Essential differences and unique advantages of the GoLive AI MR model compared to existing AI models (such as ChatGPT/OpenAI, Meta LLaMA, Claude, etc.). Below is the main summary in comparison:

​

1. Deep Integration with the Physical World – True Multimodal Model

GoLive AI MR is based on real-time integration of data from multiple sensors: video, audio, sensor inputs (location, movement, gaze tracking), natural language input, and visual and social environment context.
In contrast, existing models like OpenAI GPT and Meta LLaMA focus mainly on text and images, with limited or no direct connection to the physical sensing environment or real-time data.

2. Context Understanding at the Physical World Model Level

GoLive builds a "Large World Model" (LWM) that understands not only text but also physical processes, spatial environment, and environment structure in real time – where people and objects are, and what is happening around them.
Existing models have limited context understanding, mostly text-based, without direct physical or real-time world perception.

3. Continuous Personalization – Long-Term Memory

GoLive maintains long-term memory storing user preferences, habits, and interactions, allowing personalized information and display adaptation based on history and ongoing behavior.
Standard models often have only immediate or session-limited memory.

4. Value-Based Decision-Making – Deliberation Module

GoLive has a special deliberation module that considers user values and contextual signals, making adaptive decisions—an agent capable of reasoning and value-based decision-making.
Typical models do not incorporate explicit value-based reasoning and follow dialogue continuation based on training data.

5. Real-Time Execution and Edge+Cloud Hybrid Architecture

GoLive combines heavy cloud processing with edge devices equipped with AI tailored for hardware and optics, enabling millisecond real-time response. It can operate even with limited or no cloud connectivity, maintaining privacy and reliability.
Most large models rely primarily on cloud servers and are relatively slow on edge devices.

6. Built-in Privacy and Security

Sensitive data filtering and privacy management happen on the edge device, with full user control over what data is sent or stored.
Typical systems send most data to remote servers.

7. Smart Display System with Personalized Optimization

Every display element (brightness, color, focus, spatial layout, AR overlays) is adapted in real time to the user's preferences and context—not just what to show, but how to show it.
Other systems lack such dynamic and personalized display adaptation.

8. Memory and Self-Reflection Mechanisms

Dedicated modules for self-reflection, learning from experience, and long-term state maintenance improve adaptation, error correction, and user state tracking.
Typical models have very limited internal memory or self-reflective mechanisms.

Summary and Key Conclusions:

• GoLive offers a spatial and embodied AI solution integrating sensors, memory, and value-based decision modules in real time.

• Leading systems from Meta, Microsoft, Apple, or OpenAI have advanced capabilities but are more limited in physical integration, long-term memory, and value-driven decision-making.

• Privacy, real-time performance, and personalization are core to GoLive's unique and sophisticated model, beyond standard language-vision AI systems.