Readit NewsMax Gumin focused on just the constraint solver and added a "minimum entropy heuristic", popularized the work and coined the term "wave function collapse", as the way the solver worked was evocative of (a naive view) of how quantum mechanics solves systems [2]. Gumin's repo also has many other resources of implementations and descriptions [3].
I've published a paper on an extension that adds in a type of backtracking to both the "WFC" portion of the solver and the modify in blocks portion of the solver, which can be found in [4], for those interested.
[0] https://paulmerrell.org/model-synthesis/
[1] https://www.boristhebrave.com/2021/10/26/model-synthesis-and...
[2] https://github.com/mxgmn/WaveFunctionCollapse
[3] https://github.com/mxgmn/WaveFunctionCollapse?tab=readme-ov-...
I just went through this and had prominent researchers willing to endorse me but unable to do so because of the now stringent requirements on arXiv.
FYI, I am an independent researcher.
arXiv has a keyword based search engine. It looks for words as is in the text. PaperMatch tries to find similar papers that are closer in meaning.
Here is an alternative approach: Take one paper that you like, copy the abstract from arXiv (or arXiv ID) and paste it in PaperMatch. This should help you find similar papers.
EDIT: You should provide this in an "information"/"about"/"how to use" dialogue or page to help people use the tool better.
Some feedback:
I tried searching for "wave function collapse algorithm", "gumin wave function collapse", "wfc" and "model synthesis" without any relevant hits to the area of research I was interested in. I got a lot of quantum computing and other physics related papers.
The "WFC algorithm" overloaded the term (and has nothing to do with quantum mechanics) so it's kind of a bad case for this type of search. Model synthesis is way too generic, so again, might be a bad case for this.
The first page of results using "wave function collapse algorithm" from arXiv itself gives relevant results.
https://github.com/zzyzek/PunchOutModelSynthesis
Here's a gallery of sample outputs from the algorithm:
https://github.com/zzyzek/PunchOutModelSynthesis/blob/main/r...
I have an online demo of the algorithm in action for different tilesets (it's a little rough, so be warned):
https://zzyzek.github.io/PunchOutModelSynthesis/
The idea is you take an example image, chop it into little segments and infer tile rules depending on the overlap. It's very much old fashioned "machine learning/artificial intelligence" (that is, without any neural networks involved). There's also a demo of tile rule inference idea here:
https://zzyzek.github.io/TileRuleHighlighter/
In CSPs, each cell is a 'decision variable' with a 'domain' of values, which get pruned by 'constraints' that propagate to values invalidated by the decisions made in the connected variables, until the whole 'problem' gets into either a solution which 'satisfies' all the constraints, or a contradictory state where a variable's domain is empty, causing the algorithm to backtrack.
CSPs have the advantage of having clear and efficient methods to go back to a previous state and keep exploring every alternate possibility, rather than having to restart from the beginning. The article hints at that possibility ('saving checkpoints' or'reverse the collapsing of a cell'); there's a whole field of study dedicated on the best ways to do that on a large scale for very general problems.
Personally, I find CSPs overly general and mired in esoteric, byzantine terminology. It's a large cognitive load to put on people to run through the glossary of terms just to talk about the problem set up. I don't think the quantum mechanic analogy is great but I can see it being much more intuitive than the obscure language of CSPs.
[0] https://www.boristhebrave.com/2021/10/31/constraint-based-ti...