Large Language Models (LLMs) like ChatGPT have revolutionized AI-powered reasoning, enabling applications across diverse domains. However, their internal reasoning processes often remain opaque, leaving users in the dark about how long the model will take to arrive at an answer.

UNMET NEED
Currently, users interact with reasoning models without any indication of the internal progress or how much longer the model will need to think. This leads to frustration, inefficient workflows, and the risk of abandonment or misjudged expectations. There is a pressing need for real-time insight into the model’s reasoning process, enabling users to monitor, gauge, and influence response generation dynamically.

OUR SOLUTION
Our technology directly tackles the real-world discomfort users experience when waiting for AI responses. By providing visibility and control over the reasoning process, it ensures users are never left guessing how long to wait—and can intervene to optimize their interaction in real time.
We introduce a novel method to visualize and control the reasoning process of LLMs:
• Internal Progress Encoding: Demonstrates that models inherently encode their reasoning progress through internal signals called “progress vectors.”
• Interactive Progress Bar: Visualizes the model’s internal planning, providing users with a transparent, real-time “loading bar” of the reasoning process during inference.
• Overclocking Technique: Allows external intervention to accelerate the reasoning, reducing unnecessary steps and preventing overthinking, thus increasing efficiency and response accuracy.
This approach bridges the gap between the model’s internal processes and user visibility, transforming opaque AI reasoning into an interpretable and controllable interaction.

INTELLECTUAL PROPERTY
Provisional patent application