Transformers are inherently succinct
openreview.net
This paper is being published at ICLR 2026 (top AI conference), and was selected as one of three outstanding papers.
openreview.net
> As a consequence of this succinctness, we show that basic verification problems for transformers, such as emptiness and equivalence, are provably intractable: specifically, EXPSPACE-complete.
If you were hoping to formally prove the correctness of a large transformer, it turns out that you're going to need an exponentially larger amount of space to do your verification, more than you could possibly afford.
SAT solvers in practice are quick on just about everything.
SAT solvers being programs that solve the original NP-compete problem.
> As a consequence of this succinctness, we show that basic verification problems for transformers, such as emptiness and equivalence, are provably intractable: specifically, EXPSPACE-complete.
This doesn't imply you can't get aid from a LLM to e.g. implement a function that has a formal specification (an application I think is very promising), but surely it has some profound implications on how much of a large system can be understood by a LLM at once, without supervision.
Transformers Are Inherently Succinct (2025) - https://news.ycombinator.com/item?id=48014197 - May 2026 (9 comments)
Authors used LTL (linear temporal logic) to express, basically, non-reduced non-ordered binary decision diagrams. Or just binary decision diagrams, BDDs.
BDDs are almost guaranteed to have exponential size because they do not employ reduction (sharing of common expressions). Reduced BDDs are more succinct and reduced ordered BDDs are even more succinct.
Also, transformers in the paper are constructed, not trained. Training any model to express some truth table is very hard. They also did not perform comparison with, say, Kolmogorov-Arnold representation, which is also universal approximator.
So this paper is not as deep as one may think it is.
Just nit picking a bit:
> Training any model to express some truth table is very hard
What kind of models are you including here? Truth tables can be modeled in regular code very easily and reliably. And I’m sure there are many deterministic models that could do the same. Are you talking about LLMs in particular or a certain category/type of models?
> What kind of models are you including here?
The truth tables? Predict zeroth bit in enwik9 from, say, bits of 64 previous bytes. Or multiply two 16 bit numbers and predict bit 16 of the multiplication result.
Reduced Ordered BDDs are likely not as succinct as an arbitrary BDD.
The famous Hidden Weight Bit problem can be more succinctly expressed in arbitrary BDDs (changing the order and revisiting nodes), but are provably EXPSPACE in ROBDDs.
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We study ROBDDs because they uniquely reduce to a canonical form. All functions have exactly one (!!!!) ROBDD.
BDDs in general however are really arbitrary, as arbitrary as any codebase can basically get. That makes BDDs in general to difficult to study or do math upon.
Results based on the studies of arbitrary BDDs do NOT apply to the simpler, easier to understand world of ROBDDs. And vice versa.
Ingo Wegener's "Branching Programs and Binary Decision Diagrams" copyright year 2000, is considered the introductory work on all BDD related matters. On the front cover are two BDDs: BOTH of these implement the hidden weighted bit function (HWB-4) introduced in the first chapter, and is basically the main example used throughout the whole book.
Please locate a copy of the front cover ASAP, before continuing with my post.
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Now that you've found the book cover, look at it carefully and understand the HWB-4 function. (Just traverse it by hand, dotted lines represent "x1 == 0", while solid lines represent "x1 == 1").
Both BDDs fail to be ROBDDs: the left diagram explores the variables in this order: x1, x2, x3, x4, and then (depending on path), x1/x2/x3. That is, the variables x1/x2/x3 are explored _TWICE_ (breaking the rule for ROBDDs).
The example on the right also fails to be a ROBDD. The variable orderings are seemingly random: one branch covers x4, x1, x2, x3. The other branch is x4, x3, x1, x2. This fails the "ordered" property as the different branches cover different orderings.
BOTH are BDDs however. Both clearly implement the HWB-4 function efficiently (less than EXP-space. Indeed, its a very small graph for the front cover of a book). If these were actually ROBDDs, they'd be ridiculously large and unable to fit.
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ROBDDs are again, studied due to the unique property (and proof), that for any given function, there can only be one ROBDD ever generated (!!!!). For the verification problem of circuits, this is extremely useful. Different circuits might implement the same function, despite one circuit being smaller or using less power (or other useful properties).
To "prove" that the circuits output the same function, you simply create an ROBDD. Because functions ALWAYS result with the same ROBDD, you can ALWAYS use this process to prove circuit equivalence.
ROBDDs aren't succinct. No: their usefulness is the opposite. Exactly one ROBDD to ever find for any binary function that has ever existed or will ever exist.
It's exactly as related to real models as computer science is to real computers.
i.e. the blowup is only exponential in one direction.
Edit: Actually nevermind. If UHAT could be compiled into LTL w/ polynomial overhead then that would also work for the languages that have exponential overhead in LTL but since they don't there is a strict separation.
> doesn't that mean we're approaching optimality?
Transformers are Markov chains [1]. Somewhere around this fascinating site [2] I read that stateful models have an advantage. Author provided an example, a state machine with two states A and B, where at state A transitions are to state A (output 0) and to state B (output 1) with equal probability and at state B the transition is always to state A and output is always 1.
For this state machine just one bit of memory can make an optimal prediction that ones always go in pairs, whereas Markov chain will approximate this prediction and never reach optimality.
[1] https://arxiv.org/abs/2410.02724
[2] https://bactra.org/If you include all the information the LLM uses to produce the next token as part of the state, then of course the LLM is a Markov chain.
So would be any other process for sampling continuations of a text, with finite memory.