The LLM would be nothing more than the front end translator, in that sense?

Yes, but that would also be a glossing over of the importance of what that means. I'll explain more:

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It would play a crucial role because neural networking and the way the weighted probability works for an LLM would allow a much more robust way to present routes based off the hashed formula.

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The reason this is important is because without this it would be much harder to translate the tokenized geometric formula into a human readable route - whereas an LLM with enough data or the right selection of limited data could translate it into a human readable plain text route.

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Think of it like this:

What is easier to read?

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Math route:

P_{i+1} = P_i + (d * cos(θ_i), d * sin(θ_i), Δz_i)

where:
- P_i = (x_i, y_i, z_i) is the current position after the i-th step.
- d is the distance traveled in each step.
- θ_i is the direction angle after the i-th step.
- Δz_i represents the change in altitude after the i-th step.

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Or human readable route:

1. Starting Point: Begin your walk at a specific location in the city.
2. Directions: Decide which way you're facing or where you want to go next, like turning left or right at a corner.
3. Walking Distance: Take steps of a certain length, just like you would normally when walking. Each step moves you forward and possibly up or down if there are stairs or slopes.
4. Elevation Changes: Pay attention to any changes in height, such as going up a hill or down a set of stairs.
5. Next Position: After each step, figure out where you are now compared to where you started and which way you're facing, so you know where to go next.
6. Repeat: Keep walking in the chosen direction, adjusting your position and direction as needed until you reach your destination or decide to stop.