You can also publish it as an insights article here
I know that paper well and people have been asking me to do one for quite a while, but I never seem to get around to it.
The thing is it must not contain your own personal ideas, but rather be an analysis of the paper - you can of course have things that could be investigated, further thoughts etc at the end of the article that we can discuss here, and that is where you can mention your lie group ideas.
In doing that, and maybe helping in answering some of your 'issues' the following may help:
https://arxiv.org/abs/1402.6562
Basically all the paper does is justify in a rigorous way the following. In mathematical modelling there are generalizations of ordinary probability - they are called generalized probability models or theory. A generalized probability model makes only very simple and quite general assumptions. You have:
1. Something unspecified called states. All you are doing here is saying whatever you are modelling can be in something called a state without specifying in anyway what a state actually is. Not much of an assumption really.
2. This is the main assumption - the space of states is convex - which simply means you can apply ordinary probability theory. Specifically it says any state a can be written in the form a = ∑pi ai where ai are other possible states of the system and the pi are all positive and sum to one. Of course that sum can just contain one element so you have a = a which is trivial. If that is the only way a state can be written as such a sum then it by definition is called pure. The interpretation of the pi is as a probability ie if the system is in state a then when you do something to it, without even specifying what that something is, the pi gives the probability in will be found in state a1.
Ordinary probability theory easily fits this - in fact it's the simplest generalized probability theory. The pure states are the possible outcomes of what you are modelling and the sum ∑pi ai where the ai are other pure states ie the event space as per the Kolmogorov axioms, then pi is the probability of getting ai. In this view the pure states are usually thought of as a vector where the i'th element is the i'th event of your event space.
Now in Hardy's paper all he is noticing is in ordinary probability theory you can't continuously go from one pure state to another. But if you want to model physical systems by pure states then this is something you want to do. If a system is in pure state a at time t=0 and state b at time t = 1, then it went through some other pure state at time t=1/2. Imposing that and you are inevitably lead to QM which Hardy's paper gives the technical detail of. That's all QM is really. Formally QM is just the simplest generalized probability model where systems continuously change to other pure states.
That's why I always say formally we know very well what QM is, and why it is that way, - what it means, or even if what it means is worth pursuing ie the math is all that's required, is another matter, and we have all sorts of answers to that.
Thanks
Bill