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ProgramBench: Can language models rebuild programs from scratch?
- vatsachak 5mo agoIn before "but they did not use my agent swarm"
- makerofthings 5mo agoIt’s the annoying thing about AI. If it works, the AI is magic. If it doesn’t work, you’re using it wrong.
- NitpickLawyer 5mo agoSo, would you change your view if someone else runs this bench w/ a different harness and gets better results?
- riffraff 5mo agoIt was the same thing with OOP, TDD, agile development, C, C++, Rust, ORMs.. Whenever something impacts a ton of people you will get some who gain a lot from it and some who don't, and they're generally unable to relate to the other side. Maybe the thing works in some domain and not the other. Maybe the two groups are doing different things. Maybe the context around it is different. Maybe they have a different definition of "better". I think it helps to keep an open mind and not grow attached to either position, but rather inquire, "well we did X with outcome Y, what did you do instead?"
- red75prime 5mo agoIn science N=1 is statistically insignificant. In business it might mean that you have a product.
- keyle 5mo agoHow long until AI is not even writing code but producing machine code? Think about it, all these compilers, tooling, what a waste! I imagine a future where chipset makers will provide a model you can just prompt to "act upon that chipset" and voila, "You're absolutely right! Here is your binary." We won't be developers, we won't be devops, we'll be rollmops! /s
- _pdp_ 5mo agoCoding agents can write ASM. But if you mean writing the actual byte-code that will require a very different approach at a very different level of abstraction that LLMs are not designed to do. Keep in mind that all LLMs are trained first on text and then fine-tuned on code.
- quinnjh 5mo agoMy hunch is that it would take years of hundreds of thousands of developers working with machine code, posting stackoverflow questions with machine code, and publishing github repos written on it with documentation. Thats all the free labor LLMs leveraged to use high level langs. >We won't be developers, we won't be devops, we'll be modelops! /s I can still see this happening with higher level langs. the thing is the compiler is not replaced in the training data, more likely LLMs will give rise to semideterministic layers on the compilers I could see nvidia achieving this first with how nice the devex is with CUDA
- osti 5mo agoI heard they are already proficient at assembly languages.
- aforwardslash 5mo agoThey are - probably more proficient than with some high-level languages. I've used it for embedded stuff, including TI sitara PRU assembly, with great results. Frontier models can also easily "learn" directly from the manuals; asm is quite easy for them to pick up due to its "flat" (non-structured) nature.
- _pdp_ 5mo agoI am not surprised but this one sticks out... > Models favor monolithic, single-file implementations that diverge sharply from human-written code. Well, all of our code is monolithic with some files close 20K lines of code and we do use coding agents - not for the original code but as of late. I've always had that hunch that splitting everything into tiny files does not improve AI coding agent performance although it feels counterintuitive due to model context constraints. To me the important parts of a program should be clustered together so the implementation is obvious. Scattering the implementation in various files all over the source tree does not help much building the mental model. That also closely match how software used to be written in the past too.
- BurningPenguin 5mo agoKinda surprising to me, since i had some trouble with Cursor & Co. once the file went over ~800 lines. It repeatedly failed to edit it, until i split it up into multiple logical components. As it should have been from the beginning... Though, it was some time ago, so things might have improved?
- _pdp_ 5mo agoVSCode basically any model can edit the 20K file without any issues. The coding harness does not read the entire file at once though. It reads chunks of it so the size does not really matter. What matters is how close are the things the agent needs to make the edit.
- tnelsond4 5mo agoYeah, that was my experience with Grok, whenever I gave it a file with over 400 lines it would just fail to comprehend it or be too lazy to write too much at a time. Splitting stuff up into separate files helped.
- Garlef 5mo ago> Scattering the implementation in various files all over the source tree If you treat the source tree seriously, you can communicate a lot with how it is structured
- luca-ctx 5mo agoRE: monolithic, single-file implementations We have a lint that caps source code files at 650 LOC and it works really well.
- deleted 5mo ago[deleted]
- miguel_martin 5mo agoIt’s unfortunate that they didn’t eval using subagents/orchestration for such a complex set of tasks (from what I can tell), e.g. analyze program to produce initial spec -> code -> review and rinse&repeat with each of those steps being a separate subagent allocated I would be interested to see if there’s a significant quantifiable difference.
- NitpickLawyer 5mo agoThis might actually be the whole value prop of this benchmark. Forget their initial scores, take open models (so we can be sure the base doesn't change), and test different combinations of harness + prompts + strategies + whatever memthing is popular today. See if the scores improve. Repeat.
- andy12_ 5mo agoIt's interesting that Figure 4 shows that Sonnet and Opus have a very clear distinct curve from all other models, even from GPT 5.4. Anthropic superiority I guess.
- behaviors 5mo agoIt's funny, because that task is very diverse. Any LLM will use the codebase given as a template(At least in free-tier models) My software as a contract of behaviors works like a program bench(I even cross tested buildouts) Made an entire corpus layout for multi agent multi platform builds to be compared. Even went ahead and ran 50 contracts for an example. It honestly showed improvable areas, and distinct differences between model code. {contract_name}/ └── submissions/ └── {date}_{os}_{agent}_{model}_{stack}/ ├── {contract}.osc.md ├── osc.osc.md └── results/ └── {contract}.snapshot.json That's it, compare to the same contract, or find a new contract to use to compare. Lot's of signed/hash pinned files are all you need to reproduce software from nothing, with an LLM. Programbench is close to that(they have a nice paper/article here. But I don't like the work used. Having software to start with is not a bench of making code but reverse engineering. github/s1ugh34d/osc
- tadamcz 5mo agoNice work once again from Ofir Press and team; this seems to be an idea that's in the air. > Our 200 tasks range from compact CLI tools to widely used software such as FFmpeg, SQLite, and the PHP interpreter. We evaluate 9 LMs and find that none fully resolve any task Fwiw, this is very different from what we find in MirrorCode: > Opus 4.6 successfully reimplements almost every program up to gotree’s size in our benchmark. https://epoch.ai/blog/mirrorcode-preliminary-results https://epoch.ai/blog/mirrorcode-preliminary-results I don't have time right now to dig in to what could explain the difference (I'm working hard on getting the full MirrorCode out as soon as possible). But I suspect that the ProgramBench authors are either under-eliciting the AIs, or their tasks are unfair/impossible given the constraints, or both. I hope to look more into it after releasing MirrorCode, and write up my conclusions.
- LeCompteSftware 5mo agoSurely the biggest difference is that you guys are mostly testing LLMs on simpler utilities, mostly involving higher-level languages, whereas ProgramBench are all very complex C programs (and much older programs with much more comprehensive test cases). Eg cal is totally routine. I would expect most sophomores to be able to write a perfectly good cal. In fact the only program you tested which actually has anywhere close to the complexity of SQLite or FFmpeg is is Pkl, and it looks like Opus 4.6 totally failed. I think your results are consistent. You're just measuring different things. Your benchmarks mostly tests LLMs ability to write technically routine programs of moderate length - yes the bioinformatics package involves specialized domain knowledge, but not specialized Go engineering. ProgramBench is harder.
- tadamcz 5mo agoI don't think so. ProgramBench authors say no LLMs fully resolve any task, i.e. even the easiest tasks in their benchmark are unsolved. Whereas we found Opus 4.6 successfully reimplements almost every program up to gotree’s size (around 15-20 of them). For Pkl, the preliminary results only went up to 1bn total tokens (costing $550, which would be cheap if LLMs could do the task). It might very well be solved at higher token budgets; see the report for more discussion of this. The preliminary results are just on 4 targets. We have several Pkl-level and harder tasks in the full set which we're releasing soon. In the following quote multiple things are not quite right: > mostly involving higher-level languages, whereas ProgramBench are all very complex C programs (and much older programs with much more comprehensive test cases). First, as I said above I think you're confusing the top-end of ProgramBench difficulty with the average. The quote in the OP is pretty clear that FFmpeg, SQLite, and PHP are the 3 hardest out of 200 in ProgramBench, and the bottom end is "compact CLI tools". Second, I don't see the relevance of C vs higher-level languages, how does this make ProgramBench harder? Third, for the test cases, I think you might be labouring under a misapprehension about how MirrorCode works? MirrorCode uses end-to-end tests from a variety of sources (the original program’s test suites, real-world data, and LLM-assisted generation). End-to-end means the stdout/stderr has to match exactly for each test case.
- weinzierl 5mo ago"Models favor monolithic, single-file implementations that diverge sharply from human-written code." You say! I might have been just an LLM all along without even knowing it since I too prefer single file implementations. Back in the old VB5/VB6 days Visual Studio had this mode where it showed the different functions in a file almost as if they were separate files. You could not scroll beyond the functions end but you could easily transition between that mode and global file view. I always found that a nice way of working (but admittedly the world was a lot simpler back then). Also my preference for fewer but longer files is only there when I write the code myself. For working with AI I think smaller files are beneficial for quicker turn around between human and machine.
- rullopat 5mo agoI think it's one (but not the only) reason that makes LLMs work very well with Ruby on Rails
- bmn__ 5mo agoThis VB feature existed to accommodate programmers coming from the DOS based QB IDE who were used to the one function per screen view there. To my sensibilities, it does not make much sense with the advent of high-resolution desktop environments.
- jongjong 5mo agoThis has been my preference as well. I build everything in one file until it becomes uncomfortable and only then I start breaking up into multiple files... But even then, I try to keep the main business logic fully visible in the main file.
- tmtvl 5mo agoHow often has there been a HN submission for a project 'in a single C header file'?
- kibwen 5mo agoThis has less to do with natural opinions regarding code organization and more to do with the fact that including, modularizing, and distributing C code has historically been a pain in the ass which is ameliorated by shoving everything into a single file.
- adrian_b 5mo ago> Open internet with cheating detection => cheating is widespread, 20-36% of tasks are flagged for the stronger models, with source code lookup accounting for the majority of the violations. Therefore: > blocking internet access entirely is the appropriate default for ProgramBench The fact that your Anthropic coding assistant has a tendency to search on the Internet code to be inserted into your program may count for an additional copyright violation (besides the possibility of reproducing recognizable fragments of its training data). (I do not agree that copyright, at least in its current form, should be applicable to computer programs, but it is weird that the same companies who try to exploit copyrights against others also insist on the use of coding assistants that are a workaround against copyright laws, which is the main reason why they can increase programming productivity, because they may cut and paste code that you are not allowed to copy yourself.)
- endymi0n 5mo ago[dead]
- whattheheckheck 5mo agoIf a photo cannot be copyrighted then dark factory code wont be either.
- adrian_b 5mo agoThe output of a coding assistant cannot be copyrighted, but it may contain code from which the copyright has been removed and which is used in a manner incompatible with the original license. Even the more permissive licenses, like BSD, MIT, etc., forbid the removal of the copyright notice when the code is reused. While this may also happen with the source programs used for training, I was not aware about the behavior described in TFA for the Anthropic agents, which may search the Internet for source code applicable to the problem that must be solved. It seems even more likely that such code will not be used as allowed by its license.
- whattheheckheck 5mo ago
- sigmar 5mo agoNeat research. I find figure 11 interesting. The models behave so differently there. imo the benchmark should be named Can_It_Pull_a_CharDet_Bench
- killerstorm 5mo agoIt's a very misleading: they don't provide any meaningful documentation/requirements. Just an executable blackbox. E.g. a doc for ffmpeg, which I checked by downloading docker image they provide to the model, is a README which basically just says this is ffmpeg and docs can be found online. They do not allow models to get online. So a model is supposed to reverse-engineer a blackbox using only limited number of tries. I'm not sure even ASI can do this under these constraints (without memorizing the ffmpeg code base, obviously.) In the only posts one of authors mentions "usage docs". Obviously they had a command-line tool like `grep` in mind -- where a man page sort-of specifies program behavior. But then added sqlite, ffmpeg, php, etc. - where a usage doc is like one millionth of information you need to implement ffmpeg. And, of course, there's no human baseline. I'd guess making such a baseline would cost billions of dollars.
- thomashop 5mo agoi thought the agent can execute real ffmpeg to compare
- killerstorm 5mo agoI think you underestimate complexity of audio & video encoding standards. There are hundreds and hundreds of pages of specification. How many times do you need to execute real ffmpeg to get all tiny details? It's certainly possible to reverse-engineer it from a blackbox access, but it would take *years* and this test has a time limit.
- astrange 5mo agoffmpeg also includes many formats with no standards that were reverse-engineered in the first place.
- GorbachevyChase 5mo agoEven given that I think solving the problem would require a certain amount of personal agency and volition to drive useful experimentation, and then you still have an inescapable problem that a design process is never verifiably done; it just a sense of taste when a product is good enough and it’s time to stop working on it. I’m not sure this benchmark is even very interesting because it requires a language model do something that it really cannot do. Maybe it would be possible with a novel harness in an ensemble system, but I would never expect a pure language model that is run in a minimal harness to ever be able to do this.
- srijanshukla18 5mo agoThis is not a serious benchmark, come on. Tomorrow I'm launching a benchmark where I check if an LLM can build a Airbus A320 from scratch without internet. (Spoiler: no LLM succeeds)
- casey2 5mo agoPreinternet people would routinely re-implement unix and get shell scripts working across systems. This benchmark shows that agentic LLMs can't even do that, not just for complex programs and scripts, but for simple programs and simple scripts. 0%. Which fits with claudes' inability to write a c compiler.
- brunoborges 5mo agoI wonder if a model that does not know anything about a hypothetical programming language X, could write code once given said language X specification, APIs, and SDK tools and their documentation. Meaning: the model has no idea, no access to examples, no previous codebase trained on, nothing, for language X. But it knows English, it knows how to program in general (training data does contain other programming languages), and everything we expect from LLMs today. It just doesn't know jack about language X.
- themafia 5mo agoSuggested alternative title for the paper: Can American corporate desires finally kill community based open source once and for all? I mean, it seems clear to me, companies hate the GPL, and they're willing to play these games to try to get that code into their hands under the MIT license and they're happy to use these thinly disguised methods to get it. I see all these absurd ideas as part and parcel of this larger strategy. I find the current state of affairs disgusting.
- arian_ 5mo ago[flagged]
- frmsaul 5mo agoIs it impossible? https://www.lesswrong.com/posts/3pdyxFi6JS389nptu/is-programbench-impossible https://www.lesswrong.com/posts/3pdyxFi6JS389nptu/is-program...
- porterbaseball 5mo agoI was curious about the variance of the output and I made some runs w/ deepseek v4 flash and found that it was pretty high? There's also a possibility of strong model memorization on the tasks, I saw a header w/ the authors generated in one of the runs for the cmatrix task (one of 3 of 200 tasks I selected to re-evaluate). Additionally curious if anyone else thinks that passing in the gold executable as part of the task lowers the usefulness of this benchmark. caveats: N=5 on my runs and I used my own generalized task prompt