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

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1. Verfasser: K V (Kengeri Vijaya Kumar), Vinay Kumar
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Sprache:Englisch
Veröffentlicht: Zenodo 2026
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author K V (Kengeri Vijaya Kumar), Vinay Kumar
author_facet K V (Kengeri Vijaya Kumar), Vinay Kumar
contents <p>We present VATSA (Video, Audio, Text, Sensory, Action), a proposed unified architecture<br>for human-level multimodal AI that integrates five distinct perceptual and actuation streams<br>within a single coherent framework. While state-of-the-art multimodal models such as GPT-4o<br>(OpenAI, 2024), Gemini Ultra, and Uni-MoE (Li et al., 2024) span two to four modalities,<br>no existing system jointly addresses video, audio, text, physiological/IoT sensory data, and<br>grounded action. Recent survey work on unified multimodal understanding (Yang et al.,<br>2025) explicitly identifies the absence of sensory integration and closed-loop action as critical<br>open frontiers.</p> <p><br>VATSA addresses these gaps through four architectural principles: (1) a shared latent space<br>in which all modality encoders project into a common high-dimensional embedding; (2) crossmodal<br>attention enabling dynamic inter-modality interaction at the representation level; (3) a<br>temporal coherence layer that synchronises streams with heterogeneous sampling rates; and<br>(4) a closed-loop action head supporting physical, digital, and communicative outputs.<br>We present the conceptual architecture, motivating applications in healthcare, regulated<br>pharmaceutical environments, autonomous systems, and adaptive education, an analysis of<br>open research questions, and a phased implementation roadmap (2026–2028). This paper<br>constitutes a timestamped declaration of the architectural hypothesis, providing a foundation<br>for systematic empirical validation as each modality module is built and published openly.<br>Benchmarks and experimental results will be incorporated in subsequent revisions.</p>
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spellingShingle VATSA: Video, Audio, Text, Sensory, Action - A Unified Five-Modality Architecture for Human-Level Perception and Action
K V (Kengeri Vijaya Kumar), Vinay Kumar
Artificial Intelligence
multimodal AI
VATSA
video audio text sensory action
<p>We present VATSA (Video, Audio, Text, Sensory, Action), a proposed unified architecture<br>for human-level multimodal AI that integrates five distinct perceptual and actuation streams<br>within a single coherent framework. While state-of-the-art multimodal models such as GPT-4o<br>(OpenAI, 2024), Gemini Ultra, and Uni-MoE (Li et al., 2024) span two to four modalities,<br>no existing system jointly addresses video, audio, text, physiological/IoT sensory data, and<br>grounded action. Recent survey work on unified multimodal understanding (Yang et al.,<br>2025) explicitly identifies the absence of sensory integration and closed-loop action as critical<br>open frontiers.</p> <p><br>VATSA addresses these gaps through four architectural principles: (1) a shared latent space<br>in which all modality encoders project into a common high-dimensional embedding; (2) crossmodal<br>attention enabling dynamic inter-modality interaction at the representation level; (3) a<br>temporal coherence layer that synchronises streams with heterogeneous sampling rates; and<br>(4) a closed-loop action head supporting physical, digital, and communicative outputs.<br>We present the conceptual architecture, motivating applications in healthcare, regulated<br>pharmaceutical environments, autonomous systems, and adaptive education, an analysis of<br>open research questions, and a phased implementation roadmap (2026–2028). This paper<br>constitutes a timestamped declaration of the architectural hypothesis, providing a foundation<br>for systematic empirical validation as each modality module is built and published openly.<br>Benchmarks and experimental results will be incorporated in subsequent revisions.</p>
title VATSA: Video, Audio, Text, Sensory, Action - A Unified Five-Modality Architecture for Human-Level Perception and Action
topic Artificial Intelligence
multimodal AI
VATSA
video audio text sensory action
url https://doi.org/10.5281/zenodo.19714353