| ▲ | ProgramBench: Can Language Models Rebuild Programs from Scratch?(arxiv.org) |
| 32 points by jonbaer 4 hours ago | 19 comments |
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| ▲ | _pdp_ 2 hours ago | parent | next [-] |
| I 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. |
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| ▲ | Garlef an hour ago | parent | next [-] | | > 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 | | |
| ▲ | _pdp_ an hour ago | parent [-] | | Well you can communicate organisation structure but not logic or intent. The directory is a tree and the Code is a graph. You can communicate some information by looking at the org chart of a company but it does not really tell you much how it works. Arguably a coding agent is less concerned about where the files are at then the code itself. |
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| ▲ | doix 6 minutes ago | parent | prev | next [-] | | > 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. Yeah, that happens where I work and I hate it. A combination of lint rules and AI reviewer prompts complain about long files and long functions. This means something that could be a 300 line self contained function that could be read linearly gets split up into 6 functions across 6 files. It's the illusion of "clean code". If you're casually skimming the code, you feel good. But as soon as you go beyond the surface level it becomes annoying. | |
| ▲ | BurningPenguin 2 hours ago | parent | prev [-] | | Kinda 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_ an hour ago | parent [-] | | VSCode 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. |
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| ▲ | miguel_martin an hour ago | parent | prev | next [-] |
| It’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. |
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| ▲ | NitpickLawyer an hour ago | parent [-] | | This 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. |
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| ▲ | vatsachak 2 hours ago | parent | prev | next [-] |
| In before "but they did not use my agent swarm" |
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| ▲ | red75prime an hour ago | parent | next [-] | | In science N=1 is statistically insignificant. In business it might mean that you have a product. | |
| ▲ | makerofthings 2 hours ago | parent | prev [-] | | It’s the annoying thing about AI. If it works, the AI is magic. If it doesn’t work, you’re using it wrong. | | |
| ▲ | riffraff an hour ago | parent | next [-] | | It 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?" | |
| ▲ | NitpickLawyer an hour ago | parent | prev [-] | | So, would you change your view if someone else runs this bench w/ a different harness and gets better results? |
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| ▲ | luca-ctx 2 hours ago | parent | prev | next [-] |
| RE: monolithic, single-file implementations We have a lint that caps source code files at 650 LOC and it works really well. |
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| ▲ | keyle 2 hours ago | parent | prev [-] |
| How 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 |
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| ▲ | _pdp_ 2 hours ago | parent | next [-] | | Coding 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 2 hours ago | parent | prev [-] | | My 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 2 hours ago | parent [-] | | I heard they are already proficient at assembly languages. |
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