Adaptive Multimodal Intelligence: Integrating Vision, Language, and Action for Next-Generation AI Systems
DOI:
https://doi.org/10.15662/IJARCST.2023.0606017Keywords:
Adaptive multimodal intelligence, vision-language-action integration, multimodal fusion, embodied AI, reinforcement learning, cognitive grounding, continual learning, autonomous systems, next-generation AI.Abstract
The rapid evolution of artificial intelligence has highlighted a critical need for systems capable of seamlessly integrating vision, language, and action—modalities essential for creating human-like, context-aware, and adaptable intelligent agents. Traditional unimodal or loosely coupled multimodal architectures remain limited in their ability to reason holistically, learn continuously, and act autonomously in dynamic environments. This research presents Adaptive Multimodal Intelligence (AMI), a next-generation framework designed to unify perception, cognition, and decision-making into a cohesive system. AMI introduces a transformative approach that tightly integrates visual understanding, natural language comprehension, and embodied action planning, enabling AI systems to engage with the world in a manner analogous to human cognitive processes.
At the core of AMI lies a Multimodal Fusion Engine that dynamically aligns vision-language-action representations using cross-attention, hierarchical context encoding, and shared latent space modeling. This fusion mechanism allows the system to form richer semantic associations and to interpret complex situations involving spatial, temporal, and linguistic dependencies. To support adaptive behavior, the framework incorporates Reinforcement Learning with Multimodal Feedback (RL-MF), allowing the agent to continuously refine its action policies based on visual cues, linguistic instructions, and environment interactions. This bidirectional learning loop enhances the system’s ability to reason, generalize, and perform tasks in unstructured settings.
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