Comment by PaulHoule | Hacker News Reader

Comment by PaulHoule | Hacker News Reader

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If you want general-purpose generation than it has to be able to respect constraints (e.g. figure art of a person has 0..1 belly buttons, 0..2 legs is unspoken) as it is generative models usually get those things right but don't always if they can stick together the tiles they use internally in some combination that makes sense locally but not globally.

General intelligence may not be SAT/SMT solving but it has to be able to do it, hence, backtracking.

Today I had another of those experiences of the weaknesses of LLM reasoning, one that happens a lot when doing LLM-assisted coding. I was trying to figure out how to rebuild some CSS after the HTML changed for accessibility purposes and got a good idea for how to do it from talking to the LLM but at that point the context was poisoned, probably because there was a lot of content about the context describing what we were thinking about at different stages of the conversation which evolved considerably. It lost its ability to follow instructions and I'd tell it specifically to do this or do that and it just wouldn't do it properly and this happens a lot if a session goes on too long.

My guess is that the attention mechanism is locking on to parts of the conversation which are no longer relevant to where I think we're at and in general the logic that considers the variation of either a practice (instances) or a theory over time is a very tricky problem and 'backtracking' is a specific answer for maintaining your knowledge base across a search process.

> General intelligence may not be SAT/SMT solving but it has to be able to do it, hence, backtracking.

Just to add some more color to this. For problems that completely reduce to formal methods or have significant subcomponents that involve it, combinatorial explosion in state-space is a notorious problem and N variables is going to stick you with 2^N at least. It really doesn't matter whether you think you're directly looking at solving SAT/search, because it's too basic to really be avoided in general.

When people talk optimistically about hallucinations not being a problem, they generally mean something like "not a problem in the final step" because they hope they can evaluate/validate something there, but what about errors somewhere in the large middle? So even with a very tiny chance of hallucinations in general, we're talking about an exponential number of opportunities in implicit state-transitions to trigger those low-probability errors.

The answer to stuff like this is supposed to be "get LLMs to call out to SAT solvers". Fine, definitely moving from state-space to program-space is helpful, but it also kinda just pushes the problem around as long as the unconstrained code generation is still prone to hallucination.. what happens when it validates, runs, and answers.. but the spec was wrong?

Personally I'm most excited about projects like AlphaEvolve that seem fearless about hybrid symbolics / LLMs and embracing the good parts of GOFAI that LLMs can make tractable for the first time. Instead of the "reasoning is dead, long live messy incomprehensible vibes", those guys are talking about how to leverage earlier work, including things like genetic algorithms and things like knowledge-bases.[0] Especially with genuinely new knowledge-discovery from systems like this, I really don't get all the people who are still staunchly in either an old-school / new-school camp on this kind of thing.

[0]: MLST on the subject: https://www.youtube.com/watch?v=vC9nAosXrJw

When I was interested in information extraction I saw the problem of resolving language to a semantic model [1] as containing an SMT problem. That is, words are ambiguous, sentences can parse different ways, you have to resolve pronouns and explicit subjects, objects and stuff like that.

Seen that way the text is a set of constraints with a set of variables for all the various choices you make determining it. And of course there is a theory of the world such that "causes must precede their effects" and all the world knowledge about instances such as "Chicago is in Illinois".

The problem is really worse than that because you'll have to parse sentences that weren't generated by sound reasoners or that live in a different microtheory, deal with situations that are ambiguous anyway, etc. Which is why that program never succeeded.

[1] in short: database rows

What if you gave the model a tool to "willfully forget" a section of context. That would be easy to make. Hmm I might be onto something.

I guess you could have some kind of mask that would let you suppress some of the context from matching, but my guess is that kind of thing might cause problems as often as it solves them.

Back when I was thinking about commonsense reasoning with logic it was obviously a much more difficult problem to add things like "P was true before time t", "there will be some time t in the future such at P is true", "John believes Mary believes that P is true", "It is possible that P is true", "there is some person q who believes that P is true", particularly when you combine these qualifiers. For one thing you don't even have a sound and complete strategy for reasoning over first-order logic + arithmetic but you also have a combinatorical explosion over the qualifiers.

Back in the day I thought it was important to have sound reasoning procedures but one of the reasons none of my foundation models ever became ChatGPT was that I cared about that and I really needed to ask "does change C cause an unsound procedure to get the right answer more often?" and not care if the reasoning procedure was sound or not.

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