🚀 go-pugleaf

I've been using Standard ML for a few years, and I've become familiar
with its type system. During this time I've come to apprieciate some
apparent deficiencies in its type system, and had a few ideas as to how
these can be overcome.

Now, I appreciete that Standard ML is not the worlds most recent functional
language, and so for that and other reasons, it wouldn't surprise me if
the ideas that I have have been conceived of by others before me.

I would be pleased to know of any papers, especially 'foundational'
ones, that document significant extensions or improvements to the model
for types and type inference used by Standard ML and similar languages.

In the unlikely event that my ideas are significantly new, suppose I'll
also need to go about setting them out formally and showing that they
are not fatally flawed. Thus I would also like to know of any papers
that would provide some explanation for me on how to do this, since I
am no expert on type theory (I do understand the mechanisms of type
inference however i.e. I can and have come up with algorithms which
certainly seem to work, but I don't think that is sufficient these days).

If you know of papers (or even books) in either category, please tell
me. I do read this newsgroup, but sending me e-mail seems more sensible
for a query such as this.

Thanks for your time and help.

Regards,

     Richard.
-- 
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