VATSA: Video, Audio, Text, Sensory, Action - A Unified Five-Modality Architecture for Human-Level Perception and Action

We present VATSA (Video, Audio, Text, Sensory, Action), a proposed unified architecture
for human-level multimodal AI that integrates five distinct perceptual and actuation streams
within a single coherent framework. While state-of-the-art multimodal models such as GPT-4o
(OpenAI, 2024), Gemini Ultra, and Uni-MoE (Li et al., 2024) span two to four modalities,
no existing system jointly addresses video, audio, text, physiological/IoT sensory data, and
grounded action. Recent survey work on unified multimodal understanding (Yang et al.,
2025) explicitly identifies the absence of sensory integration and closed-loop action as critical
open frontiers.

VATSA addresses these gaps through four architectural principles: (1) a shared latent space
in which all modality encoders project into a common high-dimensional embedding; (2) crossmodal
attention enabling dynamic inter-modality interaction at the representation level; (3) a
temporal coherence layer that synchronises streams with heterogeneous sampling rates; and
(4) a closed-loop action head supporting physical, digital, and communicative outputs.
We present the conceptual architecture, motivating applications in healthcare, regulated
pharmaceutical environments, autonomous systems, and adaptive education, an analysis of
open research questions, and a phased implementation roadmap (2026–2028). This paper
constitutes a timestamped declaration of the architectural hypothesis, providing a foundation
for systematic empirical validation as each modality module is built and published openly.
Benchmarks and experimental results will be incorporated in subsequent revisions.