4 ms·
“Our systematic study exposes a phenomenon of constraint decay in LLM-based coding agents. While current models excel at unconstrained generation, their perform
by jdlshore 5mo ago
“Our systematic study exposes a phenomenon of constraint decay in LLM-based coding agents. While current models excel at unconstrained generation, their performance drops when forced to navigate explicit architectural rules. For end-users, this dichotomy implies that agents are reliable for rapid prototyping but remain unreliable for production-grade backend development.”
One major weakness of this study is that they didn’t fully test frontier models for cost reasons, so the specific performance results should be taken with a grain of salt. But the overall conclusion that models degrade when both behavior and architecture must be correct is interesting, and something to keep an eye on.
- qsort 5mo agoI think it's downstream of "you can't optimize for two different objectives". If you only have functional requirements, then in effect you're doing some form of program synthesis, and RL can optimize that very hard. If you have a mixture of functional and non-functional requirements, you are basically giving the model an incomplete specification, and it must in some way guess at the user's intent to fill in the blanks. This is also why adding to the prompt examples of the style of code you want (hats off to antirez for this particular tip ;)) is phenomenally powerful.
- apsurd 5mo agoWould you mind sharing antirez' suggestion?
- qsort 5mo agoI am obviously paraphrasing, but the general idea is that trying to synthesize style from a codebase into e.g. a markdown guide generally doesn't work very well. What achieves style transfer is providing the model with a lot of examples of the style, conventions, patterns you want. To put it in practice: if you point claude/codex to a repository and you ask it to implement feature X using style guide Y, the code will probably work, but you can usually get better results by saying "do it in the style of this file, it was done well there".
- brandensilva 5mo agoRight more simply put it's great at being a copy cat, exploring similar data points that match your token needs. It is not great at decision making or judgment calls that don't have a well defined spec or plan in place yet; like unofficial or unapproved tokens if you will. A lot of this stuff simply never has had specs as it has been internal to how companies work and their secret sauce. The closest thing we have are governance and compliance policies due to legal/business needs requiring it so it's far more well documented than operational ones in how we work. It is more about the how versus the what here I guess is what I'm saying. But yeah this is why it does great when there are tests, design systems, evals, and other artifacts to mirror. Far more reckless and unpredictable without these things, but still great for exploration and finding the data output you seek.
- withinboredom 5mo agoDoesn't that make sense? Its text prediction. If you give it examples, it can predict. Synthesizing "put semi-colons on new lines" requires it to generate its own examples 'in its head' (so to speak) and remember that. It won't. It's like when I see people feeding it a whole bunch of "best practices" and expect it to follow them. It won't. But you could ask it questions about the best practices all day long.
- brandensilva 5mo agoYes, exactly. Any engineer deep on this stuff right now understands that grounded predictive engine sprinkled with RL training and are discovering what that means in terms of its strengths and weaknesses for company use.
- dnautics 5mo agoSupposing an unspecified or poorly specified function f(x), and example "f(A)=>B", "given C tell me what f(C) is" lies at the core of creativity. Idk, calling it "just text prediction " seems unfairly dismissive of this capability
- gib444 5mo ago> ... This is also why adding to the prompt examples of the style of code you want ... You could take it a step further and put the example code into source code files...and be like, super comprehensive with your examples ... ;)
- rco8786 5mo agoWell yes, ideally. But real world codebases aren't clean enough to be used as the example ideal. Styles change over time, there are always code migrations and refactors in flight, legacy code exists, etc. Using specific examples of what you expect the LLM (and humans) to do now is necessary.
- jeremyjh 5mo agoEven the strongest frontier model they used - GPT 5.2 - I would consider barely usable for agentic programming. I’m not really interested in analysis of the weaknesses of such models because in my experience many weaknesses disappear entirely as models get stronger and reasoning effort is turned up. Especially if you tell them what you want them to do. Also, it’s not surprising to learn that when more acceptance criteria are added the failure rate increases.
- sigbottle 5mo agoWait isn't gpt 5.2 good? Or is it not thinking / not codex? 5.2 was what sparked the late 2025 openai agentic programming revolution.
- mkozlows 5mo ago5.2 still had a Codex variant, which this doesn't describe using. It also notably is not using the Codex harness -- it does everything with open source harnesses (which obviously are worse). And while it uses two harnesses with its cheap models, it only uses the worse-performing one of those with GPT 5.2 for cost reasons. (They also don't specify effort/thinking level used for GPT 5.2, but given that it performs worse in their baseline testing than obviously non-SOTA models, I'm guessing it wasn't set to anything high.)
- nozzlegear 5mo agoOldheads remember when GPT 5.2 was at the forefront of agentic programming. December 2025 feels like eons ago, but alack it was an entire half year!
- ipaddr 5mo agoIf I'm not using got 5.5 high reasoning I'm wasting time.
- nozzlegear 5mo agoWell, maybe so, but how did you feel about 5.2 when it was OpenAI's frontier model? That's what I'm getting at – it was the equivalent of your gpt 5.5 high reasoning just six months ago.
- nijave 5mo agoHmm, I have some anecdotal evidence this is true. Interactively working out a plan with Opus on multiple occasions it'd come up with an incompatible solution, I'll add additional context/requirements, and it has a tendency to "anchor" on it's original architecture and struggles to adapt. Sometimes it tries to sneak in changes for the original plan anyway.
- whstl 5mo agoOpus does this waaaay too much for my taste. It works fine for vibe-coders but for technical work it is infuriating.
- UncleEntity 5mo agoI think the problem is they take the shortest path to the goal ...which may or may not coincide with what you have planned. Oh, and generally think instructions are merely suggestions and what you really want this this totally different thing and not the one in the plan you handed them plus, as a stoke of good luck, this other system is a lot easier to implement as well. I mean, I spend more tokens having them clean up all the places they didn't follow the the plan (if I catch it) or implementing what came out of a 'complete and tested' previous plan where they just stop as soon as all the pathetic new test pass and you discover half of it isn't even there when trying to implement the next thing on top of it. Though... I have been conducting an experiment, of sorts, where we've been cooking on these fairly complicated projects and I don't ever touch a single line of code, just yell at them a lot, and with suitable amounts of marijuana (they are very frustrating most of the time) it's been going pretty well. I also helps that they need to explain what they're doing to somebody fairly-baked -- maybe not such an HR friendly plan?
- xienze 5mo ago> their performance drops when forced to navigate explicit architectural rules Even the best models have trouble adhering to stuff as mundane as rules for how to style generated code (indent this much, name things with these patterns, etc.). Even the most die-hard AI-first coder will admit to that kind of stuff being not unheard-of. Yet they still delude themselves into thinking that these models will follow a sufficiently detailed spec to the letter, every time.
- Animats 5mo agoThat may be the same problem seen when prompts try to force "alignment" or "guardrails". There's a performance drop. Seemingly, a big chunk of the potential solution space has been made unreachable. For example, if you apply "guardrails" to an image generator of about a year ago, all the people start looking alike. Story generators start using only a few standard names. That was last year. Is it happening with the frontier models?
- zdragnar 5mo agoI've noticed something similar with AI assist authored books as well. Early on it does alright, but after some chapters the beginning of each chapter repeats the end of the previous, and obvious LLM tells become more frequent. The more it has to go on, the more it relies on repetition of what came before. It's also possible that authors start paying much less attention and put less effort into editing later chapters. Despite the sheer volume on Amazon, LLMs are not at the point of writing well.
- deleted 5mo ago[deleted]