supervised learning across modalities with carefully curated synthetic data.
Through distributed training across NVIDIA GPUs and Google TPUs, we achieve efficient utilization of heterogeneous hardware platforms.