Leanstral 1.5: Proof abundance for all

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• Hacker News
• I applaud mistral's efforts but reading this release made me realize that Europe is far far behind and that once the gap is solidified I don't think its recoverable in the same way Canada's brain drain had on its economy

  The best and the brightest from Europe have no incentive to build in Europe when they can do it in America and be compensated and treated far better

  by zuzululu
• treated far better != earn more money
  by pbkompasz
• As somebody who moved to the US and then back to Europe again, you may get more money there, but you'll pay for it with a much worse environment and lifestyle.

  Life's not all about the number on your bank account. Once you're past a certain level (which a competent engineer can easily reach in Europe as well), the marginal utility of money diminished quite quickly.

  by vrganj
• Fortunately for Europe, the US is doing its best to make itself both an undesirable and unavailable immigration target.
  by InsideOutSanta
• At this point I wouldn’t move to USA if you paid me double the salary. There are more things in life than money.

  That said, if (or when) the progress of the LLMs flatten out, then I think even Europe can catch up in a few years. If they don’t, and that seems unlikely to me, if the required compute needs to increase at the rate it does today, then I am not sure any of us can predict where society ends up.

  by bjelkeman-again
• I gave Codex with GPT-5.5 High this prompt:

      Identify bugs in [datrs/varinteger](https://github.com/datrs/varinteger) . Do NOT look at the GitHub issues, just inspect the source
  

  It also found the bug that Leanstral 1.5 found and the authors highlighted. I think this bug wasn't especially tricky; it's just a case of too few eyeballs on this repo.

  Congrats on the release regardless! Excited for the direction Lean + automated AI proofs are headed.

  Disclosure: I work at OpenAI.

  by moonset
• the mechanism is whats interesting, rather than whether it could do it.

  this sounds like a great tool to add to the toolbelt, as part of the "how do we handle all the code output from LLMs" problem

  by 8note
• GPT-5.5 is what, a trillion+ parameter model? I think the insight here is that you can do this with a tiny model.
  by InsideOutSanta
• Leanstral 1.5 has 6B active parameters. How many parameters does GPT-5.5 have?
  by noperator
• Given that they directly compare to GPT-5.5 in their documentation. This comes off as puppy kicking to me. They state it is not SOTA, even IN its domain!

  Honestly: Think twice before dragging your firm into what you say.

  Disclaimer: I speak for myself. Not any firm I am associated with.

  by ilc
• > It also found the bug that Leanstral 1.5 found and the authors highlighted

  This is a little bit like someone pointing the moon and you look at the finger.

  The formal proof domain goes way beyond just finding bugs.

  It has tons of usages in term of functional safety, protocol validation, cryptography, etc...

  The fact Mistral tackle this kind of problem is both smart and not so surprising.

  Smart because it is niche enough that they do not front face the big competitors (yet).

  No so surprising because the French labs have a well known and long time expertise with formal proof tools (Coq and all its Ocaml associated tools). It has been historically mainly pushed by the aerospace and train industries (Airbus, Dassault, Alsthom).

  by adev_
• [dupe] https://news.ycombinator.com/item?id=48738938
  by ChrisArchitect
• Have you ever been downvoted for calling 'dupe?' I once was downvoted after calling dupe on a link posted thrice. HN is an interesting place to hang out, that's for sure.
  by rtaylorgarlock
• Lean is such a wonderful language. So hyped by these releases.
  by strongly-typed
• Try out Leanstral 1.5 on the latest version of OpenATP! OpenATP is an open-source Python package and CLI for agentic automated theorem provers. It natively supports running provers locally in Docker or remotely in Modal sandboxes.

  GitHub: https://github.com/henryrobbins/open-atp

  Docs: https://open-atp.henryrobbins.com

  by henryrobbins00
• It would be nice if special purpose models provided a some diverse examples of exactly the input required to get its expected performance on a mix of problem types. Maybe also a document intended for LLMs to read that advises on prompt construction.

  I've found that you can get wildly different quality results from these sorts of models due to seemingly insignificant differences in prompt construction. It would be much easier to guess at what it wants if I could just see some RL transcripts -- and so the model author is in a much better position to provide initial advice.

  by nullc
• Was fun to see their developers make nods to Le Chaton Fat in the announcements for this on Twitter.

  I suspect a true "big new general-purpose" model is around the corner from them, whether or not they were in on Le Chaton Fat for real. They've mentioned it after the media circus. Hopefully more creatively named than just "Large 4".

  by easygenes
• I also submitted the HuggingFace link itself here: https://news.ycombinator.com/item?id=48779902
  by satvikpendem
• Discussed the other day:

  https://news.ycombinator.com/item?id=48738938

  by andai
• Can this be useful for someone with no prior knowledge of lean? I'd like to verify a software I'm working on, but I have no experience in formal verification. Can I get useful result with the spec, the code and some (limited) learning time on my side?
  by raphinou
• I think at minimum you would need to understand which theorems you want to prove about your code, and how to express those in Lean. Otherwise you won’t be able to verify the output. It may have proven some statement that is machine checked to be correct, but it’s pointless if you don’t understand what that statement means and if it covers what you want to verify about your code.
  by rubendev
• Read this section of the article “ Bug Discovery: Finding Hidden Flaws”, they appear to have used the model on open source Rust to find issues starting with just the Rust code. You might be also able to have conversations that help you write the Lean to verify your application, but I’m not certain about this.
  by camkego
• You need to understand the bits you are trying to prove, but not the full proof. It's more like reading haskell types than math, even though the vocabulary is heavily inspired by math.
  by rzmmm
• I've gone from zero knowledge of lean4 to the point where I'm doing most of my coding with it in ~6 months, and this was dramatically helped by how facile the AI assist is: it's remarkable how consistently fluent models are in lean4. I've found this to be true of the near frontier and smaller local models alike, LLMs just seem to get lean4.

  I still have a ways to go before calling myself a lean4 expert, but I don't need assist to get useful programs anymore.

  The ability to start with very little knowledge and still be able to trust parts you don't fully understand is a real unlock on learning progress: it's both practical and motivating to get useful programs you can rely on with incomplete knowledge, it sort of drags you in. You're bounded by the subset of the language that describes your axiom and proposition surface, not the subset that describes the intermediate steps. Over time as your ambition goes up, you need to understand more to do more things, but you can operate safely at level N+1 in a sense.

  It's also just a delightful programming language irrespective of its theorem proving role, and it's remarkably fast. I've got it bolted to io_uring and in many cases it blows the ass off of C++ with libuv or Rust with Tokio. Now and again you'll see some huge tail at the p99.99 latency or something and you go make a number fixed width or something, but you have to tune C++ and Rust too.

  by reinitctxoffset
• Curious that they are pitching Lean 4 for formal verification. I thought that this was more the domain of Isabelle/HOL and TLA+. At least I would have expected a model trained at using all three. Maybe also Isabell/Isar, which seems preferable for forward derivations in linear algebra. Could anyone shed some light on this?
  by RossBencina
• It is true that Lean has seen relatively little adoption in software verification compared to e.g. Isabelle and Rocq (previously Coq). Even Agda has had more traction in that domain.

  However, Lean is currently gaining significant momentum as an alternative, particularly due to its capabilities as a general-purpose functional programming language.

  Personally, I think something based on Hoare or separation logic would be more practical as it'd be easier to align requirements with specifications. I like Dafny and F*.

  by nextos
• >One such bug was in the sign function for zigzag decoding of the datrs/varinteger library. On input Std.U64.MAX, the expression (value + 1) overflowed, causing crashes in debug mode and silent corruption in release mode—an edge case that testing and fuzzing would typically miss.

  that library is: https://github.com/datrs/varinteger

  it seems probably correct, as there's an identical issue filed on that repo a week before this was published: https://github.com/datrs/varinteger/issues/8 (is this a leanstral employee? they have almost no info and only very sparse activity. or did leanstral perhaps just pick up this issue?)

  it's a tiny, surprisingly-poorly tested, long-untouched (8y) library: https://github.com/datrs/varinteger/blob/master/tests/test.r... that has about 1k downloads per day: https://crates.io/crates/varinteger [1] which seems rather low.

  I don't think I'd consider that such a smashing success that it's worth bringing up as the sole example tbh. though automated detection is certainly useful. or is this a noteworthy accomplishment for this sub-field? I haven't played with proof-writing LLMs, but given the paucity of training data I wouldn't be surprised if they're a bit rough compared to general coding.

  1: https://crates.io/crates/varinteger lists it as https://github.com/mafintosh/varinteger-rs which redirects to https://github.com/datrs/varinteger , so despite looking different at a glance it does appear to be the same library

  by Groxx
• The problem with proof is that it’s a bit hard sometimes to convey the value. The point is not to find bugs, but to prove that there are none (of a certain class; under certain assumptions; etc). But it’s a hard story to sell, so often the marketing is around “look at this bug we found”.
  by frostlynx
• I find it a bit ridiculous to be critisizing Mistral, of all companies, as falling behind the Frontier models.

  Firstly, who hasn't fallen behind? Grok...Meta....? A lot of big companies are struggling.

  Secondly, Mistral are trying to solve a different problem.

  Finally, Mistral should be congratulated for staying in the race for so long now.

  by bjt12345
• Considering the (apparent) heaps of money and brains thrown at GROK I'd argue MISTRAL is relatively futher ahead.

  Every time I go back to trying GROK it is an abysmal disappointment.

  by canada_dry