I think the issue with them making assumptions and failing to properly diagnose issues comes more from fine-tuning than any particular limitation in LLMs themse

I think the issue with them making assumptions and failing to properly diagnose issues comes more from fine-tuning than any particular limitation in LLMs themselves. When fine tuned on a set of problem->solution data it kind of carries the assumption that the problem contains enough data for the solution. What is really needed is a tree of problems which appear identical at first glance, but the issue and the solution is something that is one of many possibilities which can only be revealed by finding what information is lacking, acquiring that information, testing the hypothesis then, if the hypothesis is shown to be correct, then finally implementing the solution. That's a much more difficult training set to construct. The editing issue, I feel needs something more radical. Instead of the current methods of text manipulation, I think there is scope to have a kind of output position encoding for a model to emit data in a non-sequential order. Again this presents another training data problem, there are limited natural sources to work from showing programming in the order a programmer types it. On the other hand I think it should be possible to do synthetic training examples by translating existing model outputs that emit patches, search/replaces, regex mods etc. and translate those to a format that directly encodes the final position of the desired text. At some stage I'd like to see if it's possible to construct the models current idea of what the code is purely by scanning a list of cached head_embeddings of any tokens that turned into code. I feel like there should be enough information given the order of emission and the embeddings themselves to reconstruct a piecemeal generated program.