No, you misunderstand, the intersection is in the solution to the instance. The 2XSAT are problems that can contain both 2-SAT clauses (two literals per OR clause) and XORSAT clauses (linear equations). 2-SAT (as well as XORSAT) are just special cases of that. You can also think of it as 2-SAT, but confined into a linear subspace of Z_2^n.
That isn't the intersection of 2SAT and XORSAT, it's the union. Problems in the intersection would be solvable by either type of solver. I don't think it's "obvious" that your problem class should be polynomial; 2XSAT as you've described it (is it your own invention? I haven't found a reference) appears to be a strictly more powerful problem class.
It's not a union of those classes, it's a different class, and as you say, it's more powerful, because it can be projected (my reduction adds additional variables) into 3-SAT and SAT instance.
Yes, 2XSAT is the name I gave it, and I couldn't find it anywhere. The reduction is surprisingly simple, yet nobody mentions it. That's why I am warning people here - just based on this alone, I 80% believe that P=NP with a practical algorithm (which either way involves solving linear equations). And I wouldn't be surprised somebody coming up with the algorithm.
The reason why I say it's an intersection is because that's how the set of solutions of an instance looks like. That's what we need to figure out - how to characterize the sets of solutions described by SAT instance (i.e. sets of assignments to boolean variables that satisfy the instance).
However, it's not that easy, even if you characterize them as interesections of 2-SAT and XORSAT instances, set of solutions to 2-SAT is notoriously hard to characterize too, for example, #2SAT is not known. And polynomial algorithms for 2-SAT and XORSAT are doing very different things, and it's not at all obvious how to generalize them into a common algorithm that can do both.
As a mathematician, my advice to you is to build up some theory around this problem class. I find it entirely plausible that you can reduce 3sat to it. I'd encourage you to look for a reduction from 2XSAT to 3sat. Find some problems that are well-expressed in the language of 2XSAT. You might find something worthy of publication. From there you'll want to shop your results around at conferences. You may drum up some interest in your work, or even a collaborator. Just don't act confident that you've cracked a keystone problem in the field. You think you're on the right path, and that's exciting, but we've all been there and we've all met dozens of novices who were utterly convinced of their incorrect solution to this problem. It's a huge red flag that you're a waste of time.
As a grad student, I got perhaps hundreds of "dear professor" emails claiming proof of everything from squaring the circle to the BSD conjecture. Reflexively running from anybody making such claims is a necessary survival skill. Math is a field where the bullshit asymmetry principle[1] is particularly stark. Finding a flaw in a proof can take vastly more effort than is spent concocting it.
The opposite reduction from 2XSAT to SAT is obvious, it's just a special case.
I think professionals of every field have to deal with passionate amateurs of all levels. I understand why many people don't want to do it, but IMHO overemphasis on professionalism (culturally coming from enormous peer pressures) is hurting any field. The superprizes make it even worse.
> Just don't act confident that you've cracked a keystone problem in the field.
I am not acting like that, but I also have to be honest that my goal is specific - to understand why we can or can't have a polynomial algorithm. I.e. I have a strategy already, what I need is a 2nd opinion about some specifics of it.
Honestly, I don't think you have the proof.
Proving p is euqal to np will have very big consequences, not only in computer science but also in logics for example.
I think this problem is far more complex than you think and it is not going to be solved by chance by an amateur. If it's going to be solved, there will be a deep math argument.