This very much aligns with my experience — I had a case yesterday where opus was trying to do something with a library, and it encountered a build error. Rather than fix the error, it decided to switch to another library. It then encountered another error and decided to switch back to the first library.
I don’t think I’ve encountered a case where I’ve just let the LLM churn for more than a few minutes and gotten a good result. If it doesn’t solve an issue on the first or second pass, it seems to rapidly start making things up, make totally unrelated changes claiming they’ll fix the issue, or trying the same thing over and over.
Workaccount2 · 1h ago
They poison their own context. Maybe you can call it context rot, where as context grows and especially if it grows with lots of distractions and dead ends, the output quality falls off rapidly. Even with good context the rot will start to become apparent around 100k tokens (with Gemini 2.5).
They really need to figure out a way to delete or "forget" prior context, so the user or even the model can go back and prune poisonous tokens.
Right now I work around it by regularly making summaries of instances, and then spinning up a new instance with fresh context and feed in the summary of the previous instance.
codeflo · 40m ago
I wonder to what extent this might be a case where the base model (the pure token prediction model without RLHF) is "taking over". This is a bit tongue-in-cheek, but if you see a chat protocol where an assistant makes 15 random wrong suggestions, the most likely continuation has to be yet another wrong suggestion.
People have also been reporting that ChatGPT's new "memory" feature is poisoning their context. But context is also useful. I think AI companies will have to put a lot of engineering effort into keeping those LLMs on the happy path even with larger and larger contexts.
kossae · 39m ago
This is my experience as well, and for now comes down to a workflow optimization. As I feel the LLM getting off track, I start a brand new session with useful previous context pasted in from my previous session. This seems to help steer it back to a decent solution, but agreed it would be nice if this was more automated based off of user/automated feedback (broken unit test, "this doesn't work", etc.)
HeWhoLurksLate · 33m ago
I've found issues like this happen extremely quickly with ChatGPT's image generation features - if I tell it to put a particular logo in, the first iteration looks okay, while anything after that starts to look more and more cursed / mutant.
vunderba · 8m ago
gpt-image-1 is unfortunately particular vulnerable to this problem. The more you want to change the initial image - the better off you'd honestly be just starting an entirely new conversation.
rvnx · 30m ago
I've noticed something, even if you ask to edit a specific picture, it will still used the other pictures in the context (and this is somewhat unwanted)
vadansky · 56m ago
I had a particularly hard parsing problem so I setup a bunch of tests and let the LLM churn for a while and did something else.
When I came back all the tests were passing!
But as I ran it live a lot of cases were still failing.
Turns out the LLM hardcoded the test values as “if (‘test value’) return ‘correct value’;”!
vunderba · 6m ago
I've definitely seen this happen before too. Test-driven development isn't all that effective if the LLM's only stated goal is to pass the tests without thinking about the problem in a more holistic/contextual manner.
bluefirebrand · 38m ago
This is the most accurate Junior Engineer behavior I've heard LLMs doing yet
ffsm8 · 25m ago
Missed opportunity for the LLM, could've just switched to Volkswagen CI
Very common to see in comments some people saying “it can’t do that” and others saying “here is how I make it work.” Maybe there is a knack to it, sure, but I’m inclined to say the difference between the problems people are trying to use it on may explain a lot of the difference as well. People are not usually being too specific about what they were trying to do. The same goes for a lot of programming discussion of course.
alganet · 25m ago
> People are not usually being too specific about what they were trying to do. The same goes for a lot of programming discussion of course.
In programming, I already have a very good tool to follow specific steps: _the programming language_. It is designed to run algorithms. If I need to be specific, that's the tool to use. It does exactly what I ask it to do. When it fails, it's my fault.
Some humans require algorithmic-like instructions too. Like cooking a recipe. However, those instructions can be very vague and a lot of humans can still follow it.
LLMs stand on this weird place where we don't have a clue in which occasions we can be vague or not. Sometimes you can be vague, sometimes you can't. Sometimes high level steps are enough, sometimes you need fine-grained instructions. It's basically trial and error.
Can you really blame someone for not being specific enough in a system that only provides you with a text box that offers anthropomorphic conversation? I'd say no, you can't.
If you want to talk about how specific you need to prompt an LLM, there must be a well-defined treshold. The other option is "whatever you can expect from a human".
Most discussions seem to juggle between those two. LLMs are praised when they accept vague instructions, but the user is blamed when they fail. Very convenient.
heyitsguay · 59m ago
I've noticed this a lot, too, in HN LLM discourse.
(Context: Working in applied AI R&D for 10 years, daily user of Claude for boilerplate coding stuff and as an HTML coding assistant)
Lots of "with some tweaks i got it to work" or "we're using an agent at my company", rarely details about what's working or why, or what these production-grade agents are doing.
nico · 28m ago
> Rather than fix the error, it decided to switch to another library
I’ve had a similar experience, where instead of trying to fix the error, it just added a try/catch around it with a log message, just so execution could continue
skerit · 1h ago
> I don’t think I’ve encountered a case where I’ve just let the LLM churn for more than a few minutes and gotten a good result.
Is this with something like Aider or CLine?
I've been using Claude-Code (with a Max plan, so I don't have to worry about it wasting tokens), and I've had it successfully handle tasks that take over an hour.
But getting there isn't super easy, that's true. The instructions/CLAUDE.md file need to be perfect.
nico · 39m ago
> I've had it successfully handle tasks that take over an hour
What kind of tasks take over an hour?
qazxcvbnmlp · 1h ago
when this happens I do thew following
1) switch to a more expensive llm and ask it to debug: add debugging statements, reason about what's going on, try small tasks, etc
2) find issue
3) ask it to summarize what was wrong and what to do differently next time
4) copy and paste that recommendation to a small text document
5) revert to the original state and ask the llm to make the change with the recommendation as context
nico · 35m ago
> 1) switch to a more expensive llm and ask it to debug
You might not even need to switch
A lot of times, just asking the model to debug an issue, instead of fixing it, helps to get the model unstuck (and also helps providing better context)
rurp · 1h ago
This honestly sounds slower than just doing it myself, and with more potential for bugs or non-standard code.
I've had the same experience as parent where LLMs are great for simple tasks but still fall down surprisingly quickly on anything complex and sometimes make simple problems complex. Just a few days ago I asked Claude how to do something with a library and rather than give me the simple answer it suggested I rewrite a large chunk of that library instead, in a way that I highly doubt was bug-free. Fortunately I figured there would be a much simpler answer but mistakes like that could easily slip through.
accrual · 1h ago
I've had some similar experiences. While I find agents very useful and able to complete many tasks on its own, it does hit roadblocks sometimes and its chosen solution can be unusual/silly.
For example, the other day I was converting models but was running out of disk space. The agent decided to change the quantization to save space when I'd prefer it ask "hey, I need some more disk space". I just paused it, cleared some space, then asked the agent to try the original command again.
onlyrealcuzzo · 1h ago
> If it doesn’t solve an issue on the first or second pass, it seems to rapidly start making things up, make totally unrelated changes claiming they’ll fix the issue, or trying the same thing over and over.
Sounds like a lot of employees I know.
Changing out the entire library is quite amusing, though.
Just imagine: I couldn't fix this build error, so I migrated our entire database from Postgres to MongoDB...
mathattack · 1h ago
It may be doing the wrong thing like an employee, but at least it's doing it automatically and faster. :)
didgeoridoo · 54m ago
Probably had “MongoDB is web scale” in the training set.
butterknife · 1h ago
Thanks for the laughs.
the__alchemist · 1h ago
This is consistent with my experience as well.
fcatalan · 1h ago
I brought over the source of the Dear imgui library to a toy project and Cline/Gemini2.5 hallucinated the interface and when the compilation failed started editing the library to conform with it. I was all like: Nono no no no stop.
enraged_camel · 1h ago
I've actually thought about this extensively, and experimented with various approaches. What I found is that the quality of results I get, and whether the AI gets stuck in the type of loop you describe, depends on two things: how detailed and thorough I am with what I tell it to do, and how robust the guard rails I put around it are.
To get the best results, I make sure to give detailed specs of both the current situation (background context, what I've tried so far, etc.) and also what criteria the solution needs to satisfy. So long as I do that, there's a high chance that the answer is at least satisfying if not a perfect solution. If I don't, the AI takes a lot of liberties (such as switching to completely different approaches, or rewriting entire modules, etc.) to try to reach what it thinks is the solution.
prmph · 1h ago
But don't they keep forgetting the instructions after enough time have passed? How do you get around that? Do you add an instruction that after every action it should go back and read the instructions gain?
PaulHoule · 1h ago
Isn't that "modern Javascript development?"
PaulHoule · 1h ago
This was always my mental model. If you have a process with N steps where your probability of getting a step right is p, your chance of success is pᶰ, or 0 as N → ∞.
It affects people too. Something I learned halfway through a theoretical physics PhD in the 1990s was that a 50-page paper with a complex calculation almost certainly had a serious mistake in it that you'd find if you went over it line-by-line.
I thought I could counter that by building a set of unit tests and integration tests around the calculation and on one level that worked, but in the end my calculation never got published outside my thesis because our formulation of the problem turned a topological circle into a helix and we had no idea how to compute the associated topological factor.
Davidzheng · 6m ago
It kind of reminds me of this vanishing gradient problem in ML early on, where really deep layers won't train b/c you get these gradients dying midway, and the solution was to add these bypass connections (resnets style). I wonder if you can have similar solutions. Ofc I think what happens in general is like control theory, like you should be able to detect going off-course with some probability too and correct [longer horizon you have probability of leaving the safe-zone so you still get the exp decay but in larger field]. Not sure how to connect all these ideas though.
bwfan123 · 54m ago
> It affects people too. Something I learned halfway through a theoretical physics PhD in the 1990s was that a 50-page paper with a complex calculation almost certainly had a serious mistake in it that you'd find if you went over it line-by-line.
Interesting, and I used to think that math and sciences were invented by humans to model the world in a manner to avoid errors due to chains of fuzzy thinking. Also, formal languages allowed large buildings to be constructued on strong foundations.
From your anecdote it appears that the calculations in the paper were numerical ? but I suppose a similar argument applies to symbolic calculations.
PaulHoule · 25m ago
These were symbolic calculations. Mine was a derivation of the Gutzwiller Trace Formula
using integration over phase space instead of position or momentum space. Most people think you need an orthogonal basis set to do quantum mechanical calculation but it turns that "resolution of unity is all you need", that is, if you integrate |x><x| over all x you get 1. If you believe resolution of unity applies in quantum gravity, then Hawking was wrong about black hole information. In my case we were hoping we could apply the trace formula and make similar derivations to systems with unusual coordinates, such as spin systems.
There are quite a few calculations in physics that involve perturbation theory, for instance, people used to try to calculate the motion of the moon by expanding out thousands of terms that look like (112345/552) sin(32 θ-75 ϕ) and still not getting terribly good results. It turns out classic perturbation theory is pathological around popular cases such as the harmonic oscillator (frequency doesn't vary with amplitude) and celestial mechanics (the frequency to go around the sun, to get closer or further from sun, or to go above or below the plane of the plane of the ecliptic are all the same.) In quantum mechanic these are not pathological, notably perturbation theory works great for an electron going around an atom which is basically the same problem as the Earth going around the Sun.
in high energy physics because frequently they're comparing a difficult experiment to an expansion of thousands of Feynman diagrams and between computational errors and the fact that perturbation theory often doesn't converge very well I don't get excited when they don't agree.
----
Note that I used numerical calculations for "unit and integration testing", so if I derived an identity I could test that the identity was true for different inputs. As for formal systems, they only go so far. See
The amusing things LLMs do when they have been at a problem for some time and cannot fix it:
- Removing problematic tests altogether
- Making up libs
- Providing a stub and asking you to fill in the code
nico · 27m ago
- Adding try/catch block to “remove” the error and let execution continue
esafak · 28m ago
If humans can say "The proof is left as an exercise for the reader", why can't LLMs :)
einrealist · 53m ago
So as the space for possible decisions increases, it increases the likelihood of models to end up with bad "decisions". And what is the correlation between the increase in "survival rate" and the increase in model parameters, compute power and memory (context)?
__MatrixMan__ · 54m ago
I don't think this has anything to do with AI. There's a half life for success rates.
deadbabe · 1h ago
This is another reason why there’s no point in carefully constructing prompts and contexts trying to coax the right solution out of an LLM. The end result becomes more brittle with time.
If you can’t zero shot your way to success the LLM simply doesn’t have enough training for your problem and you need a human touch or slightly different trigger words. There have been times where I’ve gotten a solution with such a minimal prompt it practically feels like the LLM read my mind, that’s the vibe.
ldjkfkdsjnv · 1h ago
another article on xyz problem with LLMs, which will probably be solved by model advancements in 6/12 months.
bwfan123 · 1h ago
you left out the other llm-apology: But humans can fail too !
I don’t think I’ve encountered a case where I’ve just let the LLM churn for more than a few minutes and gotten a good result. If it doesn’t solve an issue on the first or second pass, it seems to rapidly start making things up, make totally unrelated changes claiming they’ll fix the issue, or trying the same thing over and over.
They really need to figure out a way to delete or "forget" prior context, so the user or even the model can go back and prune poisonous tokens.
Right now I work around it by regularly making summaries of instances, and then spinning up a new instance with fresh context and feed in the summary of the previous instance.
People have also been reporting that ChatGPT's new "memory" feature is poisoning their context. But context is also useful. I think AI companies will have to put a lot of engineering effort into keeping those LLMs on the happy path even with larger and larger contexts.
When I came back all the tests were passing!
But as I ran it live a lot of cases were still failing.
Turns out the LLM hardcoded the test values as “if (‘test value’) return ‘correct value’;”!
https://github.com/auchenberg/volkswagen
In programming, I already have a very good tool to follow specific steps: _the programming language_. It is designed to run algorithms. If I need to be specific, that's the tool to use. It does exactly what I ask it to do. When it fails, it's my fault.
Some humans require algorithmic-like instructions too. Like cooking a recipe. However, those instructions can be very vague and a lot of humans can still follow it.
LLMs stand on this weird place where we don't have a clue in which occasions we can be vague or not. Sometimes you can be vague, sometimes you can't. Sometimes high level steps are enough, sometimes you need fine-grained instructions. It's basically trial and error.
Can you really blame someone for not being specific enough in a system that only provides you with a text box that offers anthropomorphic conversation? I'd say no, you can't.
If you want to talk about how specific you need to prompt an LLM, there must be a well-defined treshold. The other option is "whatever you can expect from a human".
Most discussions seem to juggle between those two. LLMs are praised when they accept vague instructions, but the user is blamed when they fail. Very convenient.
(Context: Working in applied AI R&D for 10 years, daily user of Claude for boilerplate coding stuff and as an HTML coding assistant)
Lots of "with some tweaks i got it to work" or "we're using an agent at my company", rarely details about what's working or why, or what these production-grade agents are doing.
I’ve had a similar experience, where instead of trying to fix the error, it just added a try/catch around it with a log message, just so execution could continue
Is this with something like Aider or CLine?
I've been using Claude-Code (with a Max plan, so I don't have to worry about it wasting tokens), and I've had it successfully handle tasks that take over an hour. But getting there isn't super easy, that's true. The instructions/CLAUDE.md file need to be perfect.
What kind of tasks take over an hour?
1) switch to a more expensive llm and ask it to debug: add debugging statements, reason about what's going on, try small tasks, etc 2) find issue 3) ask it to summarize what was wrong and what to do differently next time 4) copy and paste that recommendation to a small text document 5) revert to the original state and ask the llm to make the change with the recommendation as context
You might not even need to switch
A lot of times, just asking the model to debug an issue, instead of fixing it, helps to get the model unstuck (and also helps providing better context)
I've had the same experience as parent where LLMs are great for simple tasks but still fall down surprisingly quickly on anything complex and sometimes make simple problems complex. Just a few days ago I asked Claude how to do something with a library and rather than give me the simple answer it suggested I rewrite a large chunk of that library instead, in a way that I highly doubt was bug-free. Fortunately I figured there would be a much simpler answer but mistakes like that could easily slip through.
For example, the other day I was converting models but was running out of disk space. The agent decided to change the quantization to save space when I'd prefer it ask "hey, I need some more disk space". I just paused it, cleared some space, then asked the agent to try the original command again.
Sounds like a lot of employees I know.
Changing out the entire library is quite amusing, though.
Just imagine: I couldn't fix this build error, so I migrated our entire database from Postgres to MongoDB...
To get the best results, I make sure to give detailed specs of both the current situation (background context, what I've tried so far, etc.) and also what criteria the solution needs to satisfy. So long as I do that, there's a high chance that the answer is at least satisfying if not a perfect solution. If I don't, the AI takes a lot of liberties (such as switching to completely different approaches, or rewriting entire modules, etc.) to try to reach what it thinks is the solution.
It affects people too. Something I learned halfway through a theoretical physics PhD in the 1990s was that a 50-page paper with a complex calculation almost certainly had a serious mistake in it that you'd find if you went over it line-by-line.
I thought I could counter that by building a set of unit tests and integration tests around the calculation and on one level that worked, but in the end my calculation never got published outside my thesis because our formulation of the problem turned a topological circle into a helix and we had no idea how to compute the associated topological factor.
Interesting, and I used to think that math and sciences were invented by humans to model the world in a manner to avoid errors due to chains of fuzzy thinking. Also, formal languages allowed large buildings to be constructued on strong foundations.
From your anecdote it appears that the calculations in the paper were numerical ? but I suppose a similar argument applies to symbolic calculations.
https://inspirehep.net/files/20b84db59eace6a7f90fc38516f530e...
using integration over phase space instead of position or momentum space. Most people think you need an orthogonal basis set to do quantum mechanical calculation but it turns that "resolution of unity is all you need", that is, if you integrate |x><x| over all x you get 1. If you believe resolution of unity applies in quantum gravity, then Hawking was wrong about black hole information. In my case we were hoping we could apply the trace formula and make similar derivations to systems with unusual coordinates, such as spin systems.
There are quite a few calculations in physics that involve perturbation theory, for instance, people used to try to calculate the motion of the moon by expanding out thousands of terms that look like (112345/552) sin(32 θ-75 ϕ) and still not getting terribly good results. It turns out classic perturbation theory is pathological around popular cases such as the harmonic oscillator (frequency doesn't vary with amplitude) and celestial mechanics (the frequency to go around the sun, to get closer or further from sun, or to go above or below the plane of the plane of the ecliptic are all the same.) In quantum mechanic these are not pathological, notably perturbation theory works great for an electron going around an atom which is basically the same problem as the Earth going around the Sun.
I have a lot of skepticism about things like
https://en.wikipedia.org/wiki/Anomalous_magnetic_dipole_mome...
in high energy physics because frequently they're comparing a difficult experiment to an expansion of thousands of Feynman diagrams and between computational errors and the fact that perturbation theory often doesn't converge very well I don't get excited when they don't agree.
----
Note that I used numerical calculations for "unit and integration testing", so if I derived an identity I could test that the identity was true for different inputs. As for formal systems, they only go so far. See
https://en.wikipedia.org/wiki/Principia_Mathematica#Consiste...
- Removing problematic tests altogether
- Making up libs
- Providing a stub and asking you to fill in the code
If you can’t zero shot your way to success the LLM simply doesn’t have enough training for your problem and you need a human touch or slightly different trigger words. There have been times where I’ve gotten a solution with such a minimal prompt it practically feels like the LLM read my mind, that’s the vibe.