Great write-up. People keep saying "we can just write tests" or more recently "we can use formal verification," thinking these are sufficient safeguards we can use and then relegate all the implementation to LLMs. But the fact is that probabilistic guessing machines can't save them. People can't escape the need to actually understand the things they are building.
These probabilistic guessing machines are pretty great for creating these formal models, e.g. TLA+, and then guessing if the implementation aligns with the spec.. In fact, it's my go-to tool for constructing soft guardrails for the model, so the design it's going to implement is logically sound. Same as for people: it's easier to make something working when you have a spec that is working.
Of course, it still allows the risk that you don't actually get to understand it.
> People can't escape the need to actually understand the things they are building.
While on the one hand, you do need some kind of grounding in human specification for what to build and what good looks like, any particular defect humans can find should be findable via software.
It does somewhat, or at least used to for a few months. Nowadays it kinda seems that the models have internalized something like TLA and are thinking in it in parallel to thinking in the language they’re writing, so it doesn’t help as much. This is all educated guesses from me, I’ve been telling models to do TLA back in the stone age around February and stopped seeing improvements when telling them to start with specs.
I’m however pretty sure that if you push a good model hard enough on a code base complex enough it’ll find stuff it wouldn’t have otherwise, the Specula folks have some experience with this.
A TLA+ spec defines both a model and properties (global invariants). How do you know that the properties the LLM specifies are the ones you care about?
My question is concerned with improving quality of AI implementations. Its plainly true, uninteresting, and beside the point to observe that AI cannot read minds.
I recently had this discussion with an energetic junior coworker who just learned about TLA+ and thought it would solve all the problems. I think it probably did give the LLM that he was using to do the actual implementation a better starting off point, but the obvious gap remains, which is whether or not the spec (TLA) matches the implementation (elixir) which there's just not a good answer for. I encouraged him to put his TLA code in our docs folder, because as far as I'm concerned, it's just a suggestion, and then write (and understand...) some property tests about the feature.
I think there probably is some value in vibecoding TLA specs and not actually understanding the invariants yourself, but it's way oversold by the talking heads of the tech world, and the gaps need to be filled in some other way if you refuse to write your own code.
i think part of this is a shortcoming of our programming languages. generally, languages allow for the expression of partial graphs, which makes the verification problem technically challenging. my take is that a language that only exposes closed-graph semantics could help bridge the gap between the model and the implementation, even if not absolute.
What you say reminds me of the "Von Neumann Languages Lack Useful Mathematical Properties" section in John Backus's Turing award paper. One of his criticisms of what we would now call imperative languages is that they make it exceedingly difficult to formally prove facts about a program.
I discovered Quint[0] due to this comment[1] on HN. Quint is "an executable specification language [which works in JavaScript] with delightful tooling based on the temporal logic of actions (TLA)". I think it is awesome and anybody interested in TLA+ should check it out.
>> Formal methods including TLA+ also can't/don't prevent or can only workaround side channels in hardware and firmware that is not verified. But that's a different layer.
>> Things formal methods shouldn't be expected to find: Floating point arithmetic non-associativity, side-channels
I love this. This is great to read if you are trying to use TLA+ for something.
In a different vein, another thing TLA+ isn't great at is modeling atomics and in particular weak-memory semantics or anything that's not sequentially consistent. If you translate your algorithm to pcal, it will run as if it was sequentially consistent. If you need to model non-sequential-consistency, then that needs to be spelled out with explicit logic to TLA+, which is probably too complicated and error-prone to do by hand. The C/C++/Rust memory models permit a lot of wacky stuff. I imagine you need to add read caches and writeback buffers for each variable with cache-flushing instructions at appropriate points, but maybe there is a more elegant way to do it.
If you use rust, miri and loom both have analyzers that can check some non-sequentially-consistent behavior (and loom doesn't actually implement sequential-consistency at all).
This is true, and it falls into the "possible but not ergonomic" category for modeling systems like this in TLA+. Concurrent programs reading & writing to shared variables can be reordered at two levels: the compiler, and then the CPU. Specifying this in TLA+ is possible but difficult, and your conventional TLA+ specification will assume things happen in a linear order within each thread, although interleaved arbitrarily between threads. In other words by default PlusCal works like there is both a barrier and memory fence between each action. Even with strong memory semantics like x86-TSO, specifying something like the action of the store buffer (where a core writes a value and can read the updated value but its write is not yet visible to other cores) requires actually writing your own tiny implementation of x86-TSO; there isn't one already defined as a library you can easily use.
I've been thinking lately about how to make this more ergonomic, as I've been getting into lock-free algorithms and would like to be able to specify them nicely in TLA+.
Which style guides? That makes no sense to me because 1) if you're writing a lock-free algorithm/data structure you presumably both know what you're doing and care a lot about performance, 2) many lock-free algorithms don't even require any seq_cst operations (or equivalent fences), and 3) weak memory orderings can be essential to getting acceptable performance in critical paths.
I would also note that aside from formal methods, LLMs are absolutely not trustworthy but the top frontier models can reason to some degree about weak memory orderings, and can at least find concurrency bugs which can be later confirmed by human expert review (preferably after eliminating false positives via adversarial LLM review of the findings).
> Atomic variables can be used simply and safely, as long as you are using the sequentially consistent memory model (memory_order_seq_cst), which is the default.
A lot of companies’ in-house guidelines then say you are allowed to use acquire/release if you are implementing a lock, relaxed if are implementing a counter.
This IMO probably reflects most companies’ distrust in their own developers to develop lock free data structures.
I disagree with this advice, and consider it outdated.
Arguably, seq_cst is helpful for informal reasoning, because it's not hard to imagine all permutations and interleavings. But in my opinion, nobody should be doing lock-free programming based on informal reasoning. Algorithms should be considered incorrect unless they've been rigorously validated, ideally with formal methods or at least model checking.
Very few lock-free algorithms require sequential consistency. There are exceptions, such as Chase-Lev queues, but they are rare.
The added confidence that seq_cst gives you if the algorithm hasn't been properly validated is IMHO worthless.
I disagree as well. It’s more of a clutch to make inexperienced people complain that their lockless algorithm is slow and find a reputable third-party library instead.
Agree, look at how many incorrect concurrent algorithms have been published over the years, where seq_cst is assumed. It's easy enough to mess up concurrency without any weak memory orderings. There are only a few people I know of who I would trust to informally reason about weak memory orderings in nontrival algorithms (Dmitry Vyukov, for one).
Yeah, the last time I had to check anything regarding memory orderings, I wrote a custom analyzer for that problem. It's...not easy. The state space is enormous too, so even with compiled code I had to take some shortcuts and prove parts of the problem by hand.
Uhm, I can see the desire to simplify, but the passage about "reachability" sounds odd.
Sure, TLA+ lets you verify whether P is true in every state of every behavior by checking []P. But a _counterexample_ to that property, if it exist, is _some_ state in _some_ behaviour where P is false. Thus, if your model checker proves []P false, you have indirectly proven E<>!P (where the initial E means exactly "for some behaviour").
Going back to the example, "proving that a game is winnable" should be achievable by model checking the invariant "the game is never winnable" and failing. Or am I missing something here?
IMO TLA+ is not very good. It has super weird syntax, and a whole separate DSL (PlusCal) to give it workable syntax for programs. You can really tell it was created by the same mind as LaTeX.
What is the Typst of formal modeling?
Another issue is that you end up with a formal model that passes, but then you have still have to convert that to a real language by hand and not make any mistakes.
It's a reasonable question. There are novel things I like about the syntax - like vertically-aligned conjunction & disjunction lists - but I don't really want to defend syntax that still uses all-caps KEYWORDS like it's the COBOL era and makes you use string values for enums. The underlying formalism is, however, amazing for thinking in, and it's used by P, Quint, and FizBee which all to varying degrees paint themselves as TLA+ successor languages.
I agree that spec/implementation conformance checking is also an issue. P has apparently had some success with PObserve for trace validation (checking whether the log of a running system is a valid execution of a P spec) but it is still not a well-known method with these tools in the same way that fuzzing or property-based testing have become. This requires some real product-level thinking to make usable and possibly full ownership of the system execution environment inside a VM or something like that.
I feel like PlusCal and TLA+ operate at a different level. I use PlusCal primarily when what I'm modeling has a lot of sequential "A then B then C..." steps. With PlusCal a program counter is implied and it maps more directly to sequential algorithms.
When I write regular TLA+, it's usually for things that aren't nearly as "order-dependent".
You should look into Quint: https://github.com/quint-co/quint. It comes with simple syntax and very good developer tooling. Quint to TLA+ is what Typst is to Latex.
While both TLA+ and ADA/Spark might be necessary, I haven’t noticed much mention of ADA/SPARK here, which kind of surprised me that they are not used in conjunction as frequent as I might have thought.
For some critical software we developed, we used TLA+ for high-level formal specification. As mentioned earlier, transitioning the actual implementation to another language can be challenging, especially if partial hardware bootstrapping is required. We ended up implementing the high-level specification created in TLA+ using ADA/Spark, which minimized our exposure to buffer and assertion failures. However, optimizing the code to meet performance thresholds was at times frustrating and time-consuming, like any low-level implementation with a new tool/language for us.
I’m curious about any new tools or workflows for leveraging TLA+ high-level specification implementation into other languages like C and Rust. What approaches are people taking once the formalized specification is verified in TLA+ to complete the implementation?
I might be out of the loop, but using TLA+ spec and translate then to other languages hasn’t been a successful use case for LLMs. While they can be helpful, a significant effort is still required to ensure that implementations accurately adhere to the specifications.
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[ 3.1 ms ] story [ 35.6 ms ] threadOf course, it still allows the risk that you don't actually get to understand it.
While on the one hand, you do need some kind of grounding in human specification for what to build and what good looks like, any particular defect humans can find should be findable via software.
I’m however pretty sure that if you push a good model hard enough on a code base complex enough it’ll find stuff it wouldn’t have otherwise, the Specula folks have some experience with this.
https://github.com/specula-org/Specula
I think there probably is some value in vibecoding TLA specs and not actually understanding the invariants yourself, but it's way oversold by the talking heads of the tech world, and the gaps need to be filled in some other way if you refuse to write your own code.
The fact is when LLMs get good enough you WILL be able to build software without reading/understanding the code.
Whether or not you think we are already at that point is kind of an unimportant detail.
I would say we are quite close, depending on the type of software you are building.
https://dl.acm.org/doi/epdf/10.1145/359576.359579
[0]: https://github.com/quint-co/quint
[1]: https://news.ycombinator.com/item?id=49865720
The internet discovers TLA+. Now what?
https://news.ycombinator.com/item?id=49863600
> From "The Future of TLA+ [pdf]" (2024) https://news.ycombinator.com/item?id=41385141 :
>> Formal methods including TLA+ also can't/don't prevent or can only workaround side channels in hardware and firmware that is not verified. But that's a different layer.
>> Things formal methods shouldn't be expected to find: Floating point arithmetic non-associativity, side-channels
In a different vein, another thing TLA+ isn't great at is modeling atomics and in particular weak-memory semantics or anything that's not sequentially consistent. If you translate your algorithm to pcal, it will run as if it was sequentially consistent. If you need to model non-sequential-consistency, then that needs to be spelled out with explicit logic to TLA+, which is probably too complicated and error-prone to do by hand. The C/C++/Rust memory models permit a lot of wacky stuff. I imagine you need to add read caches and writeback buffers for each variable with cache-flushing instructions at appropriate points, but maybe there is a more elegant way to do it.
If you use rust, miri and loom both have analyzers that can check some non-sequentially-consistent behavior (and loom doesn't actually implement sequential-consistency at all).
I've been thinking lately about how to make this more ergonomic, as I've been getting into lock-free algorithms and would like to be able to specify them nicely in TLA+.
I would also note that aside from formal methods, LLMs are absolutely not trustworthy but the top frontier models can reason to some degree about weak memory orderings, and can at least find concurrency bugs which can be later confirmed by human expert review (preferably after eliminating false positives via adversarial LLM review of the findings).
> Atomic variables can be used simply and safely, as long as you are using the sequentially consistent memory model (memory_order_seq_cst), which is the default.
That’s from https://isocpp.github.io/CppCoreGuidelines/CppCoreGuidelines
A lot of companies’ in-house guidelines then say you are allowed to use acquire/release if you are implementing a lock, relaxed if are implementing a counter.
This IMO probably reflects most companies’ distrust in their own developers to develop lock free data structures.
Arguably, seq_cst is helpful for informal reasoning, because it's not hard to imagine all permutations and interleavings. But in my opinion, nobody should be doing lock-free programming based on informal reasoning. Algorithms should be considered incorrect unless they've been rigorously validated, ideally with formal methods or at least model checking.
Very few lock-free algorithms require sequential consistency. There are exceptions, such as Chase-Lev queues, but they are rare.
The added confidence that seq_cst gives you if the algorithm hasn't been properly validated is IMHO worthless.
There's also RustMC for Rust.
Sure, TLA+ lets you verify whether P is true in every state of every behavior by checking []P. But a _counterexample_ to that property, if it exist, is _some_ state in _some_ behaviour where P is false. Thus, if your model checker proves []P false, you have indirectly proven E<>!P (where the initial E means exactly "for some behaviour").
Going back to the example, "proving that a game is winnable" should be achievable by model checking the invariant "the game is never winnable" and failing. Or am I missing something here?
What is the Typst of formal modeling?
Another issue is that you end up with a formal model that passes, but then you have still have to convert that to a real language by hand and not make any mistakes.
I'm not sure there is one, but you can start exploring here:
https://en.wikipedia.org/wiki/Category:Formal_specification_...
For TLA+-styled model checking, though, there is Quint: https://quint.sh/docs/why
I agree that spec/implementation conformance checking is also an issue. P has apparently had some success with PObserve for trace validation (checking whether the log of a running system is a valid execution of a P spec) but it is still not a well-known method with these tools in the same way that fuzzing or property-based testing have become. This requires some real product-level thinking to make usable and possibly full ownership of the system execution environment inside a VM or something like that.
When I write regular TLA+, it's usually for things that aren't nearly as "order-dependent".
For some critical software we developed, we used TLA+ for high-level formal specification. As mentioned earlier, transitioning the actual implementation to another language can be challenging, especially if partial hardware bootstrapping is required. We ended up implementing the high-level specification created in TLA+ using ADA/Spark, which minimized our exposure to buffer and assertion failures. However, optimizing the code to meet performance thresholds was at times frustrating and time-consuming, like any low-level implementation with a new tool/language for us.
I’m curious about any new tools or workflows for leveraging TLA+ high-level specification implementation into other languages like C and Rust. What approaches are people taking once the formalized specification is verified in TLA+ to complete the implementation?
I might be out of the loop, but using TLA+ spec and translate then to other languages hasn’t been a successful use case for LLMs. While they can be helpful, a significant effort is still required to ensure that implementations accurately adhere to the specifications.