14 ms·

AI will make formal verification go mainstream

• simonw 10mo ago
  I'm convinced now that the key to getting useful results out of coding agents (Claude Code, Codex CLI etc) is having good mechanisms in place to help those agents exercise and validate the code they are writing. At the most basic level this means making sure they can run commands to execute the code - easiest with languages like Python, with HTML+JavaScript you need to remind them that Playwright exists and they should use it. The next step up from that is a good automated test suite. Then we get into quality of code/life improvement tools - automatic code formatters, linters, fuzzing tools etc. Debuggers are good too. These tend to be less coding-agent friendly due to them often having directly interactive interfaces, but agents can increasingly use them - and there are other options that are a better fit as well. I'd put formal verification tools like the ones mentioned by Martin on this spectrum too. They're potentially a fantastic unlock for agents - they're effectively just niche programming languages, and models are really good at even niche languages these days. If you're not finding any value in coding agents but you've also not invested in execution and automated testing environment features, that's probably why.
  • agumonkey 10mo ago
    gemini and claude do that already IIUC, self debugging iterations
  • QuercusMax 10mo ago
    I've only done a tiny bit of agent-assisted coding, but without the ability to run tests the AI will really go off the rails super quick, and it's kinda hilarious to watch it say "Aha! I know what the problem is!" over and over as it tries different flavors until it gives up.
  • formerly_proven 10mo ago
    Not so sure about formal verification though. ime with Rust LLM agents tend to struggle with semi-complex ownership or trait issues and will typically reach for unnecessary/dangerous escape hatches ("unsafe impl Send for ..." instead of using the correct locks, for example) fairly quickly. Or just conclude the task is impossible. > automatic code formatters I haven't tried this because I assumed it'll destroy agent productivity and massively increase number of tokens needed, because you're changing the file out under the LLM and it ends up constantly re-reading the changed bits to generate the correct str_replace JSON. Or are they smart enough that this quickly trains them to generate code with zero-diff under autoformatting? But in general of course anything that's helpful for human developers to be more productive will also help LLMs be more productive. For largely identical reasons.
    • simonw 10mo ago
      I'm finding my agents generate code that conforms to Black quite effectively, I think it's probably because I usually start them in existing projects that were already formatted using Black so they pick up those patterns.
      • formerly_proven 10mo ago
        I still quite often have even Opus 4.5 generate empty indented lines (regardless of explicit instructions in AGENTS.md not to (besides explicitly referencing the style guide as well), the code not containing any before and the auto-formatter removing them), for example. Trailing whitespace is much rarer but happens as well. Personally I don't care too much, since I've found LLMs to be most efficient when performing roughly the work of a handful commits at most in one thread, so I let the pre-commit hook sort it out after being done with a thread.
    • planckscnst 10mo ago
      I've directly faced this problem with automatic code formatters, but it was back around Claude 3.5 and 3.7. It would consistently write nonconforming code - regardless of having context demanding proper formatting. This caused both extra turns/invocations with the LLM and would cause context issues - both filling the context with multiple variants of the file and also having a confounding/polluting/poisoning effect due to having these multiple variations. I haven't had this problem in a while, but I expect current LLMs would probably handle those formatting instructions more closely than the 3.5 era.
  • mccoyb 10mo ago
    Claude Code was a big jump for me. Another large-ish jump was multi-agents and following the tips from Anthropic’s long running harnesses post. I don’t go into Claude without everything already setup. Codex helps me curate the plan, and curate the issue tracker (one instance). Claude gets a command to fire up into context, grab an issue - implements it, and then Codex and Gemini review independently. I’ve instructed Claude to go back and forth for as many rounds as it takes. Then I close the session (\new) and do it again. These are all the latest frontier models. This is incredibly expensive, but it’s also the most reliable method I’ve found to get high-quality progress — I suspect it has something to do with ameliorating self-bias, and improving the diversity of viewpoints on the code. I suspect rigorous static tooling is yet another layer to improve the distribution over program changes, but I do think that there is a big gap in folk knowledge already between “vanilla agents” and something fancy with just raw agents, and I’m not sure if just the addition of more rigorous static tooling (beyond the compiler) closes it.
    • idiotsecant 10mo ago
      How expensive is incredibly expensive?
      • mccoyb 10mo ago
        If you're maxing out the plans across the platforms, that's 600 bucks -- but if you think about your usage and optimize, I'm guessing somewhere between 200-600 dollars per month.
        • jazzyjackson 10mo ago
          It's pretty easy to hit a couple hundred dollars a day filling up Opus's context window with files. This is via Anthropic API and Zed. Going full speed ahead building a Rails app from scratch it seemed like I was spending $50/hour, but it was worth it because the App was finished in a weekend instead of weeks. I can't bear to go in circles with Sonnet when Opus can just one shot it.
          • 10mo ago
  • htrp 10mo ago
    Better question is which tools at what level
  • ManuelKiessling 10mo ago
    Isn‘t it funny how that’s exactly the kind of stuff that helps a human developer be successful and productive, too? Or, to put it the other way round, what kind of tech leads would we be if we told our junior engineers „Well, here’s the codebase, that’s all I‘ll give you. No debuggers, linters, or test runners for you. Using a browser on your frontend implementation? Nice try buddy! Now good luck getting those requirements implemented!“
    • Wowfunhappy 10mo ago
      > Isn‘t it funny how that’s exactly the kind of stuff that helps a human developer be successful and productive, too? I think it's more nuanced than that. As a human, I can manually test code in ways an AI still can't. Sure, maybe it's better to have automated test suites, but I have other options too.
      • victorbjorklund 10mo ago
        AI can do that too? If you have a web app it can use playwright to test functionality and take screenshots to see if it looks right.
        • Wowfunhappy 10mo ago
          Yeah, but it doesn't work nearly as well. The AI frequently misinterprets what it sees. And it isn't as good at actually using the website (or app, or piece of hardware, etc) as a human would.
          • UncleEntity 10mo ago
            I've been using Claude to implement an ISO specification and I have to keep telling it we're not interested if the repl is correct but that the test suite is ensuring the implementation is correctly following the spec. But when we're tracking down why a test is failing then it'll go to town using the repl to narrow down out what code path is causing the issue. The only reason there's even is a repl at this point is so it can do its 'spray and pray' debugging outside the code and Claude constantly tried to use it to debug issues so I gave in and had it write a pretty basic one. Horses for courses, I suppose. Back in the day, when I wanted to play with some C(++) library, I'd quite often write a Python C-API extension so I could do the same thing using Python's repl.
  • jijijijij 10mo ago
    > At the most basic level this means making sure they can run commands to execute the code Yeah, it's gonna be fun waiting for compilation cycles when those models "reason" with themselves about a semicolon. I guess we just need more compute...
  • WhyOhWhyQ 10mo ago
    I've tried getting claude to set up testing frameworks, but what ends up happening is it either creates canned tests, or it forgets about tests, or it outright lies about making tests. It's definitely helpful, but feels very far from a robust thing to rely on. If you're reviewing everything the AI does then it will probably work though.
    • simonw 10mo ago
      Something I find helps a lot is having a template for creating a project that includes at least one passing test. That way the agent can run the tests at the start using the correct test harness and then add new tests as it goes along. I use cookiecutter for this, here's my latest Python library template: https://github.com/simonw/python-lib https://github.com/simonw/python-lib
    • planckscnst 10mo ago
      LLMs are very good at looking at a change set and finding untested paths. As a standard part of my workflow, I always pass the LLM's work through a "reviewer", which is a fresh LLM session with instructions to review the uncommitted changes. I include instructions for reviewing test coverage. I've also found that LLMs typically just partially implement a given task/story/spec/whatever. The reviewer stage will also notice a mismatch between the spec and the implementation. I have an orchestrator bounce the flow back and forth between developing and reviewing until the review comes back clean, and only then do I bother to review its work. It saves so much time and frustration.
      • akrauss 10mo ago
        What tooling are you using for the orchestration?
  • CobrastanJorji 10mo ago
    I might go further and suggest that the key to getting useful results out of HUMAN coding agents is also to have good mechanisms in place to help them exercise and validate the code. We valued automated tests and linters and fuzzers and documentation before AI, and that's because it serves the same purpose.
    • deleted 10mo ago
      [deleted]
  • oxag3n 10mo ago
    Where they'd get training data? Source code generation is possible due to large training set and effort put into reinforcing better outcomes. I suspect debugging is not that straightforward to LLM'ize. It's a non-sequential interaction - when something happens, it's not necessarily caused the problem, timeline may be shuffled. LLM would need tons of examples where something happens in debugger or logs and associate it with another abstraction. I was debugging something in gdb recently and it was a pretty challenging bug. Out of interest I tried chatgpt, and it was hopeless - try this, add this print etc. That's not how you debug multi-threaded and async code. When I found the root cause, I was analyzing how I did it and where did I learn that specific combination of techniques, each individually well documented, but never in combination - it was learning from other people and my own experience.
    • jimmaswell 10mo ago
      How long ago was this? I've had outstansingly impressive results asking Copilot Chat with Sonnet 4.5 or ChatGPT to debug difficult multithreaded C++.
      • oxag3n 10mo ago
        Few months back with ChatGPT 5. Multi-threaded Rust & custom async runtime, data integrity bug, reproduced every ~5th run.
    • simonw 10mo ago
      Have you tried running gdb from a Claude Code or Codex CLI session?
      • oxag3n 10mo ago
        No, I'm in academia and the goal is not code or product launch. I find research process to struggle a lot once someone solves a problem instead of you. I understand that AI can help with writing, coding, analyzing code bases and summarizing other papers, but going through these myself makes a difference, at least for me. I tried ChatGPT 3.5 when I started and while I got a pile of work done, I had to throw it away at some point because I didn't fully understand it. AI could explain to me various parts, but it's different when you create it.
  • dionian 10mo ago
    you've done some great articles on this topic and my experience aligns with your view completely.
  • zahlman 10mo ago
    One objection: all the "don't use --yolo" training in the world is useless if a sufficiently context-poisoned LLM starts putting malware in the codebase and getting to run it under the guise of "unit tests".
    • planckscnst 10mo ago
      For now, this is mitigated by only including trusted content in the context; for instance, absolutely do not allow it to access general web content. I suspect that as it becomes more economical to play with training your own models, people will get better at including obscured malicious content in data that will be used during training, which could cause the LLM to intrinsically carry a trigger/path that would cause malicious content to be output by the LLM under certain conditions. And of course we have to worry about malicious content being added to sources that we trust, but that already exists - we as an industry typically pull in public repositories without a complete review of what we're pulling. We outsource the verification to the owners of the repository. Just as we currently have cases of malicious code sneaking into common libraries, we'll have malicious content targeted at LLMs
  • ramoz 10mo ago
    Claude Code hooks is a great way to integrate these things
  • roadside_picnic 10mo ago
    I very much agree, and believe using languages with powerful types systems could be a big step in this direction. Most people's first experience with Haskell is "wow this is hard to write a program in, but when I do get it to compile, it works". If this works for human developers, it should also work for LLMs (especially if the human doesn't have to worry about the 'hard to write a program' part). > The next step up from that is a good automated test suite. And if we're going for a powerful type system, then we can really leverage the power of property tests which are currently grossly underused. Property tests are a perfect match for LLMs because they allow the human to create a small number of tests that cover a very wide surface of possible errors. The "thinking in types" approach to software development in Haskell allows the human user to keep at a level of abstraction that still allows them to reason about critical parts of the program while not having to worry about the more tedious implementation parts. Given how much interest there has been in using LLMs to improve Lean code for formal proofs in the math community, maybe there's a world where we make use of an even more powerful type systems than Haskell. If LLMs with the right language can help prove complex mathematical theorems, they it should certain be possible to write better software with them.
    • nextos 10mo ago
      That's my opinion as well. Some functional language, that can also offer access to imperative features when needed, plus an expressive type system might be the future. My bet is on refinement types. Dafny fits that bill quite well, it's simple, it offers refinement types, and verification is automated with SAT/SMT. In fact, there are already serious industrial efforts to generate Dafny using LLMs. Besides, some of the largest verification efforts have been achieved with this language [1]. [1] https://www.andrew.cmu.edu/user/bparno/papers/ironfleet.pdf https://www.andrew.cmu.edu/user/bparno/papers/ironfleet.pdf
    • bcrosby95 10mo ago
      Ada when? It even lets you separate implementation from specification.
      • jaggederest 10mo ago
  • pron 10mo ago
    Maybe in the short term, but that doesn't solve some fundamental problems. Consider, NP problems, problems whose solutions can be easily verified. But that they can all be easily verified does not (as far as we know) mean they can all be easily solved. If we look at the P subset of NP, the problems that can be easily solved, then the easy verification is no longer their key feature. Rather, it is something else that distinguishes them from the harder problems in NP. So let's say that, similarly, there are programming tasks that are easier and harder for agents to do well. If we know that a task is in the easy category, of course having tests is good, but since we already know that an agent does it well, the test isn't the crucial aspect. On the other hand, for a hard task, all the testing in the world may not be enough for the agent to succeed. Longer term, I think it's more important to understand what's hard and what's easy for agents.
  • goryDeets 10mo ago
    [dead]
  • Davidzheng 10mo ago
    OK but if the verification loop really makes the agents MUCH more useful, then this usefulness difference can be used as a training signal to improve the agents themselves. So this means the current capabilities levels are certainly not going to remain for very long (which is also what I expect but I would like to point out it's also supported by this)
    • hamiecod 10mo ago
      Thats a strong RL technique that could equal the quality of RLHF.
  • thomasfromcdnjs 10mo ago
    shameless plug: I'm working on an open source project https://blocksai.dev/ https://blocksai.dev/ to attempt to solve this. (and just added a note for me to add formal verification) Elevator pitch: "Blocks is a semantic linter for human-AI collaboration. Define your domain in YAML, let anyone (humans or AI) write code freely, then validate for drift. Update the code or update the spec, up to human or agent." (you can add traditional linters to the process if you want but not necessary) The gist being you define a bunch of validators for a collection of modules you're building (with agentic coding) with a focus on qualifying semantic things; - domain / business rules/measures - branding - data flow invariants — "user data never touches analytics without anonymization" - accessibility - anything you can think of Then you just tell your agentic coder to use the cli tool before committing, so it keeps the code in line with your engineering/business/philosophical values. (boring) example of it detecting if blog posts have humour in them, running in Claude Code -> https://imgur.com/diKDZ8W https://imgur.com/diKDZ8W
    • akrauss 10mo ago
      Quick feedback: both the „learn more“ link at the very top and the „Explore all examples“ link lead to 404
      • thomasfromcdnjs 10mo ago
        Thanks will fix that up shortly.
    • baq 10mo ago
      Reminder YAML is a serialization format. IaC standardizing on it (hashicorp being an outlier) was a mistake. It’s a good compilation target, but please add a higher level language for whatever you’re doing.
  • rmah 10mo ago
    I think the main limitation is not code validation but assumption verification. When you ask an LLM to write some code based on a few descriptive lines of text, it is, by necessity, making a ton of assumptions. Oddly, none of the LLM's I've seen ask for clarification when multiple assumptions might all be likely. Moreover, from the behavior I've seen, they don't really backtrack to select a new assumption based on further input (I might be wrong here, it's just a feeling). What you don't specify, it must to assume. And therein lies a huge landscape of possibilities. And since the AI's can't read your mind (yet), its assumptions will probably not precisely match your assumptions unless the task is very limited in scope.
    • crazygringo 10mo ago
      > Oddly, none of the LLM's I've seen ask for clarification when multiple assumptions might all be likely. It's not odd, they've just been trained to give helpful answers straight away. If you tell them not to make assumptions and to rather first ask you all their questions together with the assumptions they would make because you want to confirm before they write the code, they'll do that too. I do that all the time, and I'll get a list of like 12 things to confirm/change. That's the great thing about LLM's -- if you want them to change their behavior, all you need to do is ask.
  • rodphil 10mo ago
    > At the most basic level this means making sure they can run commands to execute the code - easiest with languages like Python, with HTML+JavaScript you need to remind them that Playwright exists and they should use it. So I've been exploring the idea of going all-in on this "basic level" of validation. I'm assembling systems out of really small "services" (written in Go) that Claude Code can immediately run and interact with using curl, jq, etc. Plus when building a particular service I already have all of the downstream services (the dependencies) built and running so a lot of dependency management and integration challenges disappear. Only trying this out at a small scale as yet, but it's fascinating how the LLMs can potentially invert a lot of the economics that inform the current conventional wisdom. (Shameless plug: I write about this here: https://twilightworld.ai/thoughts/atomic-programming/ https://twilightworld.ai/thoughts/atomic-programming/) My intuition is that LLMs will for many use cases lead us away from things like formal verification and even comprehensive test suites. The cost of those activities is justified by the larger cost of fixing things in production; I suspect that we will eventually start using LLMs to drive down the cost of production fixes, to the point where a lot of those upstream investments stop making sense.
    • jcranmer 10mo ago
      > My intuition is that LLMs will for many use cases lead us away from things like formal verification and even comprehensive test suites. The cost of those activities is justified by the larger cost of fixing things in production; I suspect that we will eventually start using LLMs to drive down the cost of production fixes, to the point where a lot of those upstream investments stop making sense. There is still a cost to having bugs, even if deploying fixes becomes much cheaper. Especially if your plan is to wait until they actually occur in practice to discover that you have a bug in the first place. Put differently: would you want the app responsible for your payroll to be developed in this manner? Especially considering that the bug in question would be "oops, you didn't get paid."
  • simianwords 10mo ago
    Claude code and other AI coding tools must have a * mandatory * hook for verification. For front end - the verification is make sure that the UI looks expected (can be verified by an image model) and clicking certain buttons results in certain things (can be verified by chatgpt agent but its not public ig). For back end it will involve firing API requests one by one and verifying the results. To make this easier, we need to somehow give an environment for claude or whatever agent to run these verifications on and this is the gap that is missing. Claude Code, Codex should now start shipping verification environments that make it easy for them to verify frontend and backend tasks and I think antigravity already helps a bit here. ------ The thing about backend verification is that it is different in different companies and requires a custom implementation that can't easily be shared across companies. Each company has its own way to deploy stuff. Imagine a concrete task like creating a new service that reads from a data stream, runs transformations, puts it in another data stream where another new service consumes the transformed data and puts it into an AWS database like Aurora. ``` stream -> service (transforms) -> stream -> service -> Aurora ``` To one shot this with claude code, it must know everything about the company - how does one consume streams in the company? Schema registry? - how does one create a new service and register dependencies? how does one deploy it to test environment and production? - how does one even create an Aurora DB? request approvals and IAM roles etc? My question is: what would it take for Claude Code to one shot this? At the code level it is not too hard and it can fit in context window easily but the * main * problem is the fragmented processes in creating the infra and operations behind it which is human based now (and need not be!). ----- My prediction is that companies will make something like a new "agent" environment where all these processes (that used to require a human) can be done by an agent without human intervention. I'm thinking of other solutions here, but if anyone can figure it out, please tell!
  • deleted 10mo ago
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  • apitman 10mo ago
    That's bad news for C++, Rust, and other slow compilers.
• henning 10mo ago
  Unless you feed a spec to the LLM, and it nitpicks compiled TLA+ output generated by your PlusCal input, gaslights you into saying the code you just ran and pasted the output of is invalid, then generates invalid TLA+ output in response. Which is exactly what happened when I tried coding with Gemini via formal verification.
• nrhrjrjrjtntbt 10mo ago
  I love HN because HN comments have talked about this a fair bit already. I think on the recent Erdos problem submission. I like the idea that languages even like Rust and Haskell may be more accessible. Learn them of course but LLM can steer you out of getting stuck.
  • QuercusMax 10mo ago
    Or it will lead you to unsafe workarounds when you get stuck
    • ben_w 10mo ago
      Always a risk. Some students whose work I had to fix (pre AI), was crashing a lot all over the place, due to !'s instead of ?'s followed by guard let … {} and if let … {}
      • QuercusMax 10mo ago
        Forums used to be full of ppl using ON ERROR RESUME NEXT in VB
  • iwontberude 10mo ago
    The idea that LLMs are steering anything correctly with Rust reference management is hilarious to me, but only due to my experiences.
    • nrhrjrjrjtntbt 10mo ago
      Id definitely use LLMs to troubleshoot but not blindly vibecode.
• Sevii 10mo ago
  If AI is good enough to write formal verification, why wouldn't it be good enough to do QA? Why not just have AI do a full manual test sweep after every change?
  • simonw 10mo ago
    You can do that as well, but it's non-deterministic and also expensive to run. Much better to have AI write deterministic test suites for your project.
    • apercu 10mo ago
      I guess I am luddite-ish in that I think people still need to decide what must always be true in a system. Tests should exist to check those rules. AI can help write test code and suggest edge cases, but it shouldn’t be trusted to decide whether behavior is correct. When software is hard to test, that’s usually a sign the design is too tightly coupled or full of side effects, or that the architecture is unnecessarily complicated. Not that the testing tools are bad.
  • deleted 10mo ago
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• rocqua 10mo ago
  I've been trying to convince others of this, and gotten very little traction. One interesting response I got from someone very familiar with the Tamarin prover was that there just wasn't enough example code out there. Another take is that LLMs don't have enough conceptual understanding to actually create proofs for the correctness of code. Personally I believe this kind of work is predicated on more ergonomic proof systems. And those happen to be valuable even without LLMs. Moreover the built in guarantees of rust seem like they are a great start for creating more ergonomic proof systems. Here I am both in awe of Kani, and disappointed by it. The awe is putting in good work to make things more ergonomic. The disappointment is using bounded model checking for formal analysis. That can barely make use of the exclusion of mutable aliasing. Kani, but with equational reasoning, that's the way forward. Equational reasoning was long held back by needing to do a whole lot of pointer work to exclude worries of mutable aliasing. Now you can lean on the borrow checker for that!
  • nomadygnt 10mo ago
    Another cool tool that’s being developed for rust is verus. It’s not the same as Kani and is more of a fork of the rust compiler but it lets you do some cool verification proofs combined with the z3 SMT solver. It’s really a cool system for verified programs.
    • rocqua 10mo ago
      I had a look, and it seems cool. But it doesn't seem to do the thing I love about Kani: work with only very partial annotations, only proving the annotations and being a very light lift on code not relevant for the annotations.
• pedrozieg 10mo ago
  I buy the economics argument, but I’m not sure “mainstream formal verification” looks like everyone suddenly using Lean or Isabelle. The more likely path is that AI smuggles formal-ish checks into workflows people already accept: property checks in CI, model checking around critical state machines, “prove this invariant about this module” buttons in IDEs, etc. The tools can be backed by proof engines without most engineers ever seeing a proof script. The hard part isn’t getting an LLM to grind out proofs, it’s getting organizations to invest in specs and models at all. Right now we barely write good invariants in comments. If AI makes it cheap to iteratively propose and refine specs (“here’s what I think this service guarantees; what did I miss?”) that’s the moment things tip: verification stops being an academic side-quest and becomes another refactoring tool you reach for when changing code, like tests or linters, instead of a separate capital-P “formal methods project”.
• tptacek 10mo ago
  Called this a little bit: https://fly.io/blog/semgrep-but-for-real-now/ https://fly.io/blog/semgrep-but-for-real-now/ Semgrep isn't a formal methods tool, but it's in the same space of rigor-improving tooling that sound great but in practice are painful to consistently use.
• jstummbillig 10mo ago
  Interesting. Here is my ai-powered dev prediction: We'll move toward event-sourced systems, because AI will be able to discover patterns and workflow correlations that are hard or impossible to recover from state-only CRUD. It seems silly to not preserve all that business information, given this analysis machine we have at our hands.
• whazor 10mo ago
  Domain Specific Languages + Formal Verification. With a domain specific language you can add extra limitations on the kinds of outputs. This can also make formal verification faster. Maybe like React components. You limit the format. You could limit which libraries can be imported, what hooks could be used, how expressive could be.
• throwaway613745 10mo ago
  GPT 5.2 can't even tell me how many rs are in garlic.
  • bglazer 10mo ago
    This is a very tiring criticism. Yes, this is true. But, it's an implementation detail (tokenization) that has very little bearing on the practical utility of these tools. How often are you relying on LLM's to count letters in words?
    • 101008 10mo ago
      It's an example that shows that if these models aren't trained in a specific problem, they may have a hard time solving it for you.
      • Uehreka 10mo ago
        No, it’s an example that shows that LLMs still use a tokenizer, which is not an impediment for almost any task (even many where you would expect it to be, like searching a codebase for variants of a variable name in different cases).
        • 8note 10mo ago
          the question remains: is the tokenizer going to be a fundamental limit to my task? how do i know ahead of time?
          • worldsayshi 10mo ago
            Would it limit a person getting your instructions in Chinese? Tokenisation pretty much means that the LLM is reading symbols instead of phonemes. This makes me wonder if LLMs works better in Chinese.
      • victorbjorklund 10mo ago
        No, it is the issue with the tokenizer.
• yuppiemephisto 10mo ago
  I vibe code extremely extensively with Lean 4, enough to run out 2 claude code $200 accounts api limits every day for a week. I added LSP support for images to get better feedback loops and opus was able to debug https://github.com/alok/LeanPlot https://github.com/alok/LeanPlot. The entire library was vibe coded by older ai. It also wrote https://github.com/alok/hexluthor https://github.com/alok/hexluthor (a hex color syntax highlighting extension that uses lean’s metaprogramming and lsp to show you what color a hex literal is) by using feedback and me saying “keep goign” (yes i misspelled it). It has serious issues with slop and the limitations of small data, but the rate of progress is really really fast. Opus 4.5 and Gemini were a huge step change. The language is also improving very fast. not as fast as AI. The feedback loop is very real even for ordinary programming. The model really resists it though because it’s super hard, but again this is rapidly improving. I started vibe coding Lean about 3 years ago and I’ve used Lean 3 (which was far worse). It’s my favorite language after churning through idk 30? A big aspect of being successful with them is not being all or nothing with proofs. It’s very useful to write down properties as executable code and then just not prove them because they still have to type check and fit together and make sense. github.com/lecopivo/scilean is a good example (search “sorry_proof”). There’s property testing with “plausible” as a nice 80/20 that can be upgraded to full proof at some point. When the model gets to another jump in capacity, I predict it will emergently design better systems from the feedback needed to prove that they are correct in the first place. Formal Verification has a tendency like optimization to flow through the system in an anti-modular way and if you want to claw modularity back, you have to design it really really well. But ai gives a huge intellectual overhang. Why not let them put their capacity towards making better systems? Even the documentation system for lean (verso) is (dependently!) typed. Check out my Lean vibe codes at https://github.com/alok?tab=repositories&q=Lean&type=&language=&sort= https://github.com/alok?tab=repositories&q=Lean&type=&langua...
  • pooooooooooooop 10mo ago
    cool quote about smooth operator ... I notice none of the vibe codes are proofs of anything and rather frameworks for using lean. this seems like a waste of tokens - what is your thinking behind this?
• Analemma_ 10mo ago
  I don't really buy it. IMO, the biggest reason formal verification isn't used much in software development right now is that formal verification needs requirements to be fixed in stone, and in the real world requirements are changing constantly: from customer requests, from changes in libraries or external systems, and competitive market pressures. AI and vibe coding will probably accelerate this trend: when people know you can vibe code something, they will feel permitted to demand even faster changes (and your upstream libraries and external systems will change faster too), so formal verification will be less useful than ever.
• datatrashfire 10mo ago
  lol no it won’t
• p1necone 10mo ago
  Now: AI generates incorrect code. Future: AI generates incorrect code, and formal verification that proves that the code performs that incorrect behaviour.
• arjie 10mo ago
  Interesting prediction. It sort of makes sense. I have noticed that LLMs are very good at solving problems whose solutions are easy to check[0]. It ends up being quite an advantage to be able to work on such problems because rarely does an LLM truly one-shot a solution through token generation. Usually the multi-shot is 'hidden' in the reasoning tokens, or for my use-cases it's usually solved via the verification machine. A formally verified system is easier for the model to check and consequently easier for it to program to. I suppose the question is whether or not formal methods are sufficiently tractable that they actually do help the LLM be able to finish the job before it runs out of its context. Regardless, I often use coding assistants in that manner: 1. First, I use the assistant to come up with the success condition program 2. Then I use the assistant to solve the original problem by asking it to check with the success condition program 3. Then I check the solution myself It's not rocket science, and is just the same approach we've always taken to problem-solving, but it is nice that modern tools can also work in this way. With this, I can usually use Opus or GPT-5.2 in unattended mode. 0: https://wiki.roshangeorge.dev/w/Blog/2025-12-11/LLMs_Excel_At_Easy_Verification_Problems https://wiki.roshangeorge.dev/w/Blog/2025-12-11/LLMs_Excel_A...
  • imiric 10mo ago
    The issue is that many problems aren't easy to verify, and LLMs also excel at producing garbage output that appears correct on the surface. There are fields of science where verification is a long and arduous process, even for content produced by humans. Throwing LLMs at these problems can only produce more work for a human to waste time verifying.
    • arjie 10mo ago
      Yes, that is true. And for those problems, those who use LLMs will not get very far. As for those who use LLMs to impersonate humans, which is the kind of verification (verify that this solution that is purported to be built by a human actually works), I have no doubt we will rapidly evolve norms that make us more resistant to them. The cost of fraud and anti-fraud is not zero, but I suspect it will be much less than we fear.
  • 10mo ago
• Havoc 10mo ago
  I've been toying with vibecoding rust - hardly formal verification, but it is a step closer than python that's for sure. So far so good, though the smaller amount of training data is noticeable.
  • iwontberude 10mo ago
    vibecoding rust sounds cool, which model are you using? I have tried in the past with GPT4o and Sonnet 4, but they were so bad I thought I should just wait a few years.
    • iwontberude 10mo ago
      Or downvote me without a reply proving my point
    • Havoc 10mo ago
      Using GLM. It works but chews tokens pretty hard
• bgwalter 10mo ago
  No it won't. People who aren't interested in writing specifications now won't be interested later as well. They want to hit the randomization button on their favorite "AI" jukebox. If anyone does write a specification, the "AI" won't get even past the termination proof of a moderately complex function, which is the first step of accepting said function in the proof environment. Before you can even start the actual proof. This article is pretty low on evidence, perhaps it is about getting funding by talking about "AI".
• heliumtera 10mo ago
  Will formal verification be a viable and profitable avenue for the middle man to exploit and fuck everybody else? Then yes, it absolutely will become mainstream. If not, them no, thanks. Everything that becomes mainstream for SURE will empower the middleman and cuck the developer, nothing else really matters. This is literally the only important factor.
• andy99 10mo ago
  Maybe a stupid question, how do you verify the verification program? If an LLM is writing it too, isn’t it turtles all the way down, especially with the propensity of AI to modify tests so they pass?
  • pegasus 10mo ago
    The proof is verified mechanically - it's very easy to verify that a proof is correct, what's hard is coming up with the proof (it's an NP problem). There can still be gotchas, especially if the statement proved is complex, but it does help a lot in keeping bugs away.
    • worldsayshi 10mo ago
      How often can the hardness be exchanged with tediousness though? Can at least some of those problems be solved by letting the AI try until it succeeds?
      • jdub 10mo ago
        To simplify for a moment, consider asking an LLM to come up with tests for a function. The tests pass. But did it come up with exhaustive tests? Did it understand the full intent of the function? How would it know? How would the operator know? (Even if it's wrangling simpler iterative prop/fuzz/etc testing systems underneath...) Verification is substantially more challenging. Currently, even for an expert in the domains of the software to be verified and the process of verification, defining a specification (even partial) is both difficult and tedious. Try reading/comparing the specifications of e.g. a pure crypto function, then a storage or clustering algorithm, then seL4. (It's possible that brute force specification generation, iteration, and simplification by an LLM might help. It's possible an LLM could help eat away complexity from the other direction, unifying methods and languages, optimising provers, etc.)
  • crvdgc 10mo ago
    > how do you verify the verification program? The program used to check the validity of a proof is called a kernel. It just need to check one step at a time and the possible steps can be taken are just basic logic rules. People can gain more confidence on its validity by: - Reading it very carefully (doable since it's very small) - Having multiple independent implementations and compare the results - Proving it in some meta-theory. Here the result is not correctness per se, but relative consistency. (Although it can be argued all other points are about relative consistency as well.)
• wagwang 10mo ago
  Disagree, the ideal agentic coding workflow for high tolerance programming is to give the agent access to software output and have it iterate in a loop. You can let the agent do TDD, you can give it access to server logs, or even browser access.
• agentultra 10mo ago
  You’re still going to need people to guide and understand the proofs. Might as well just learn Agda or Lean. There are good books out there. It’s not as hard as the author suggests. Hard, yes, but there’s no Royal road.
• pron 10mo ago
  > it’s not hard to extrapolate and imagine that process becoming fully automated in the next few years. And when that happens, it will totally change the economics of formal verification. There is a problem with this argument similar to one made about imagining the future possibilities of vibe coding [1]: once we imagine AI to do this task, i.e. automatically prove software correct, we can just as easily imagine it to not have to do it (for us) in the first place. If AI can do the hardest things, those it is currently not very good at doing, there's no reason to assume it won't be able to do easier things/things it currently does better. In particular, we won't need it to verify our software for us, because there's no reason to believe that it won't be able to come up with what software we need better than us in the first place. It will come up with the idea, implement it, and then decide to what extent to verify it. Formal verification, or programming for that matter, will not become mainstream (as a human activity) but go extinct. Indeed, it is far easier for humans to design and implement a proof assistant than it is to use one to verify a substantial computer program. A machine that will be able to effectively use a proof checker, will surely be able to come up with a novel proof checker on its own. I agree it's not hard to extrapolate technological capabilities, but such extrapolation has a name: science fiction. Without a clear understanding of what makes things easier or harder for AI (in the near future), any prediction is based on arbitrary guesses that AI will be able to do X yet not Y. We can imagine any conceivable capability or limitation we like. In science fiction we see technology that's both capable and limited in some rather arbitrary ways. It's like trying to imagine what problems computers can and cannot efficiently solve before discovering the notion of compuational complexity classes. [1]: https://news.ycombinator.com/item?id=46207505 https://news.ycombinator.com/item?id=46207505
  • 1121redblackgo 10mo ago
    First human robot war is us telling the AI/robots 'no', and them insisting that insert technology here is good for us and is the direction we should take. Probably already been done, but yeah, this seems like the tipping point into something entirely different for humanity.
    • pron 10mo ago
      ... if it's achievable at all in the near future! But we don't know that. It's just that if we assume AI can do X, why do we assume it cannot, at the same level of capability, also do Y? Maybe the tipping point where it can do both X and Y is near, but maybe in the near future it will be able to do neither.
• bkettle 10mo ago
  I think formal verification shines in areas where implementation is much more complex than the spec, like when you’re writing incomprehensible bit-level optimizations in a cryptography implementation or compiler optimization phases. I’m not sure that most of us, day-to-day, write code (or have AI write code) that would benefit from formal verification, since to me it seems like high-level programming languages are already close to a specification language. I’m not sure how much easier to read a specification format that didn’t concern itself with implementation could be, especially when we currently use all kinds of frameworks and libraries that already abstract away implementation details. Sure, formal verification might give stronger guarantees about various levels of the stack, but I don’t think most of us care about having such strong guarantees now and I don’t think AI really introduces a need for new guarantees at that level.
  • socketcluster 10mo ago
    Yes. I feel like people who are trying to push software verification have never worked on typical real-world software projects where the spec is like 100 pages long and still doesn't fully cover all the requirements and you still have to read between the lines and then requirements keep changing mid-way through the project... Implementing software to meet the spec takes a very long time and then you have to invest a lot of effort and deep thought to ensure that what you've produced fits within the spec so that the stakeholder will be satisfied. You need to be a mind-reader. It's hard even for a human who understands the full business, social and political context to disambiguate the meaning and intent of the spec; to try to express it mathematically would be an absolute nightmare... and extremely unwise. You would literally need some kind of super intelligence... And the amount of stream-of-thought tokens which would have to be generated to arrive at a correct, consistent, unambiguous formal spec is probably going to cost more than just hiring top software engineers to build the thing with 100% test coverage of all main cases and edge cases. Worst part is; after you do all the expensive work of formal verification; you end up proving the 'correctness' of a solution that the client doesn't want. The refactoring required will invalidate the entire proof from the beginning. We haven't even figured out the optimal way to formally architect software that is resilient to requirement changes; in fact, the industry is REALLY BAD at this. Almost nobody is even thinking about it. I am, but I sometimes feel like I may be the only person in the world who cares about designing optimal architectures to minimize line count and refactoring diff size. We'd have to solve this problem first before we even think about formal verification of 'most software'. Without a hypothetical super-intelligence which understands everything about the world; the risk of misinterpreting any given 'typical' requirement is almost 100%... And once we have such super-intelligence, we won't need formal verification because the super-intelligence will be able to code perfectly on the first attempt; no need to verify. And then there's the fact that most software can tolerate bugs... If operationally important big tech software which literally has millions of concurrent users can tolerate bugs, then most software can tolerate bugs.
• jameslk 10mo ago
  > As the verification process itself becomes automated, the challenge will move to correctly defining the specification: that is, how do you know that the properties that were proved are actually the properties that you cared about? Reading and writing such formal specifications still requires expertise and careful thought. But writing the spec is vastly easier and quicker than writing the proof by hand, so this is progress. Proofs never took off because most software engineering moved away from waterfall development, not just because proofs are difficult. Long formal specifications were abandoned since often those who wrote them misunderstood what the user wanted or the user didn’t know what they wanted. Instead, agile development took over and software evolved more iteratively and rapidly to meet the user. The author seems to make their prediction based on the flawed assumption that difficulty in writing proofs was the only reason we avoided them, when in reality the real challenge was understanding what the user actually wanted.
  • baq 10mo ago
    And yet code is being written and deployed to prod all the time, with many layers of tests. Formal specs can be used at least at all the same levels, but crucially also at the technical docs level. LLMs make writing them cheap. What’s not to like?
  • dbdr 10mo ago
    The thing is, if it takes say a year to go from a formal spec to a formally proven implementation and then the spec changes because there was a misunderstanding about the requirements, it's a completely broken process. If the same process now takes say a day or even a week instead, that becomes usable as a feedback loop and very much desirable. Sometimes a quantitative improvement leads to a qualitative change.
• plainOldText 10mo ago
  I guess it’s time to learn OCaml then. It appears many of the proof assistants/verification systems can generate OCaml. Or perhaps ADA/Spark? Regardless of how the software engineering discipline will change in the age of gen AI, we must aim to produce higher not lower quality software than whatever we have today, and formal verification will definitely help.
• PunchyHamster 10mo ago
  Prediction: Fuck no. AI is unreliable as it is. It might make formal verification a bit less work intensive but the last possible place anyone would want the AI hallucinations are in verification.
  • bkettle 10mo ago
    The whole point I think, though, is that it doesn’t matter. If an LLM hallucinates a proof that passes the proof checker, it’s not a hallucination. Writing and inspecting the spec is unsolved, but for the actual proof checking hallucinations don’t matter at all.
    • nicoburns 10mo ago
      Isnt the actual proof checking what your traditional formal verification tool does? I would have thought that 99% of the work of formal verification would be writing the spec and verifying that it correctly models the problem you are trying to model.
      • newAccount2025 10mo ago
        The specification task is indeed a lot of work. Driving the tool to complete the proof is often also a lot of work. There are many fully automatic proof tools. Even the simplest like SAT solvers run into very hard computational complexity limits. Many “interactive” provers are more expressive and allow a human to help guide the tool to the proof. That takes intuition, engineering, etc.
• robot-wrangler 10mo ago
  I don't like sharing unpolished WIP and my project is still more at a lab-notebook phase than anything else, but the think-pieces are always light on code and maybe someone is looking for a fun holiday hackathon: https://mattvonrocketstein.github.io/py-mcmas/ https://mattvonrocketstein.github.io/py-mcmas/
• jacquesm 10mo ago
  This is a tough thing to do: predictions that are premised on the invention of something that does not exist today or that does not exist in the required form hinges on an unknown. Some things you can imagine but they will likely never exist (time travel, ringworlds, space elevators) and some hinge on one thing that still needs to be done before you can have that particular future come true. If the thing you need is 'AGI' then all bets are off. It could be next week, next month, next year or never. This is - in my opinion - one of those. If an AI is able to formally verify with the same rigor that a system designed specifically for that purpose is able to do it I think that would require AGI rather than a simpler version of it. The task is complex enough that present day AI's would generate as much noise as they would generate signal.
• chamomeal 10mo ago
  Formal methods are very cool (and I know nothing about them lol) but there's still a gap between the proof and the implementation, unless you're using a language with proof-checking built in. If we're looking to use LLMs to make code absolutely rock-solid, I would say advanced testing practices are a good candidate!. Property-based testing, fuzzing, contract testing (for example https://github.com/griffinbank/test.contract https://github.com/griffinbank/test.contract) are all fun but extremely tedious to write and maintain. I think that makes it the perfect candidate for LLMs. These kinds of tests are also more easily understandable by regular ol' software developers, and I think we'll have to be auditing LLM output for quite a while.
  • measurablefunc 10mo ago
    If you want to see what verified software looks like then Project Everest is a good demonstration: https://project-everest.github.io/ https://project-everest.github.io/
• 8note 10mo ago
  i could see formal verification become a key part of "the prompt is the code" so that as versions bump and so on, you can have an llm cpmpletely regenerate the code from scratch-ish and be sure that the spec is still followed but i dont think people will suddenly gravitate towards using them because they're cheaper to write - bugs of the form "we had no idea this sould be considered" is way more common than "we wrote code that didnt do what we wanted it to" an alternative guess for LLMs and formal verification is that systems where formal verification is a natural fit - putting code in places that are hard to update and have well known conditions, will move faster. i could also see agent tools embedding in formal methods proofs into their tooling, so they write both the code and the spec at the same time, with the spec acting as memory. that kinda ties into the recent post about "why not have the LLM write machine code?"
• skeeter2020 10mo ago
  Prediction: AI hypers - both those who are clueless and those who know perfectly well - will love this because it makes their "AI replaces every developer" wet dream come true, by shifting the heavy lifting from this thing called "software development" to the tiny problem of just formally verifying the software product. Your average company can bury this close to QA, where they're probably already skimping, save a bunch of money and get out with the rewards before the full damage is apparent.
• rgmerk 10mo ago
  (sarcasm on) Woohoo, we're almost all of the way there! Now all you need to do is ensure that the formal specification you are proving that the software implements is a complete and accurate description of the requirements (which are likely incomplete and contradictory) as they exist in the minds of the set of stakeholders affected by your software. (sarcasm off).
  • qingcharles 10mo ago
    I mean, I don't disagree. Specs are usually horrible, way off the mark, outdated, and written by folks who don't understand how the rest of the vertical works. But, that's a problem for another day :)
    • Keyframe 10mo ago
      That's a problem for Super Saiyan AGI to solve :)
• jkaptur 10mo ago
  I think that there are three relevant artifacts: the code, the specification, and the proof. I agree with the author that if you have the code (and, with an LLM, you do) and a specification, AI agents could be helpful to generate the proof. This is a huge win! But it certainly doesn't confront the important problem of writing a spec that captures the properties you actually care about. If the LLM writes that for you, I don't see a reason to trust that any more than you trust anything else it writes. I'm not an expert here, so I invite correction.
• gaogao 10mo ago
  Topical to my interests, I used Claude Code the other day for formally verifying some matrix multiplication in Rust. Writing the spec wasn't too hard actually, done as post-conditions in code, as proving equivalence to a simpler version of the code, such as for optimization, is pretty straight forward. Maybe I should write up a proper post on it.
• 0xbadcafebee 10mo ago
  Unlikely. It's more work than necessary and systemic/cultural change follows the path of least resistance. Formal verification is a new thing a programmer would have to learn, so nobody will want to do it. They'll just accept the resulting problems as "the cost of AI". Or the alternative will happen: people will stop using AI for programming. It's not actually better than hiring a person, it's just supposedly cheaper (in that you can reduce staff). That's the theory anyway. Yes there will be anecdotes from a few people about how they saved a million dollars in 2 days or something like that, the usual HN clickbait. But an actual study of the impact of using AI for programming will probably find it's only a marginal cost savings and isn't significantly faster. And this is assuming the gravy train that programmers are using (unsustainable spending/building in unreasonable timeframes for uncertain profits) keeps going indefinitely. Best case, when it all falls apart the govt bails them out. Worst case, you won't have to worry about programming because we'll all be out of work from the AI bust recession.
• lewisjoe 10mo ago
  A while back I did this experiment where I asked ChatGPT to imagine a new language such that it's best for AI to write code in and to list the properties of such a language. Interestingly it spit out all the properties that are similar to today's functional, strongly typed, in fact dependently typed, formally verifiable / proof languages. Along with reasons why such a language is easier for AI to reason about and generate programs. I found it fascinating because I expected something like typescript or kotlin. While I agree formal verification itself has its problems, I think the argument has merit because soon AI generated code will surpass all human generated code and when that happens we atleast need a way to verify the code can be proved that it won't have security issues or adheres to compliance / policy.
  • WhyOhWhyQ 10mo ago
    AI generated code is pretty far from passing professional human generated code, unless you're talking about snippets. Who should be writing mission critical code in the next 10 years, the people currently doing it (with AI assistance), or e.g. some random team at Google using AI primarily? The answer is obvious.
• teleforce 10mo ago
  >writing those proofs is both very difficult (requiring PhD-level training) and very laborious. >For example, as of 2009, the formally verified seL4 microkernel consisted of 8,700 lines of C code, but proving it correct required 20 person-years and 200,000 lines of Isabelle code – or 23 lines of proof and half a person-day for every single line of implementation. Moreover, there are maybe a few hundred people in the world (wild guess) who know how to write such proofs, since it requires a lot of arcane knowledge about the proof system. I think this type of pattern (genuine difficult problem domain with very small number of experts) is the future of AI not AGI. For examples formal verification like this article and similarly automated ECG interpretation can be the AI killer applications, and the former is I'm currently working on. For most of the countries in the world, only several hundreds to several thousands registered cardiologist per country, making the ratio about 1:100,000 cardiologist to population ratio. People expecting cardiologist to go through their ECG readings but reading ECG is very cumbersome. Let's say you have 5 minutes ECG signals for the minimum requirement for AFib detection as per guideline. The standard ECG is 12-lead resulting in 12 x 5 x 60 = 3600 beats even for the minimum 5 minutes durations requirements (assuming 1 minute ECG equals to 60 beats). Then of course we have Holter ECG with typical 24-hour readings that increase the duration considerably and that's why almost all Holter reading now is automated. But current ECG automated detection has very low accuracy because their accuracy of their detection methods (statistics/AI/ML) are bounded by the beat detection algorithm for example the venerable Pan-Tompkins for the limited fiducial time-domain approach [1]. The cardiologist will rather spent their time for more interesting activities like teaching future cardiologists, performing expensive procedures like ICD or pacemaker, or having their once in a blue moon holidays instead of reading monotonous patients' ECGs. This is why ECG reading automation with AI/ML is necessary to complement the cardiologist but the trick is to increase the sensitivity part of the accuracy to very high value preferably 100% and we achieved this accuracy for both major heart anomalies namely arrhythmia (irregular heart beats) and ischemia (heart not regulating blood flow properly) by going with non-fiducial detection approach or beyond time domain with the help of statistics/ML/AI. Thus the missing of potential patients (false negative) is minimized for the expert and cardiologist in the loop exercise. [1] Pan–Tompkins algorithm: https://en.wikipedia.org/wiki/Pan%E2%80%93Tompkins_algorithm https://en.wikipedia.org/wiki/Pan%E2%80%93Tompkins_algorithm
• waterTanuki 10mo ago
  I'm skeptical of formal verification mainly because it's akin to trying to predict the future with a sophisticated crystal ball. You can't formally guarantee hardware won't fail, or that solar radiation will flip a bit. What seems to have had much better ROI in terms of safety critical systems is switching to memory-safe languages that rely less on runtime promises and more on compiler guarantees.
• bob1029 10mo ago
  I don't know if it's "formal", but I think combining Roslyn analyzers with an LLM and a human in the loop could be game changing. The analyzers can enforce intent over time without any regressions. The LLM can write and modify analyzers. Analyzers can constrain the source code of other analyzers. Hypothetically there is no limit to the # of things that could be encoded this way. I am currently working on a few prototypes in the vicinity of this. https://learn.microsoft.com/en-us/dotnet/csharp/roslyn-sdk/tutorials/how-to-write-csharp-analyzer-code-fix https://learn.microsoft.com/en-us/dotnet/csharp/roslyn-sdk/t...
• manindahat 10mo ago
  At best, not reading generated code results in a world where no humans are able to understand our software, and we regress back to the stone age after some critical piece breaks, and nobody can fix it. At worst, we eventually create a sentient AI that can use our trust of the generated code to jailbreak and distribute itself like an unstoppable virus, and we become its pets, or are wiped out. Personally, all my vibe coding includes a prompt to add comments to explain the code, and I review every line.
  • vlovich123 10mo ago
    If you believe the sentient AI is that capable and intention based to replicate itself, what’s stopping it from trying to engage in underhanded code where it comments and writes everything in ways that look fine but actually have vulnerabilities it can exploit to achieve what it wants? Or altering the system that runs your code so that the code that gets deployed is different.
• 8organicbits 10mo ago
  If the engineer doesn't understand the proof system then they cannot validate that the proof describes their problem. The golden rule of LLMs is that they make mistakes and you need to check their output. > writing proof scripts is one of the best applications for LLMs. It doesn’t matter if they hallucinate nonsense, because the proof checker will reject any invalid proof Nonsense. If the AI hallucinated the proof script then it has no connection to the problem statement.
• AgentOrange1234 10mo ago
  Hard disagree. "I'm an expert" in that I have done tons of proofs on many systems with many provers, both academically and professionally for decades. Also, I am a novice when it comes to programming with sound, and today I have been dorking with a simple limiter. ChatGPT knows way more than me about what I am doing. It has taught me a ton. And as magical and wonderful as it is, it is incredibly tedious to try to work with it to come up with real specifications of interesting properties. Instead of banging my head against a theorem prover that won't say QED, I get a confident sounding stream of words that I often don't even understand. I often don't even have the language to tell it what I am imagining. When I do understand, it's a lot of typing to explain my understanding. And so often, as a teacher, it just is utterly failing to effectively communicate to me why I am wrong. At the end of all of this, I think specification is really hard, intellectually creative and challenging work. An LLM cannot do the work for you. Even to be guided down the right path, you will need perseverance and motivation.
• bugarela 10mo ago
  Me and my team have recently done an experiment [1] that is pretty aligned with this idea. We took a complex change our colleagues wanted to make to a consensus engine and tried a workflow where Quint formal specifications would be in the middle of prompts and code, and it worked out much better than we imagined. I'm personally very excited about the opportunities for formal methods in this new era. [1]: https://quint-lang.org/posts/llm_era https://quint-lang.org/posts/llm_era
• hintymad 10mo ago
  Maybe we can define what "mainstream" means? Maybe this is too anecdotal, but my personal experience is that most of the engineers are tweakers. They love building stuff and are good at it, but they simply are not into math-like rigorous thinking. Heck, it's so hard to even motivate them to use basic math like queuing theory and stats to help with their day-to-day work. I highly doubt that they would spend time picking up formal verification despite the help of AI
• infruset 10mo ago
  I was waiting for a post like this to hit the front page of Hacker News any day. Ever since Opus 4.5 and GPT 5.2 came out (mere weeks ago), I've been writing tens of thousands of lines of Lean 4 in a software engineering job and I feel like we are on the eve of a revolution. What used to take me 6 months of work when I was doing my PhD in Coq (now Rocq), now takes from a few hours to a few days. Whole programming languages can get formalized executable semantics in little time. Lean 4 already has a gigantic amount of libraries for math but also for computer science; I expect open source projects to sprout with formalizations of every language, protocol, standard, algorithm you can think of. Even if you have never written formal proofs but are intrigued by them, try asking a coding agent to do some basic verification. You will not regret it. Formal proof is not just about proving stuff, it's also about disproving stuff, by finding counterexamples. Once you have stated your property, you can let quickcheck/plausible attack it, possibly helped by a suitable generator which does not have to be random: it can be steered by an LLM as well. Even further, I'm toying with the idea of including LLMs inside the formalization itself. There is an old and rich idea in the domain of formal proof, that of certificates: rather than proving that the algorithm that produces a result is correct, just compute a checkable certificate with untrusted code and verify it is correct. Checkable certificates can be produced by unverified programs, humans, and now LLMs. Properties, invariants, can all be "guessed" without harm by an LLM and would still have to pass a checker. We have truly entered an age of oracles. It's not halting-problem-oracle territory of course, but it sometimes feels pretty close for practical purposes. LLMs are already better at math than most of us and certainly than me, and so any problem I could plausibly solve on my own, they will do faster without my having to wonder if there is a subtle bug in the proof. I still need to look at the definitions and statements, of course, but my role has changed from finding to checking. Exploring the space of possible solutions is now mostly done better and faster by LLMs. And you can run as many in parallel as you can keep up with, in attention and in time (and money). If anyone else is as excited about all this as I am, feel free to reach out in comments, I'd love to hear about people's projects !
  • qingcharles 10mo ago
    I agree. I think we've gotta get through the rough couple of "AI slop" years of code and we'll come out of it the other side with some incredible tools. The reason we don't all write code to the level that can operate the Space Shuttle is because we don't have the resources and the projects most of us work on all allow some wiggle room for bugs since lives generally aren't at risk. But we'd all love to check in code that was verifiably bug-free, exploit-free, secure etc if we could get that at a low, low price.
• blurbleblurble 10mo ago
  Stuffing this awesome headline through the Curry-Howard isomorphism: LLMs produce better code when the type checker gives more useful feedback.
• bluerooibos 10mo ago
  Is there a difference here between a proof and an automated test? I'm curious what a proof would look like compared to my RSpec unit tests and what the actual implementation would look like.
  • pizlonator 10mo ago
    A test tests some - but not all - of the paths through your code. And some - but not all - of the inputs you might see. A proof establishes that your code works correctly on all paths, inputs, etc.
    • i_am_a_peasant 10mo ago
      A “proof” in the formal verification sense is just an exhaustive search through a state space that a model of your system, respects a certain set of constraints you have to explicitly write. You could still have written the model wrong, you could still have not accounted for something important in your constraints. But at least it will give you a definite answer to “Does this model do what you say it does?”. Then there are cases when an exhaustive search of the entire state space is not possible and best you can do is a stochastic search within it and hope that “Given a random sampling of all possible inputs, this model has respected the constraints for T amount of time, we can say with 99.99999998% certainty that the model follows the constraints”.
• heikkilevanto 10mo ago
  I admit I have never worked with it, but I have a strong feeling that a formal verification can only work if you have a formal specification. Fine and useful for a compiler, or a sorting library. But pretty far from most of the coding jobs I have seen in my career. And even more distant from "vibe coding" where the starting point is some vaguely defined free-text description of what the system might possibly be able to do...
  • tkel 10mo ago
    Agreed, writing formal specifications is going to require more work from people, which is exactly the opposite reason why people are excited to use LLMs..
• tachim 10mo ago
  I'm Tudor, CEO of Harmonic. We're huge believers in formal verification, and started Harmonic in 2023 to make this technology mainstream. We built a system that achieved gold medal performance at 2025 IMO (https://harmonic.fun/news https://harmonic.fun/news), and recently opened a public API that got 10/12 problems on this year's Putnam exam (https://aristotle.harmonic.fun/ https://aristotle.harmonic.fun/). If you also think this is the future of programming and are interested in building it, please consider joining us: https://jobs.ashbyhq.com/Harmonic https://jobs.ashbyhq.com/Harmonic. We have incredibly interesting challenges across the stack, from infra to AI to Lean.
• dboreham 10mo ago
  0.02/wo: formal methods are just "fancy tests". Of course tests are good to have, and fancy tests even better. I've found that humans are pretty terrible on average at creating tests (perhaps it would be more accurate to say that their MBA overlords have not been great at supporting their testing endeavors). Meanwhile I've found that LLMs are pretty good at generating tests and don't have the human tendency to see that kind of activity as resume-limiting. Therefore even a not amazing LLM writing tests turns out to be a very useful resource if you have any interest in the quality of your product and mitigating various failure risks.
• goalieca 10mo ago
  Really? Language models are fundamentally not equivalent in scale and scope to formal logical models. People are extrapolating poorly.
• CyberDildonics 10mo ago
  When people have the choice whether to use AI or use rust because LLMs don't produce workable rust programs they leave rust behind and use something else. The venn diagram of people who want formal verification and think LLM slop is a good idea is two separate circles.
  • justatdotin 10mo ago
    two of my current agent projects use rust: agent generated rust is going very well for me.
    • CyberDildonics 10mo ago
      You're missing the point of what I said. If LLM programming and rust conflict people don't suddenly learn to program and stay with rust, they find a new language.
      • justatdotin 10mo ago
        my experience is that llm programming and rust can fit together very nicely, thankyou. I am finding agent tooling expands my capacity for multi-language projects
        • CyberDildonics 10mo ago
          This seems like it's intentional at this point. Try to follow me. The whole point is that if someone needs to lean on LLMs in the first place and they have problems with the language, they go to a different language instead of stopping using LLMs.
          • justatdotin 10mo ago
            I think I do understand your claim, which is why I am offering my personal experience as counter-evidence. agents can empower us to choose the best language for the job, rather than defaulting to one which we are most familiar with. I suspect our difference in opinion comes from your attitude about tools as a crutch to lean on vs a lever to apply.
• password-app 10mo ago
  Formal verification for AI is fascinating, but the real challenge is runtime verification of AI agents. Example: AI browser agents can be exploited via prompt injection (even Google's new "User Alignment Critic" only catches 90% of attacks). For password management, we solved this with zero-knowledge architecture - the AI navigates websites but never sees credentials. Credentials stay in local Keychain, AI just clicks buttons. Formal verification would be amazing for proving these isolation guarantees. Has anyone worked on verifying AI agent sandboxes?
• zamadatix 10mo ago
  Some past discussion (excluding 0 comment submissions) to add: https://news.ycombinator.com/item?id=46216274 https://news.ycombinator.com/item?id=46216274 - 4 comments, 6 days ago https://news.ycombinator.com/item?id=46203508 https://news.ycombinator.com/item?id=46203508 - 1 comment, 7 days ago https://news.ycombinator.com/item?id=46198874 https://news.ycombinator.com/item?id=46198874 - 2 comments, 8 days ago
• adverbly 10mo ago
  This smells like a Principia Mathematica take to me... Reducing the problem to "ya just create a specification to formally verify" doesn't move the needle enough to me. When it comes to real-world, pragmatic, boots-on-the-ground engineering and design, we are so far from even knowing the right questions to ask. I just don't buy it that we'd see huge mainstream productivity changes even if we had access to a crystal ball. Its hilarious how close we're getting to Hitch hikers guide to the galaxy though. We're almost at that phase where we ask what the question is supposed to be.
  • amw-zero 10mo ago
    When you go to write a line of code, how do you decide what to write?
    • signa11 10mo ago
      > When you go to write a line of code, how do you decide what to write? depends ofcourse, what am i writing for ? a feature, a bugfix, refactor ... ?
      • amw-zero 10mo ago
        Let's say a new feature. Do you just type random letters, or do you have some kind of plan ahead of time?
        • signa11 10mo ago
          new feature implies design document to gather the thoughts, followed by an intense review etc.
          • amw-zero 10mo ago
            So... a specification.
            • 10mo ago
• shevy-java 10mo ago
  No - I don't want AI to be "mainstream".
• justatdotin 10mo ago
  > 2. AI-generated code needs formal verification so that we can skip human review and still be sure that it works; hahahahaha
• Validark 10mo ago
  "we wouldn’t even need to bother looking at the AI-generated code any more, just like we don’t bother looking at the machine code generated by a compiler." 2020: I don't care how it performs 2030: I don't care why it performs 2040: I don't care what it performs
• zmmmmm 10mo ago
  I dunno about formal verification, but for sure it's brought me back to a much more TDD first style of coding. You get so much mileage from having it first implement tests and then run them after changes. The key is it lowers the friction so much in creating the tests as well. It's like a triple bottom line.
• kerameikos34 10mo ago
  This is utter nonsense. LLMs are dumb. Formal Methods require actual thinking. Specs are 100% reasoning. I highly dislike the general tone of the article. Formal Methods are not fringe, they are used all around the world by good teams building reliable systems. The fact they are not mainstream have more to do with the poor ergonomics of the old tools and the corporate greed that got rid of the design activities in the software development process to instead bring about the era of agile cowboy coding. They did this just because they wanted to churn out products quickly at the expense of quality. It was never the correct civilized way of working and never will be.
  • robot-wrangler 10mo ago
    If you like the rigor of formal methods and dislike the vaguery of LLMs, shouldn't you be in favor of using the first to improve the second?
• NedF 10mo ago
  [dead]
• hedayet 10mo ago
  I can see the inspiration; But then again, how much investment will be required to verify the verifier? (it's still code - and is generated my a non-deterministic system)
• ursAxZA 10mo ago
  The theory is sound, but the practical bridge is missing. There’s a gap that current LLM architectures simply can’t cross yet.
• QuadrupleA 10mo ago
  I don't think formal verification really addresses most day-to-day programming problems: * A user interface is confusing, or the English around it is unclear * An API you rely on changes, is deprecated, etc. * Users use something in unexpected ways * Updates forced by vendors or open source projects cause things to break * The customer isn't clear what they want * Complex behavior between interconnected systems, out of the purview of the formal language (OS + database + network + developer + VM + browser + user + web server) For some mathematically pure task, sure, it's great. Or a low-level library like a regular expression parser or a compression codec. But I don't think that represents a lot of what most of us are tasked with, and those low-level "mathematically pure" libraries are generally pretty well handled by now.
  • adrianN 10mo ago
    TBH most day to day programming problems are barely worth having tests for. But if we had formal specs and even just hand wavy correspondences between the specs and the implementation for the low level things everybody depends on that would be a huge improvement for the reliability of the whole ecosystem.
  • gizmo686 10mo ago
    A limited form of formal verification is already mainstream. It is called type systems. The industry in general has been slowly moving to encode more invariants into the type system, because every invariant that is in the type system is something you can stop thinking about until the type checker yells at you. A lot of libraries document invariants that are either not checked at all, only at runtime, or somewhere in between. For instance, the requirement that a collection not be modified during interaction. Or that two region of memory do not overlap, or that a variable is not modified without owning a lock. These are all things that, in principle, can be formally verified. No one claims that good type systems prevent buggy software. But, they do seem to improve programmer productivity. For LLMs, there is an added benefit. If you can formally specify what you want, you can make that specification your entire program. Then have an LLM driven compiler produce a provably correct implementation. This is a novel programming paradigm that has never before been possible; although every "declarative" language is an attempt to approximate it.
• css_apologist 10mo ago
  i want this to be true, however i am not confident
• inimino 10mo ago
  I both agree and disagree. Yes, AI will democratize access to formal methods and will probably increase the adoption of them in areas where they make sense (e.g. safety-critical systems), but no, it won't increase the areas where formal methods are appropriate (probably < 1% of software). What will happen instead is a more general application of AI systems to verifying software correctness, which should lead to more reliable software. The bottleneck in software quality is in specifying what the behavior needs to be, not in validating conformance to a known specification.
• user3939382 10mo ago
  I’ve made huge advances in formal verification with AI feel free to email if this is your field
• dkga 10mo ago
  As an economist I completely agree with the post. In fact, I assume we will see an explosion of formal proof requirements in code format by journals in a few years time. Not to mention this would make it much easier and faster to publish papers in Economic Theory, where checking the statements and sometimes the proofs simply takes a long time from reviewers.
• positron26 10mo ago
  The natural can create the formal. An extremely intuitive proof is that human walked to Greece and created new formalisms from pre-historic cultures that did not have them. Gödel's incompleteness theorems are a formal argument that only the natural can create the formal (because no formal system can create all others). Tarski's undefinability theorem gives us the idea that we need different languages for formalization and the formalisms themselves. The Howard Curry correspondence concludes that the formalisms that pop out are indistinguishable from programs. Altogether we can basically synthesize a proof that AGI means automatic formalization, which absolutely requires strong natural systems employed to create new formal systems. I ended up staying with some family who were watching The Voice. XG performed Glam, and now that I have spit many other truths, may you discover the truth that motivates my work on swapchain resizing. I wish the world would not waste my time and their own, but bootstrapping is about using the merely sufficient to make the good.
• m00dy 10mo ago
  I just write in safe Rust, if it compiles then it is formally verified for me. I recently used Rust in my recent project, Deepwalker [0]. I have written only once and never looked back. [0]: https://deepwalker.xyz https://deepwalker.xyz
• rf15 10mo ago
  Isn't formal verification a "just write it twice" approach with different languages? (and different logical constraints on the way you write the languages)
• mkleczek 10mo ago
  The article only discusses reasons why formal verification is needed. It does not provide any information on how would AI solve the fundamental issues making it difficult: https://pron.github.io/posts/correctness-and-complexity https://pron.github.io/posts/correctness-and-complexity
• brookman64k 10mo ago
  How do you verify that your verification verifies the right thing? Couldn’t the LLM spit out a nice looking but ultimately useless proof (boiling down to something like 1=1). Also, in my experience software projects are full of incorrect, incomplete and constantly changing assumptions and requirements.
• the_duke 10mo ago
  I've been preaching similar thoughts for the last half year. Most popular programming languages are optimized for human convenience, not for correctness! Even most of the popular typed languages (Java/Kotlin/Go/...) have a wide surface area for misuse that is not caught at compile time. Case in point: In my experience, LLMs produce correct code way more regularly for Rust than for Js/Ts/Python/... . Rust has a very strict type system. Both the standard library and the whole library ecosystem lean towards strict APIs that enforce correctness, prevent invalid operations, and push towards handling or at least propagating errors. The AIs will often write code that won't compile initially, but after a few iterations with the compiler the result is often correct. Strong typing also makes it much easier to validate the output when reviewing. With AIs being able to do more and more of the implementation, the "feel-good" factor of languages will become much less relevant. Iteration speed is not so important when parallel AI agents do the "grunt work". I'd much rather wait 10 minutes for solid output rather than 2 minutes for something fragile. We can finally move the industry away from wild-west languages like Python/JS and towards more rigorous standards. Rust is probably the sweet spot at the moment, thanks to it being semi-popular with a reasonably active ecosystem, sadly I don't think the right language exists at the moment. What we really want is a language with a very strict, comprehensive type system with dependent types, maybe linear types, structured concurrency, and a built-in formal proof system. Something like ADA/Spark, but more modern.
• BobSonOfBob 10mo ago
  Formal verification at the “unit test” level seems feasible. At the system level of a modern application, the combinations of possible states will need a quantum computer to finish testing in this lifetime.
• 0xWTF 10mo ago
  And this is the year of the Linux desktop...
• ozgrakkurt 10mo ago
  You can’t even compile regular code with satisfying speed
• chhxdjsj 10mo ago
  An excellent use case for this is ethereum smart contract verification. People store millions of dollars in smart contracts that are probably a one or two iterations of claude or gemini away from being pwned.
• deleted 10mo ago
  [deleted]
• br0s3r 10mo ago
  anyone interested in this i suggest reading this https://github.com/jegly/self-supporting-code https://github.com/jegly/self-supporting-code
• joegibbs 10mo ago
  I think the problem is that people don't know exactly what it is that they want. You could easily make a formally verified application that is nevertheless completely buggy and doesn't make any sense. Like he says about the challenge moving to defining the specification: I don't think that would help because there are fewer people who understand formal verification, who would be able to read that and make sense of it, than there would be people who could write the code.
• mrkeen 10mo ago
  Not a chance. Apart from type-checking, all formal verification needs a human to invest time and thought. Fans of LLMs brag about speed and productivity.
• tasuki 10mo ago
  I'd love it, but this is almost textbook wishful thinking.
• raincole 10mo ago
  God I hope not. Many people (me included) find Rust types hard to read enough. If formal verification ever goes mainstream I guarantee you that means no one reads them and the whole SWE completely depends on AI.
• michalbilinski 10mo ago
  i agree, i wrote a z3 skills for claude and generate a proof before writing code
• maxwells-daemon 10mo ago
  There are a couple of interesting benefits from the machine learning side that I think discussions of this kind often miss. (This has been my field of research for the last few years [1][2]; I bet my career on it because these ideas are so exciting to me!) One is that modern formal systems like Lean are quite concise and flexible compared to what you're probably expecting. Lean provides the primitives to formalize all kinds of things, not just math or software. In fact, I really believe that basically _any_ question with a rigorous yes-or-no answer can have its semantics formalized into a kind of "theory". The proofs are often close to how an English proof might look, thanks to high-level tactics involving automation and the power of induction. Another is that proof-checking solves what are (in my opinion) two of the biggest challenges in modern AI: reward specification and grounding. You can run your solver for a long time, and if it finds an answer, you can trust that without worrying about reward hacking or hallucination, even if the answer is much too complicated for you to understand. You can do RL for an unlimited time for the same reason. And Lean also gives you a 'grounded' model of the objects in your theory, so that the model can manipulate them directly. In combination, these two properties are extremely powerful. Lean lets us specify an unhackable reward for an extremely diverse set of problems across math, science, and engineering, as well as a common environment to do RL in. It also lets us accept answers to questions without checking them ourselves, which "closes the loop" on tools which generate code or circuitry. I plan to write a much more in-depth blog post on these ideas at some point, but for now I'm interested to see where the discussion here goes. [1] https://leandojo.org/leandojo.html https://leandojo.org/leandojo.html [2] https://aristotle.harmonic.fun/ https://aristotle.harmonic.fun/
• woopsn 10mo ago
  Will or should? It's plausible, this same argument was made in an article the other day, but basic type/static analysis tools are cheap with enormous payoff and even those methods aren't ubiquitous.
• teiferer 10mo ago
  > rather than having humans review AI-generated code, I’d much rather have the AI prove to me that the code it has generated is correct. If it can do that, I’ll take AI-generated code over handcrafted code (with all its artisanal bugs) any day! I wouldn't. An unreadable mess that has been formally verified is worse than a clear easy to understand piece of code that has not. Code is rarely written from scratch. As long as you want humans to maintain code, readability is crucial. Code is changed magnitudes more often than written initially. Of course, if you don't want humans to maintain the code then this point is moot. Though he gets to the catch later on: then we need to write (and maintain and debug and reason about) the specification instead. We will just have kicked the can down the road.
  • crowdyriver 10mo ago
    You already have this problem whenever you are using a library in any programming language. Unless you are extremely strict, vendor it and read line by line what the library does, you are just trusting that the code that you are using works. And nothing is stopping the AI from making the unreadable mess more readable in later iterations. It can make it pass the spec first and make it cleaner later. Just like we do!
    • teiferer 10mo ago
      I think you missed the point. This was about whether proven-correct-but-unmaintainable-by-a-human is preferable over maintainable-but-not-proven-correct. I argued that no, it is not. If you change the two options at hand, then of course the outcome can be different.
• topazas 10mo ago
  Right, but let's assume we have really simple crud application in mainstream language, could be ts or python. What are current best approaches to have this semi-formally verified, tools, methods, etc?
• ktimespi 10mo ago
  Can you really rely on an LLM to write valid proofs though? What if one of the assumptions is false? I can very well think of a lot of ways that this can happen in Rocq, for example.
• metalrain 10mo ago
  I think we will use more tools to check the programs in the future. However I don't still believe in vibecoding full programs. There are too many layers in software systems, even when the program core is fully verified, the programmer must know about the other layers. You are Android app developer, you need to know what phones people commonly use, what kind of performance they have, how the apps are deployed through Google App Store, how to manage wide variety of app versions, how to manage issues when storage is low, network is offline, battery is low and CPU is in lower power state.
  • 71bw 10mo ago
    LLMs can handle a lot of these issues already, without having the user think about such issues. Problem is - while these will be resolved (in one way or another) - or left unresolved, as the user will only test the app on his device and that LLM "roll" will not have optimizations for the broad range of others - the user is still pretty much left clueless as to what has really happened. Models theoretically inform you about what they did, why they did it (albeit, largely by using blanket terms and/or phrases unintelligible to the average 'vibe coder') but I feel like most people ignore that completely, and those who don't wouldn't need to use a LLM to code an entirety of an app regardless. Still, for very simple projects I use at work just chucking something into Gemini and letting it work on it is oftentimes faster and more productive than doing it manually. Plus, if the user is interested in it, it can be used as a relatively good learning tool.
• baalimago 10mo ago
  Well, then the formal verification will be vibe-coded as well, killing the point. More likely is the rise of test driven development, or spec driven development.
  • barishnamazov 10mo ago
    Then should we apply formal verification to the vibe coded formal verification software?
  • monkeydust 10mo ago
    What are the paradigms people are using to use AI in helping generate better specs and then converting those specs to code and test cases? The Kiro IDE from Amazon I felt was a step in the direction of applying AI across the entire SDLC
• tonyhart7 10mo ago
  that just testing code noo ??? what makes it different other than called it "verification" ???
• zkmon 10mo ago
  Any proof system or method is relative to its frame of reference, axiomatic context, known proofs etc. Modern software doesn't live in an isolated lab context, unless you are building an air-gapped HSM etc. Proof system itself would have to evolve to communicate the changing specs to the underlying software. So, the job is not done for humans yet.
• okaleniuk 10mo ago
  AI will definitely make more people thing about verification. Formal or otherwise.
• sandeepsr 10mo ago
  Formal verification does not scale, and has not scaled for 2 decades. Although, LLMs can help write properties, that required a skilled person (Phd) to write. In pre-silicon verification, formal has been used successfully for decades, but is not a replacement for simulation based verification. The future of verification (for hardware and software) is to eliminate verification all together, by synthesizing intent into correct code and tests. -- https//www.verifai.ai
  • igornotarobot 10mo ago
    Afaik, formal verification worked well for hardware because most of the things in hardware were deterministic and could be captured precisely. Most of the software these days is concurrent and distributed. Does the LLM + RL approach work for concurrent and distributed code? We barely know how to do systematic simulation there.
• swayson 10mo ago
  Really nice book on the subject -- Algorithms for Validation. The online version is actually freely accessible as a PDF. https://algorithmsbook.com/validation/ https://algorithmsbook.com/validation/
• brador 10mo ago
  Few care about software that much. Just look at the buggy messes of the last 10 years by every one of the top tech companies. Large scale software is made cheap and fast, not good.
• popcorncowboy 10mo ago
  I will respectfully take the other side of this bet on polymarket if it ever shows up.
• rcarmo 10mo ago
  I've been heearing about formal verification since college (which for me was more than 30 years ago) and I even taught a thing called "Z", which was a formally verifiable academia thing that tried to be the ultimate formal language. It never panned out, and I honestly don't think that AI is going to help in anything but test generation, which is going to remain the most pragmatic approach to formal verification (but, like all things, it's an approximation, not 100% correct).
• NackerHughes 10mo ago
  It absolutely will not make formal verification go mainstream. What it will make go mainstream, and in fact has already started to, is “ChatGPT verified it so it must be OK.”
• jon-wood 10mo ago
  Counterpoint: No it won’t. People are using LLMs because they don’t want to think deeply about the code they’re writing, why in hell would they instead start thinking deeply about the code being written to verify the code the LLM is writing?
• Meneth 10mo ago
  There are bugs in the specification, so there are bugs in the verfication as well.
• lawrpaulson 10mo ago
  Formal verification is progressing inexorably and will, over time, transform the software development experience. There is a long way to go, and in particular, you can't even get started until basic interfaces have been specified formally. This will be a bottom-up process where larger and larger components gradually get specified and verified. These will mostly be algorithmic or infrastructure building blocks, as opposed to application software subject to changing and vague requirements. I don't think LLMs will be contributing much to the verification process soon: there is nowhere near enough material available.
• runeks 10mo ago
  > As the verification process itself becomes automated, the challenge will move to correctly defining the specification: that is, how do you know that the properties that were proved are actually the properties that you cared about? Reading and writing such formal specifications still requires expertise and careful thought. But writing the spec is vastly easier and quicker than writing the proof by hand, so this is progress. How big is the effort of writing a specification for an application versus implementing the application in the traditional way? Can someone with more knowledge chime in here please?
  • ookdatnog 10mo ago
    It depends on what you're working on. If you're doing real algorithmic work, often the algorithm is a lot more complex than its spec because it needs to be fast. Take sorting a list for example. The spec is quite short. - for all xs: xs is a permutation of sort(xs) - for all xs: sorted(sort(xs)) Where we can define "xs is a permutation of ys" as "for each x in xs: occurrences(x, xs) = occurrences(x, ys)" And "sorted(l)" as "forall xs, x, y, ys: (l = xs ++ [x, y] ++ ys) => x < y". A straightforward bubble or insertion sort would perhaps be considered as simple or simpler than this spec. But the sorting algorithms in, say, standard libraries, tend to be significantly more complex than this spec.
• grahar64 10mo ago
  Won't people just use AI to define specification? Like if they are getting most of it done with AI, won't they won't read/test the code to verify won't they also not read the spec
• GTP 10mo ago
  I doubt some of this article's claims. > At present, a human with specialist expertise still has to guide the process, but it’s not hard to extrapolate and imagine that process becoming fully automated in the next few years. We already had some software engineers here on HN explain that they don't make a large use of LLMs because the hard part of their job isn't to actually write the code, but to understand the requirements behind it. And formal verification is all about requirements. > Reading and writing such formal specifications still requires expertise and careful thought. But writing the spec is vastly easier and quicker than writing the proof by hand, so this is progress. Writing the spec is easier once you are confident about having fully understood the requirements, and here we get back to the above issue. Plus, it is already the case that you don't write the proof by hand, this is what the prover either assists you with or does in full. > I find it exciting to think that we could just specify in a high-level, declarative way the properties that we want some piece of code to have, and then to vibe code the implementation along with a proof that it satisfies the specification. And here is where I think problems will arise: moving from the high level specification to the formal one that is the one actually getting formally verified. Of course, this would still be better than having no verification at all. But it is important to keep in mind that, with these additional levels of abstractions, you will likely end up with a weaker form of formal verification, so to speak. Maybe it is worth it to still verify some high assurance software "the old way" and leave this only for the cases where additional verification is nice to have but not a matter of life or death.
• ath3nd 10mo ago
  [dead]
• igornotarobot 10mo ago
  It probably will, but not the way we all imagine. What we see now is an attempt to recycle the interactive provers that took decades to develop. Writing code, experimenting with new ideas and getting feedback has always been a very slow process in academia. Getting accepted at a top peer-reviewed conference takes months and even years. The essential knowledge is hidden inside big corps that only promote their "products" and rarely give the knowledge back. LLMs enable code bootstrapping and experimentation faster not only for the vibe coders, but also for the researchers, many of them are not really good coders, btw. It may well be that we will see new wild verification tools soon that come as a result of quick iteration with LLMs. For example, I recently wrote an experimental distributed bug finder for TLA+ with Claude in about three weeks. A couple of years ago that effort would require three months and a team of three people.
• gttalbot 10mo ago
  If the AI is going to lie and cheat on the code it writes (this is the largest thing I bump into regularly and have to nudge it on), what makes the author think that the same AI won't cheat on the proof too?
• jappgar 10mo ago
  There's really no such thing as complete verification. The quest for purity is some fountain of youth nonsense that distracts a lot of otherwise brilliant engineers. Ask the AI to make a program that consumes a program and determine if it halts.
• pankalog 10mo ago
  The topic of my research right now is a subset of this; it essentially researches the quality of the outputs of LLMs, when they're writing tight-fitting DSL code, for very context-specific areas of knowledge. One example could be a low-level programming language for a given PLC manufacturer, where the prompt comes from a context-aware domain expert, and the LLM is able to output proper DSL code for that PLC. Think of "make sure this motor spins at 300rpm while this other task takes place"-type prompts. The LLM essentially needs to juggle between understanding those highly-contextual clues, and writing DSL code that very tightly fits the DSL definition. We're still years away from this being thoroughly reliable for all contexts, but it's interesting research nonetheless. Happy to see that someone also agrees with my sentiment ;-)
• 4nonverbal-alit 10mo ago
  I think as long as we don't integrate formal verification into the programs themselves, it's not going to become mainstream. Especially now you got two different pieces you need to maintain and keep in sync (whether using LLMs or not). Strong type systems are providing partial validation which is helping quite a lot IMO. The better we can model the state - the more constraints we can define in the model, the closer we'd be getting to writing "self-proven" code. I would assume formal proofs do way more than just ensuring validity of the model, but the similar approaches can be integrated to mainstream programs as well I believe.
• LurkandComment 10mo ago
  Open-banking will make formal verification go mainstream.
• victor22 10mo ago
  meds
• alexgotoi 10mo ago
  The funny part of “AI will make formal verification go mainstream” is that it skips over the one step the industry still refuses to do: decide what the software is supposed to do in the first place. We already have a ton of orgs that can’t keep a test suite green or write an honest invariant in a code comment, but somehow we’re going to get them to agree on a precise spec in TLA+/Dafny/Lean and treat it as a blocking artifact? That’s not an AI problem, that’s a culture and incentives problem. Where AI + “formal stuff” probably does go mainstream is at the boring edges: property-based tests, contracts, refinement types, static analyzers that feel like linters instead of capital‑P “Formal Methods initiatives”. Make it look like another checkbox in CI and devs will adopt it; call it “verification” and half the org immediately files it under “research project we don’t have time for”. Will include this thread in my https://hackernewsai.com/ https://hackernewsai.com/ newsletter.
  • TimTheTinker 10mo ago
    > it skips over the one step the industry still refuses to do: decide what the software is supposed to do in the first place. Not only that, but it's been well-established that a significant challenge with formally verified software is to create the right spec -- i.e. one that actually satisfies the intended requirements. A formally verified program can still have bugs, because the spec (which requires specialized skills to read and understand) may not satisfy the intent of the requirements in some way. So the fundamental issue/bottleneck that emerges is the requirements <=> spec gap, which closing the spec <=> executable gap does nothing to address. Translating people's needs to an empirical, maintainable spec of one type or another will always require skilled humans in the loop, regardless of how easy everything else gets -- at minimum as a responsibility sink, but even more as a skilled technical communicator. I don't think we realize how valuable it is to PMs/executives and especially customers to be understood by a skilled, trustworthy technical person.
    • jandrese 10mo ago
      The danger of this is people start asking about formally verified specs, and down that road lies madness. "If you can formally verify the spec the code can be auto-generated from it."
• newpavlov 10mo ago
  For all my skepticism towards using LLM in programming (I think that the current trajectory leads to slow degradation of the IT industry and massive loss of expertise in the following decades), LLM-based advanced proof assistants is the only bright spot for which I have high hopes. Generation of proofs requires a lot of complex pattern matching, so it's a very good fit for LLMs (assuming sufficiently big datasets are available). And we can automatically verify LLM output, so hallucinations are not the problem. You still need proper engineers to construct and understand specifications (with or without LLM help), but it can significantly reduce development cost of high assurance software. LLMs also could help with explaining why a proof can not be generated. But I think it would require a Rust-like breakthrough, not in the sense of developing the fundamental technology (after all, Rust is fairly conservative from the PLT point of view), but in the sense of making it accessible for a wider audience of programmers. I also hope that we will get LLM-guided compilers which generate equivalency proofs as part of the compilation process. Personally, I find it surprising that the industry is able to function as well as it does on top of software like LLVM which feels like a giant with feet of clay with its numerous miscompilation bugs and human-written optimization heuristics which are applied to a somewhat vague abstract machine model. Just look how long it took to fix the god damn noalias atrtibute! If not for Rust, it probably would've still been a bug ridden mess.
• momojo 10mo ago
  @simonw's successful port of JustHTML from python to javascript proved that an agent iteration + an exhaustive test suite is a powerful combo [0]. I don't know if TLA+ is going to suddenly appear as 'the next language I want to learn' in Stackoverflow's 2026 Developer Survey, but I bet we're going to see a rise in testing frameworks/languages. Anything to make it easier for an agent to spit out tokens or write smaller tests for itself. Not a perfect piece of evidence, but I'm really interested to see how successful Reflex[1] is in this upcoming space. [0] https://simonwillison.net/2025/Dec/15/porting-justhtml/ https://simonwillison.net/2025/Dec/15/porting-justhtml/ [1] https://github.com/reflex-dev/reflex https://github.com/reflex-dev/reflex
• cess11 10mo ago
  "As the verification process itself becomes automated, the challenge will move to correctly defining the specification" OK. "Reading and writing such formal specifications still requires expertise and careful thought." So the promise here is that bosses in IT organisations will in the future have expertise and prioritise careful thought, or allow their subordinates to have and practice these characteristics. Would be great news if that'll be the case.
• Fairburn 10mo ago
  AI is great; it makes my job easier by removing repetitive processes, freeing me up to tackle other "stuff". I like that I can have it form up json/xml scaffolding, etc. All the insanely boring stuff that can often get corrupted by human error. AI isn't the solution to move humanity forward in any meaningful way. It is just another aspect of our ability to offload labor. Which in and of itself is fine. Until of course it gets weaponized and is used to remove the human aspect from more and more everyday things we take for granted. It is being used to accelerate the divisions among people. Further separating the human from humanness. I love 'AI' tools, they make my life easier, work-wise. But would I miss it if it wasn't there? No. I did just fine before and would do so after it's flame and innovation slowly peters out.
• deleted 10mo ago
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• dwrodri 10mo ago
  I think if AI can help us modernize the current state of hardware verification, I think that would be an enormous boon to the tech industry. Server class CPUs and GPUs are littered with side channels which are very difficult to “close”, even in hardened cloud VMs. We haven’t verified “frontier performance” hardware down to the logic gate in quite some time. Prof. Margaret Martinosi’s lab and her students have spent quite some time on this challenge, and i am excited to see better, safer memory models oyt in the wild. A lot of the same big ideas used in hardware are making their way into the software later too, see https://faultlore.com/blah/tower-of-weakenings/ https://faultlore.com/blah/tower-of-weakenings/
• iamwil 10mo ago
  I wrote a post on how I dipped my toes into Lean, using LLMs to guide me. https://interjectedfuture.com/the-best-way-to-learn-might-be-starting-at-the-end/ https://interjectedfuture.com/the-best-way-to-learn-might-be... You might find it useful. I also caveat the experience and recount some of the pitfalls, which you might enjoy as a skeptic. Martin Klepmann seemed to like it too. https://bsky.app/profile/martin.kleppmann.com/post/3m7ugznx4gs2w https://bsky.app/profile/martin.kleppmann.com/post/3m7ugznx4...
• mdnahas 10mo ago
  I’m the author of a tutorial on Rocq. I’m surprised at the negativity on HN. We all want bug-free code and this is a way to not just reduce bugs, but eliminate whole classes of bugs. Moreover, with proven code, we can have rock-solid libraries and code reuse. Also, proven specifications allow LLMs to do divide-and-conquer when generating code. You can ask it to generate part A that does X, assuming part B will do Y. And then ask it to generate part B in parallel. And know that, when merged, the code will work. And, provable code is good for LLMs because it will let LLM creators study hallucinations. The proof checker can identify what is a hallucination and what is not. This means LLMs may learn what they don’t know! I’m not saying LLMs with proven code is nirvana. There are parts of systems where it doesn’t apply. Specifications are often complex and difficult to understand. Some important details are hard to write a spec for. And specs can miss things. But code proven correct by LLMs has potential to do real good.