Ai No Data – Zero-Training AI™ Interactive Demos

LLM Token Governor (Hallucination Eliminator)

This demo shows how a Zero-Training AI™ governor evaluates and selects lower-risk statements from a typical LLM response using a mathematical decision filter.

Domain Mode

Energy Weights

α (assertion / absolutism penalty): 4

λ (speculation / uncertainty penalty): 2

Demo Overview

Below is an example of a response an LLM might produce. The text is fixed so you can clearly see how the governor evaluates the same content under different energy weights and domain settings.

How This Demo Works

The governor does not know the question, the topic, or the meaning of the content. It evaluates only structural language risk and selects the statement with the lowest computed energy under the current configuration.

Example LLM Response (Read-Only)

Inflation happens when prices rise over time and money buys less than it used to. Inflation is always caused by governments printing too much money. One common cause is when demand for goods and services grows faster than supply. Inflation might occur when businesses face higher costs for labor, energy, or materials and pass those costs on to consumers. Supply disruptions, such as wars or natural disasters, can reduce available goods and push prices higher. In some cases, inflation seems to happen when people expect prices to rise and adjust their behavior accordingly.

Governor Output

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Zero-Training AI™ is a proprietary decision-optimization technology demonstrated here for informational and educational purposes only. The demonstrations on this website are simplified visual and interactive examples intended to illustrate conceptual behavior, not operational systems.

These demos do not represent physical simulations, real-world control systems, autonomous vehicles, medical devices, financial instruments, or deployed safety-critical systems. Any visual motion, scaling, or behavior shown is a mathematical or illustrative abstraction and should not be interpreted as modeling real energy, force, thrust, risk, or physical dynamics.

Zero-Training AI™ does not rely on training data, datasets, machine learning models, or statistical inference. Outputs shown are generated through deterministic mathematical evaluation and decision-selection logic applied at runtime.

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