Key Derivation Functions offering better than 2^hlen security?
Note that Krawczyk's paper builds the extract-and-expand paradigm from a notion that he labels computational randomness extractor.  This is a modification of the concept of randomness extractor such that the output of the computational extractor need only be computationally near to a uniform random distribution, while a plain old randomness extractor needs to be unconditionally near to such a distribution.  So an extract-and-expand KDF is a combination of:


A computational extractor, which takes weakly random inputs and "condenses" it into a computationally-strong master key;
A pseudorandom function keyed off the computational extractor's output, which is used to produce expanded pseudorandom outputs.


So a KDF that can extract the maximum entropy that can be extracted from the source key material would replace the composition of these two submodules with a randomness extractor that's capable of producing output with the desired entropy from the source key material.  This is akin to the techniques used in true random number generators to produce full-entropy, uniform outputs from biased or correlated noise sources.  

Randomness extraction is a big topic; searching the web for "randomness extractor" produces some technical introductory material.