Let's take the wiki-speedrun diffusion approach that is easier to understand.
At query time : You embed the input prompt to a vector.
You start from the wikipedia home page. You use your diffusion model network( that takes as input the current page you are on, and the input prompt vector), and you predict which link to follow (or whether or not you should stop because you have arrived). That takes you to a new current page, and you use your diffusion network again to pick the next link to follow. After doing this n (~20 times?), you have arrived on the relevant page.
Concerning the signals you can use : You have a lot of design freedom. You can basically add any information that you can embed, to pass it as input to the diffusion network. One such signals can be for example a context vector that represent your previous queries.
In a similar way during the organization of the links space, you have lot of freedom to define what is a relevant query. You can score it semantically (if you don't have a lot of real user signal), or if you have already plenty of users that have made plenty of queries, you learn the mapping with the pair (successful query of the user, link).
Let's take the wiki-speedrun diffusion approach that is easier to understand.
At query time : You embed the input prompt to a vector.
You start from the wikipedia home page. You use your diffusion model network( that takes as input the current page you are on, and the input prompt vector), and you predict which link to follow (or whether or not you should stop because you have arrived). That takes you to a new current page, and you use your diffusion network again to pick the next link to follow. After doing this n (~20 times?), you have arrived on the relevant page.
Concerning the signals you can use : You have a lot of design freedom. You can basically add any information that you can embed, to pass it as input to the diffusion network. One such signals can be for example a context vector that represent your previous queries.
In a similar way during the organization of the links space, you have lot of freedom to define what is a relevant query. You can score it semantically (if you don't have a lot of real user signal), or if you have already plenty of users that have made plenty of queries, you learn the mapping with the pair (successful query of the user, link).