In the wikipedia page for the NP-intermediate complexity class, the following observation is made:
Under the assumption that P ≠ NP, Ladner explicitly constructs a problem in NPI, although this problem is artificial and otherwise uninteresting. It is an open question whether any "natural" problem has the same property [...]
In the '80s, a work by Galperin and Widgerson was dedicated to problems on graphs with succinct circuit representations. Intuitively, a graph has a succint circuit representation if there is a polylogarithmically sized boolean circuit which takes two (padded) integers representing node numbers as input, and outputs 1 if there is an edge between those two nodes and 0 otherwise. Many NP-complete predicates of graphs were shown to be NEXP-complete when the graph is provided in its succinct form.
Now consider any particular NP-complete problem among these. The above fact can be because, aside from the input having become exponentially smaller, the following informal statement must be true:
Call $\sigma$ the subset of instances of the original NP-complete problem that are representable by a succinct circuit. $\sigma$ turns out to not be dramatically easier to decide than its parent set; that is, $\sigma$ is not susceptible to some specialized, polynomial method leveraging some particular structural peculiarity common to all its members.
In fact, if there was a polynomial time algorithm A that, when applied to members of $\sigma$ (it doesn't even matter what A does with the other instances!), would correctly decide them, you could simply "decompress" the graph and then use A to decide the instance. Both of these steps take deterministic time exponential in the succinct input length. But the succinct problem is NEXP complete, so this could happen only if EXP=NEXP. This provides evidence that $\sigma$ itself is not in P unless EXP=NEXP. To decide $\sigma$, you would need to establish that the instance has a small circuit, then solve it. The first part is the MCSP problem, which for all we know could be in P. But the second part would require A, that as mentioned exists only if EXP=NEXP.
I wish to observe that $\sigma$ is sparse, by the simple fact that, asymptotically, as graph size increases linearly only logarithmically many small circuits can be proposed, and therefore only logarithmically many instances selected.
In Papadimitriou and Yannakakis this topic is tackled. They observe:
[...] Also, our result can be interpreted as an elaboration on the work of Hartmanis, Sewelson, and Immerman (1983). They showed that there exist sparse sets in NP-P iff NEXP ≠ EXP. We have exhibited, essentially for any known NP-complete set, a sparse subset that is the most likely to be in NP - P exactly if NEXP ≠ EXP (namely, the members of the language that are succinctly representable by the scheme suggested in the proof).
NP-P includes both NP-complete and NP-intermediate. But, assuming P ≠ NP, $\sigma$ cannot be NP-complete by virtue of Mahaney's theorem, and therefore it must be NP-intermediate. Thus, $\sigma$ is an arguably "natural" example of a problem strictly in NPI (provided naturally that EXP≠NEXP), in contrast with the wikipedia article.
Did I make some mistake in my reasoning? Why do Papadimitriou and Yannakakis use NP-P, and why do they say that if EXP≠NEXP $\sigma$ is only "most likely" to be in NP-P, and not certainly in NP-P?
If $\sigma$ is, in fact, in NPI if NEXP ≠ EXP, why isn't this mentioned in NP-intermediate discussions? Could this really just have been overlooked?