How many samples are needed to reconstruct a path? Consider an input set of vertices $V$ and vertices $s,t\in V$.
The goal is to learn some unknown shortest path from $s$ to $t$; the set of edges of the graph is hidden at first and there may be multiple shortest paths.

At each iteration, each vertex is randomly colored using $0$ or $1$.
We then get a single bit, that is $1$ with a probability proportional to the number of $1$-vertices along the path.
That is, if $6$ of the vertices along the path were colored with $1$ and $4$ were zero, then the bit will be $1$ w.p. 3/5.

Importantly, we get to know the coloring of every iteration, and the goal path does not change.


  How many samples do we need until we can compute the path with, say, probability 2/3?




A very simplistic approach would be to eliminate all monochromatic paths whose color is different than the given bit (e.g., a $1$-path can be eliminated if we get a $0$-bit). This seems to give a very weak bound.
Here’s a simple idea: try a certain number $s$ of samples, and for each vertex, record how many times its colour agreed with the provided bit.

For vertices outside the path, they agree with probability $1/2$ on each iteration, while for vertices on the path, the probability is $1/2+1/2k$, where $k$ is the number of vertices on the path.

Thus, if $s\approx k^2\log n$, the Chernoff–Hoeffding inequality ensures that with probability at least $2/3$, all vertices outside the path agree for less than $s(1/2+1/4k)$ samples, and all vertices on the path agree for more than $s(1/2+1/4k)$ samples, which lets you pinpoint the path. If you don’t know $k$ in advance, you can just bound it by $n$.