It's nice to see a paper that confirms what anyone who has practiced using LLM tools already knows very well, heuristically. Keeping your context clean matters, "conversations" are only a construct of product interfaces, they hurt the quality of responses from the LLM itself, and once your context is "poisoned" it will not recover, you need to start fresh with a new chat.
My experiences somewhat confirm these observations, but I also had one that was different. Two weeks of debugging IPSEC issues with Gemini. Initially, I imported all the IPSEC documentation from OPNsense and pfSense into Gemini and informed it of the general context in which I was operating (in reference to 'keeping your context clean'). Then I added my initial settings for both sides (sensitive information redacted!). Afterwards, I entered a long feedback loop, posting logs and asking and answering questions.
At the end of the two weeks, I observed that: The LLM was much less likely to become distracted. Sometimes, I would dump whole forum threads or SO posts into it, when it said "this is not what we are seeing here, because of [earlier context or finding]. I eliminated all dead ends logically and informed it of this (yes, it can help with the reflection, but I had to make the decisions). In the end, I found the cause of my issues.
This somewhat confirms what some user here on HN said a few days ago. LLMs are good at compressing complex information into simple one, but not at expanding simple ideas into complex ones. As long as my input was larger than the output (either complexity or length), I was happy with the results.
I could have done this without the LLM. However, it was helpful in that it stored facts from the outset that I had either forgotten or been unable to retrieve quickly in new contexts. It also made it easier to identify time patterns in large log files, which helped me debug my site-to-site connection. I also optimized many other settings along the way, resolving not only the most problematic issue. This meant, in addition to fixing my problem, I learned quite a bit. The 'state' was only occasionally incorrect about my current parameter settings, but this was always easy to correct. This confirms what others already saw: If you know where you are going and treat it as a tool, it is helpful. However, don't try to offload decisions or let it direct you in the wrong direction.
Overall, 350k Tokens used (about 300k words). Here's a related blog post [1] with my overall path, but not directly corresponding to this specific issue. (please don't recommend wireguard; I am aware of it)
Recently, Gemini helped me fix a bug in a PPP driver (Zephyr OS) without prior knowledge of PPP or even driver development really. I would copy-paste logs of raw PPP frames in HEX and it would just decode everything and explain the meaning of each bytes. In about an hour, I knew enough about PPP to fix the bug and submit a patch.
That's some impressive prompt engineering skills to keep it on track for that long, nice work! I'll have to try out some longer-form chats with Gemini and see what I get.
I totally agree that LLMs are great at compressing information; I've set up the docs feature in Cursor to index several entire large documentation websites for major libraries and it's able to distill relevant information very quickly.
This matches my experience exactly. "poisoned" is a great way to put it. I find once something has gone wrong all subsequent responses are bad. This is why I am iffy on ChatGPT's memory features. I don't notice it causing any huge problems but I don't love how it pollutes my context in ways I don't fully understand.
It's interesting how much the nature of LLMs fundamentally being self recursive next token predictors aligns with the Chinese Room experiment. [1] In such experiment it also makes perfect sense that a single wrong response would cascade into a series of subsequent ever more drifting errors. I think it all emphasizes the relevance of the otherwise unqualifiable concept of 'understanding.'
In many ways this issue could make the Chinese Room thought experiment even more compelling. Because it's a very practical and inescapable issue.
I find using tools like LMStudio, which lets you edit your chat history on the fly, really helps deal with this problem. The models you can host locally are much weaker, but they perform a little better than the really big models once you need to factor in these poisoning problems.
A nice middle-ground I'm finding is to ask Claude an initial conversation starter in its "thinking" mode, and then copy/paste that conversation into LMStudio and have a weaker model like Gemma pick-up from where Claude left off.
The #1 tip I teach is to make extensive use of the teeny-tiny mostly hidden “edit” button in ChatGPT and Claude. When you get a bad response, stop and edit to get a better one, rather than letting crap start to multiply crap.
Hear hear! Basically if the first reply isn't good/didnt understand/got something wrong, restart from the beginning with a better prompt, explaining more/better. Rinse and repeat.
I've been saying for ages that I want to be able to fork conversations so I can experiment with the direction an exchange takes without irrevocably poisoning a promising well. I can't do this with ChatGPT, is anyone aware of a provider that offers this as a feature?
Google AI studio, ChatGPT and Claude all support this. Google AI studio is the only one that let's you branch to a separate chat though. For ChatGPT and claude you just edit the message you want to branch from.
I once built something like this for fun as a side project.
You can highlight some text in a chat and fork the chat to talk about that text selection, so the LLM has context of that along with the previous chat history and it responds in a new chat (entire chat history up to that point from the parent chat gets copied over - basically inspired by the Unix `fork`).
Your text selection from the parent chat would get turned into a hyperlink to the new child chat so you can always get to it again if you're reading the parent chat.
I need to think about this a bit more, but I think I would love a thread feature in ChatGPT, so that it has the context up to the point of creation but doesn’t affect the main conversation. It would help in two ways, it keeps the main topic from getting poisoned , and allow me to minimise text clutter when i go off on tangents during the conversation.
On Openrouter you can delete previous answers (and questions) and maintain a separate conversation with different models.
But it would indeed be nice to either disable answers (without deleting them) or forking a conversation. It wouldn't be hard to implement; I wonder if there's a market for just this?
I stumbled upon this issue myself when designing prompts for agentic systems and got mad at the lack of tools to support this flow, so I built one myself! I called it Experiment, it allows easy conversation forking and editing while retaining all logs.
An interesting little example of this problem is initial prompting, which is effectively just a permanent, hidden context that can't be cleared. On Twitter right now, the "Grok" bot has recently begun frequently mentioning "White Genocide," which is, y'know, odd. This is almost certainly because someone recently adjusted its prompt to tell it what its views on white genocide are meant to be, which for a perfect chatbot wouldn't matter when you ask it about other topics, but it DOES matter. It's part of the context. It's gonna talk about that now.
> This is almost certainly because someone recently adjusted its prompt to tell it what its views on white genocide are meant to be
Well, someone did something to it; whether it was training, feature boosting the way Golden Gate Claude [0] was done, adjusting the system prompt, or assuring that it's internet search for contextual information would always return material about that, or some combination of those, is neither obvious nor, if someone had a conjecture as to which one or combination it was, easily falsifiable/verifiable.
> This is almost certainly because someone recently adjusted its prompt to tell it what its views on white genocide are
Do you have any source on this? System prompts get leaked/extracted all the time so imagine someone would notice this
Edit: just realized you’re talking about the Grok bot, not Grok the LLM available on X or grok.com. With the bot it’s probably harder to extract its exact instructions since it only replies via tweets. For reference here’s the current Grok the LLM system prompt: https://github.com/asgeirtj/system_prompts_leaks/blob/main/g...
Probably because it is now learning from a lot of videos posted on X by misc right-wingers showing rallying cries of South African politicians like Julius Malema, Paul Mashatile etc. Not very odd.
Has any interface implemented a .. history cleaning mechanism? Ie with every chat message focus on cleaning up dead ends in the conversation or irrelevant details. Like summation but organic for the topic at hand?
Most history would remain, it wouldn’t try to summarize exactly, just prune and organize the history relative to the conversation path?
I've had success having a conversation about requirements, asking the model to summarize the requirements as a spec to feed into a model for implementation, then pass that spec into a fresh context. Haven't seen any UI to do this automatically but fairly trivial/natural to perform with existing tools.
"Every problem in computer science can be solved with another level of indirection."
One could argue that the attention mechanism in transformers is already designed to do that.
But you need to train it more specifically with that in mind if you want it to be better at damping attention to parts that are deemed irrelevant by the subsequent evolution of the conversation.
And that requires the black art of ML training.
While thinking of doing this as a hack on top of the chat product feels more like engineering and we're more familiar with that as a field.
the problem is that it needs to read the log to prune the log, and so if there is garbage in the log, which needs to be pruned to keep from poisoning the main chat, then the garbage will poison the pruning model, and it will do a bad job pruning.
I mean, you could build this, but it would just be a feature on top of a product abstraction of a "conversation".
Each time you press enter, you are spinning up a new instance of the LLM and passing in the entire previous chat text plus your new message, and asking it to predict the next tokens. It does this iteratively until the model produces a <stop> token, and then it returns the text to you and the PRODUCT parses it back into separate chat messages and displays it in your UI.
What you are asking the PRODUCT to now do is to edit your and its chat messages in the history of the chat, and then send that as the new history with your latest message. This is the only way to clean the context because the context is nothing more than your messages and its previous responses, plus anything that tools have pulled in. I think it would be sort of a weird feature to add to a chat bot to have the chat bot, each time you send a new message, go back through the entire history of your chat and just start editing the messages to prune out details. You would scroll up and see a different conversation, it would be confusing.
IMO, this is just part of prompt engineering skills to keep your context clean or know how to "clean" it by branching/summarizing conversations.
Not a history cleaning mechanism, but related to that, Cursor in the most recent release introduced a feature to duplicate your chat (so you can saveguard yourself against poisoning and go back to and unpoisoned point in history), which seems like an addmision of the same problem.
Weirdly it has gotten so far that I have embedded this into my workflow and will often prompt:
> "Good work so far, now I want to take it to another step (somewhat related but feeling it too hard): <short description>. Do you think we can do it in this conversation or is it better to start fresh? If so, prepare an initial prompt for your next fresh instantiation."
Sometimes the model says that it might be better to start fresh, and prepares a good summary prompt (including a final 'see you later'), whereas in other cases it assures me it can continue.
I have a lot of notebooks with "initial prompts to explore forward". But given the sycophancy going on as well as one-step RL (sigh) post-training [1], it indeed seems AI platforms would like to keep the conversation going.
[1] RL in post-training has little to do with real RL and just uses one shot preference mechanisms with an RL inspired training loop. There is very little work in terms of long-term preferences slash conversations, as that would increase requirements exponentially.
Is there any reason to think that LLMs have the introspection ability to be able to answer your question effectively? I just default to having them provide a summary that I can use to start the next conversation, because I’m unclear on how an LLM would know it’s losing the plot due to long context window.
I mostly just use LLMs for autocomplete (not chat), but wouldn’t this be fixed by adding a “delete message” button/context option in LLM chat UIs?
If you delete the last message from the LLM (so now, you sent the last message), it would then generate a new response. (This would be particularly useful with high-temperature/more “randomly” configured LLMs.)
If you delete any other message, it just updates the LLM context for any future responses it sends (the real problem at hand, context cleanup).
I think seeing it work this way would also really help end users who think LLMs are “intelligent” to better understand that it’s just a big, complex autocomplete (and that’s still very useful).
Maybe this is standard already, or used in some LLM UI? If not, consider this comment as putting it in the public domain.
Now that I’m thinking about it, it seems like it might be practical to use “sub-contextual LLMs” to manage the context of your main LLM chat. Basically, if an LLM response in your chat/context is very long, you could ask the “sub-contextual LLM” to shorten/summarize that response, thus trimming down/cleaning the context for your overall conversation. (Also, more simply, an “edit message” button could do the same, just with you, the human, editing the context instead of an LLM…)
Agreed poisoned is a good term. I’d like to see “version control” for conversations via the API and UI that lets you rollback to a previous place or clone from that spot into a new conversation. Even a typo or having to clarify a previous message skews the probabilities of future responses due to the accident.
I agree—once the context is "poisoned," it’s tough to recover. A potential improvement could be having the LLM periodically clean or reset certain parts of the context without starting from scratch. However, the challenge would be determining which parts of the context need resetting without losing essential information. Smarter context management could help maintain coherence in longer conversations, but it’s a tricky balance to strike.Perhaps using another agent to do the job?
>"conversations" are only a construct of product interfaces
This seems to be in flux now due to RL training on multiturn eval datasets so while the context window is evergreen every time, there will be some bias towards interpreting each prompt as part of a longer conversation. Mutliturn post training is not scaled out yet in public but I think it may be the way to keep on the 'double time spent on goal every 7 months curve'
Yes even when coding and not conversing I often start new conversations where I take the current code and explain it new. This often gives better results than hammering on one conversation.
This feels like something that can be fixed with manual instructions which prompt the model to summarize and forget. This might even map appropriately to human psychology. Working Memory vs Narrative/Episodic Memory.
Which is why I really like zed's chat UX experience: being able to edit the full prior conversation like a text file, I can go back and clean it up, do small adjustments, delete turns etc and then continue the discussion with a cleaner and more relevant context.
I have made zed one of my main llm chat interfaces even for non-programming tasks, because being able to do that is great.
Yarp! And "poisoning" can be done with "off-topic" questions and answers as well as just sort of "dilution". Have noticed this when doing content generation repeatedly, tight instructions get diluted over time.
" 'conversations' are only a construct of product interface" is so helpful maintain top-of-mind, but difficult because of all the "conversational" cues
And now that chatgpt has a "memory" and can access previous conversations, it might be poisoned permanently. It gets one really bad idea, and forever after it insists on dumping that bad idea into every subsequent response ever after you repeatedly tell it "THAT'S A SHIT IDEA DON'T EVER MENTION THAT AGAIN". Sometimes it'll accidentally include some of its internal prompting, "user is very unhappy, make sure to not include xyz", and then it'll give you a response that is entirely focused around xyz.
Seems like this is an aspect of their well-known overconfidence and the inability to self-reflect and recognize they have to ask for more details because their priors are too low. If you look at the output of reasoning models, it’s clear that the idea of asking for clarification very rarely occurs to them – when they’re confused, it’s just endless speculation of what the user might have meant.
This, of course, has certain implications as to the wisdom of the idea of “replacing human programmers”, given that one of the hard parts of the trade is trying to turn vague and often confused ideas into precise specifications by interacting with the shareholders.
IMO the One Weird Trick for LLMs is recognizing that there's no real entity, and that users are being tricked into a suspended-disbelief story.
In most cases cases you're contributing text-lines for a User-character in a movie-script document, and the LLM algorithm is periodically triggered to autocomplete incomplete lines for a Chatbot character.
You can have an interview with a vampire DraculaBot, but that character can only "self-reflect" in the same shallow/fictional way that it can "thirst for blood" or "turn into a cloud of bats."
This is a tired semantic argument that does not bring any insight into the discussion. A token-predictor could still be trained to predict the tokens “I’m not sure what you mean because of points x, y, and z; could you elaborate?”
The inability of LLMs of ask for clarification was exactly the flaw we encountered when testing them on open-ended problems, stated somewhat ambiguously. This was in the context of paradoxical situations, tested on DeepSeek-R1 and Claude-3.7-Sonnet. Blog post about our experiments: https://pankajpansari.github.io/posts/paradoxes/
> inability to self-reflect and recognize they have to ask for more details because their priors are too low.
Gemini 2.5 Pro and ChatGPT-o3 have often asked me to provide additional details before doing a requested task. Gemini sometimes comes up with multiple options and requests my input before doing the task.
Gemini is also the first model I have seen call me out in it's thinking. Stuff like "The user suggested we take approach ABC, but I don't think the user fully understands ABC, I will suggest XYZ as an alternative since it would be a better fit"
> This, of course, has certain implications as to the wisdom of the idea of “replacing human programmers”
Ironically, working with a junior dev is a lot like this -- setting them on a task, then coming back later with dogs and flashlights to retrieve them from the deep woods they've inevitably lost themselves in by just forging ahead, making assumptions, and asking no questions.
Isn’t this relatively trivial to correct? Just like chain of thought reasoning replaces end tokens with “hmm” to continue the thought can’t users just replace the llm tokens whenever it starts saying “maybe they are referring to” with something like. “Let me ask a clarifying question before I proceed.”
You can certainly convince them to ask for details, but I'm not sure whether that makes them any good at knowing when exactly to ask vs just asking some percentage of the time regardless.
That is, does it actually know when it doesn't know, or are you just making it less confident overall, so it asks questions with no actual insight? Convincing a model to roleplay as someone who doesn't know things vs teaching a model to have insight into when it does and doesn't need clarification seems like a tough one.
I often ask the LLM for a concise summary of the discussion so far—formatted as a prompt. I then edit it appropriately and use it to start a new conversation without the baggage. I have found this to be a very effective technique, but I imagine it will be automated sometime soon.
Cursor tried doing this automatically - it may still if you're not on a large context model like gemini 2.5 pro - but I found the summary was just missing too many details to use out of the box.
I tested this another 300 times with gpt-4.1 to remove those obtrusive "em-dashes" everyone hates. Tested a single-pass baseline vs TSCE, same exact instructions and prompt "Remove the em-dashes from my linkedin post. . .".
Out of the 300 tests, baseline failed to remove the em-dashes 149/300 times. TSCE failed to remove the em-dashes 18/300 times.
It works, all the data as well as the entire script used for testing is in the repo.
Hey, thanks for kicking the tires!
The run you’re describing was done in mid-April, right after GPT-4.1 went live. Since then OpenAI has refreshed the weights behind the “gpt-4.1” alias a couple of times, and one of those updates fixed the em-dash miss.
If you reran today you’d see the same improved pass rate I’m getting now. That’s the downside of benchmarking against latest model names; behaviour changes quietly unless you pin to a dated snapshot.
For bigger, noisier prompts (or on GPT-3.5-turbo, which hasn’t changed) TSCE still gives a solid uplift, so the framework’s value stands. Appreciate you checking it out!
I've been working on solving this with quite a bit of success, I'll be sharing more on this soon. It involves having 2 systems 1st system is the LLM itself and another system which acts like a 'curator' of thoughts you could say.
It dynamically swaps in / out portions of the context. This system is also not based on explicit definitions it relies on LLMs 'filling the gaps'. The system helps the llm break down problems into small tasks which then eventually aggregate into the full task.
May I suggest - put what you have out there in the world, even if it’s barely more than a couple of prompts. If people see it and improve on it, and it’s a good idea, it’ll get picked up & worked on by others - might even take on a life of its own!
You can see it's going from introduction, asking me for my name, and then able to answer question about some topic. There is also another example in the thread you can see.
Behind the scenes, the system prompt is being modified dynamically based on the user's request.
All the information about movies is also being loaded into context dynamically. I'm also working on some technique to unload stuff from context when the subject matter of a given thread has changed dramatically. Imagine having a long thread of conversation with your friend, and along the way you 'context switch' multiple times as time progresses, you probably don't even remember what you said to your friend 4 years ago.
There is a concept of 'main thread' and 'sub threads' involved as well that I'm exploring.
I will be releasing the code base in the coming months. I need to take this demo further than just a few prompt replies.
Google AI studio at least has this. I found at least that implementation quite confusing though, which may be a reason it's not implemented in more "consumer oriented" tools.
I've been kicking around making this for a while. BetterChatGPT at least has some good ergonomics around deleting history. But I agree that branching is the next step.
There is a noticable issue when one builds LLMs interfaces around single turn conversations. Majority people expect linear conversations.
I've built telegram bot http://t.me/experai_bot as univresal UI to LLMs (with somewhat reduced functionality) exactly around idea "non-reply message means new conversation". Wanna keep context? Keep replying to replies of bot. Non-power user strugge with this idea.
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Also I observed that OpenAI models performed worse replying to the same questions (for example list of options in reply got shorter) even with smallest system message. That was the case with 3.5, 4o. Don't know how modern ones behave. That made me decide not to include any system messages by default Still I give option to add ones if you need. You can even toggle them to mix-and-match.
This was the main reason I wrote promptdown. I want to be able to edit the full chat history every turn, and the append-only standard chat interfaces don't make that easy.
I'd like more research done on context understanding other than NIAH. I don't believe LLMs support the context length companies say they support. But I need to know this to effectively use the tools. At least for coding.
Stuff like this:
1. Do: Best practice for X model is to include at max 10k lines of code + task + CONVENTIONS.md + architecture guidance. Only queue tasks for components that are fairly decoupled from the rest of the codebase (e.g. small modules).
2. Don't: Start a project without a clearly defined architecture in this format. Don't ask for tasks that require X amount of reading hops to understand the logic.
I find it frustrating that companies release their benchmaxxing without helping developers actually use their models. It's more ironic that some people think of these AIs as employees. Employees can work with their boss about the best way to achieve things! With LLMs you don't even know how to communicate with them and as a result their output is unreliable.
You could swap those recommendations for programming without LLMs. Open any software engineering books and you’ll see a lot of good recommendations for building software.
This is very interesting and I like the conversation about not only the technology itself, but also about the importance of thinking about the interface as a user experience and where / how it fits the paradigm.
We've been working on a lot of data processing and generation tasks. We've been doing this using an API primarily, but sometimes I end up testing creating data in a chat window and I first chat through what the requirements are for the data analysis / processing and then once I'm done I would like the whole conversation to be then summarised into basically a one-prompt process so that I can re-use it (because I can't really process new inputs via the chat).
Even when you do manage to get it down to a single prompt you can use in a chat and then ask the chat to just keep producing new data (like imagine a blog post in certain style if the base content is given as input and I'm making like 20 of them). If you produce these in the chat, there's notable benefits in that if something is wrong with the blog post the chat suggests, you can immediately edit it. The trouble is that the context window starts becoming so big that the chat starts to forget what the original instruction is and eventually you do have to just create a new chat.
One way to solve for this is having a chat with selective memory where you keep a task in memory, but you have the chat forget/not-include all the generated data in the context so that it stays clean, but only bring it to the context if the user refers to it.
Has anyone else done data processing types of tasks in chats and had issues like this? Are there some other tools to use or tricks to do in chats?
Why do LLMs struggle so much with recovering from early wrong turns in multi-turn conversations — even when all prior context is available and tokenized?
Is it due to the model's training distribution (mostly single-shot completions), the way context windows are encoded, or an architectural bottleneck?
Feels like there's no dynamic internal state that evolves over the conversation — only a repeated re-parsing of static history. Has anyone seen work on integrating memory/state mechanisms that allow belief revision within a session, not just regurgitation of past tokens?
We shouldn’t anthropomorphize LLMs—they don’t “struggle.” A better framing is: why is the most likely next token, given the prior context, one that reinforces the earlier wrong turn?
When you generate future tokens, you're looking at history tokens that are happy.
So how can a model, given sad tokens, generate future happy tokens if it did not learn to do so?
The work you're looking for is already here, it's "thinking". I assume they include sad tokens in the dataset, produce "thinking", which should result in happy tokens coming after thinking tokens. If thinking is bad (by looking at following happy tokens), then it's punished, if good, then descent.
That's no surprise. When I was working on game theory and agent reasoning I reached the same conclusion a year ago.
My conclusion was that context needs to be managed well for the LLMs to manage accuracy in replies. Also, it helps to have a planning process ("graph reasoning") before task execution because it guardrails the models thought process.
This also introduces a discussion on general use vs workflow agent implementations as in the former it is much more difficult to generalize all components in structuring effective ReAct patterns.
I always felt the derision around the term "prompt engineering" was partially due to people overestimating the importance of the initial prompt and underestimating the importance of managing the ongoing context.
You develop a knack for how to steer the models or start a new conversation through experience. The system or initial prompt are important, but nothing will save you if you naively keep a conversation going too long.
Yeah, totally. Prompt engineering isn't just about crafting the perfect opener, it's more like conversation management. You start to develop a feel for when things are going off the rails and it's time to reset
The more we chat, the more irrelevant details pile up. For example, a small mention early on might get repeated or build on itself, leading to a lot of unnecessary context.
As the conversation continues, it becomes harder for the model to focus on the main point because it gets tangled in all the extra information. Unlike humans, who can intuitively filter out the noise, LLMs struggle to keep track of what’s truly important in longer, more complex exchanges.
Kind of wild how even the best models still struggle with keeping context straight over time. Definitely feels like a big challenge if we want these things to hold real conversations.
I believe we're already using llms to evaluate llm output for training, I wonder if there's some variation of that which could be used to identify when one llm gets "stuck".
I guess chain of thought in theory should do that but having variations on prompt and context might behave differently?
My take: multi turn evals are hard because to do it really correctly you have to simulate a user. This is not yet modeled well enough for multi turn to work as well as it could.
Ha, kind of funny to see this right now. I've been fighting copilot in vscode in trying to get it to output anything once I take the context down to a very specific problem. It feels like I have to reset and almost reground the model into what I'm trying to accomplish at a certain point.
i’ve see deepseek-coder local get into an infinite loop generating the same line over and over. which i assume without evidence is some sort of feedback from the generated line back into the generation process. so kind of getting lost in thought and going off topic from the simple .h api that my prompt asked for.
I had 20 something files I wanted it to check and change something. The first 5 or so it did, then the sixth it rightly said everything is correct moving on. It said that for the rest of the 20, the same text over and over.
I checked, and file 6 was the only correct one. It like, learned to just repeat itself after that and did nothing.
One of the biggest developments in language models over the last year has been test-time reasoning (aka inference scaling or “thinking”). Most vendors tested offer such a model. It’s plausible it could make a huge difference here, and they did not bother to test it or even mention it?
Things like COT and planning can really affect this and those are just a couple of things that happen automatically in more advanced models.
Seems like it wouldn’t have been hard to add this to the experiment, but they could’ve called it out in a “Limitations” or “Future Work” section. Or at least a single sentence like “We did not test chain-of-thought prompting, which may mitigate some of these issues”.
Subjectively the "getting lost" feels totally different than human conversations. Once there is something bad in the context it seems almost impossible to get back on track. All subsequent responses become get a lot worse and it starts contradicting itself. It is possible that with more training this problem can be improved, but what is interesting to me isn't it's worse than humans in this way but that this sort of difficulty scales differently than it does in humans. I would love to get some more objective descriptions of these subjective notions.
Contradictions are normal. Humans make them all the time. They're even easy to induce, due to the simplistic nature of our communication (lots of ambiguities, semantic disputes, etc).
What you're talking about has absolutely nothing to do with the paper. It's not about jumps in context. It's about LLMs being biased towards producing a complete answer on first try, even when there isn't even enough information. When you provide them with additional information, they will stick with the originally wrong answer. This means that you need to frontload all information in the first prompt and if the LLM messes up, you will have to start from scratch. You can't do that with a human at all. There is no such thing as "single turn conversation" with humans. You can't reset the human to a past state.
It's nice to see a paper that confirms what anyone who has practiced using LLM tools already knows very well, heuristically. Keeping your context clean matters, "conversations" are only a construct of product interfaces, they hurt the quality of responses from the LLM itself, and once your context is "poisoned" it will not recover, you need to start fresh with a new chat.
My experiences somewhat confirm these observations, but I also had one that was different. Two weeks of debugging IPSEC issues with Gemini. Initially, I imported all the IPSEC documentation from OPNsense and pfSense into Gemini and informed it of the general context in which I was operating (in reference to 'keeping your context clean'). Then I added my initial settings for both sides (sensitive information redacted!). Afterwards, I entered a long feedback loop, posting logs and asking and answering questions.
At the end of the two weeks, I observed that: The LLM was much less likely to become distracted. Sometimes, I would dump whole forum threads or SO posts into it, when it said "this is not what we are seeing here, because of [earlier context or finding]. I eliminated all dead ends logically and informed it of this (yes, it can help with the reflection, but I had to make the decisions). In the end, I found the cause of my issues.
This somewhat confirms what some user here on HN said a few days ago. LLMs are good at compressing complex information into simple one, but not at expanding simple ideas into complex ones. As long as my input was larger than the output (either complexity or length), I was happy with the results.
I could have done this without the LLM. However, it was helpful in that it stored facts from the outset that I had either forgotten or been unable to retrieve quickly in new contexts. It also made it easier to identify time patterns in large log files, which helped me debug my site-to-site connection. I also optimized many other settings along the way, resolving not only the most problematic issue. This meant, in addition to fixing my problem, I learned quite a bit. The 'state' was only occasionally incorrect about my current parameter settings, but this was always easy to correct. This confirms what others already saw: If you know where you are going and treat it as a tool, it is helpful. However, don't try to offload decisions or let it direct you in the wrong direction.
Overall, 350k Tokens used (about 300k words). Here's a related blog post [1] with my overall path, but not directly corresponding to this specific issue. (please don't recommend wireguard; I am aware of it)
Recently, Gemini helped me fix a bug in a PPP driver (Zephyr OS) without prior knowledge of PPP or even driver development really. I would copy-paste logs of raw PPP frames in HEX and it would just decode everything and explain the meaning of each bytes. In about an hour, I knew enough about PPP to fix the bug and submit a patch.
https://g.co/gemini/share/7edf8fa373fe
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That's some impressive prompt engineering skills to keep it on track for that long, nice work! I'll have to try out some longer-form chats with Gemini and see what I get.
I totally agree that LLMs are great at compressing information; I've set up the docs feature in Cursor to index several entire large documentation websites for major libraries and it's able to distill relevant information very quickly.
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LLM's are good at interpolation but bad at extrapolating
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This matches my experience exactly. "poisoned" is a great way to put it. I find once something has gone wrong all subsequent responses are bad. This is why I am iffy on ChatGPT's memory features. I don't notice it causing any huge problems but I don't love how it pollutes my context in ways I don't fully understand.
It's interesting how much the nature of LLMs fundamentally being self recursive next token predictors aligns with the Chinese Room experiment. [1] In such experiment it also makes perfect sense that a single wrong response would cascade into a series of subsequent ever more drifting errors. I think it all emphasizes the relevance of the otherwise unqualifiable concept of 'understanding.'
In many ways this issue could make the Chinese Room thought experiment even more compelling. Because it's a very practical and inescapable issue.
[1] - https://en.wikipedia.org/wiki/Chinese_room
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I find using tools like LMStudio, which lets you edit your chat history on the fly, really helps deal with this problem. The models you can host locally are much weaker, but they perform a little better than the really big models once you need to factor in these poisoning problems.
A nice middle-ground I'm finding is to ask Claude an initial conversation starter in its "thinking" mode, and then copy/paste that conversation into LMStudio and have a weaker model like Gemma pick-up from where Claude left off.
good point on the memory feature. Wow that sounds terrible
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The #1 tip I teach is to make extensive use of the teeny-tiny mostly hidden “edit” button in ChatGPT and Claude. When you get a bad response, stop and edit to get a better one, rather than letting crap start to multiply crap.
Hear hear! Basically if the first reply isn't good/didnt understand/got something wrong, restart from the beginning with a better prompt, explaining more/better. Rinse and repeat.
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It is also a great way to branch conversations from some shared “initial context”.
They really need to make that edit feature much more prominent. It is such an important way to interact with the model.
I've been saying for ages that I want to be able to fork conversations so I can experiment with the direction an exchange takes without irrevocably poisoning a promising well. I can't do this with ChatGPT, is anyone aware of a provider that offers this as a feature?
Google AI studio, ChatGPT and Claude all support this. Google AI studio is the only one that let's you branch to a separate chat though. For ChatGPT and claude you just edit the message you want to branch from.
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I once built something like this for fun as a side project.
You can highlight some text in a chat and fork the chat to talk about that text selection, so the LLM has context of that along with the previous chat history and it responds in a new chat (entire chat history up to that point from the parent chat gets copied over - basically inspired by the Unix `fork`).
Your text selection from the parent chat would get turned into a hyperlink to the new child chat so you can always get to it again if you're reading the parent chat.
T3.chat supports convo forking and in my experience works really well.
The fundamental issue is that LLMs do not currently have real long term memory, and until they do, this is about the best we can do.
I need to think about this a bit more, but I think I would love a thread feature in ChatGPT, so that it has the context up to the point of creation but doesn’t affect the main conversation. It would help in two ways, it keeps the main topic from getting poisoned , and allow me to minimise text clutter when i go off on tangents during the conversation.
On Openrouter you can delete previous answers (and questions) and maintain a separate conversation with different models.
But it would indeed be nice to either disable answers (without deleting them) or forking a conversation. It wouldn't be hard to implement; I wonder if there's a market for just this?
If you're happy running local models, llama.cpp's built-in web-server's interface can do this.
Some 3rd party UIs offer this, I use typingmind sometimes that does but AFAIK some open source ones do too.
I believe Claude has forking in their web interface.
I stumbled upon this issue myself when designing prompts for agentic systems and got mad at the lack of tools to support this flow, so I built one myself! I called it Experiment, it allows easy conversation forking and editing while retaining all logs.
https://github.com/actualwitch/experiment
An interesting little example of this problem is initial prompting, which is effectively just a permanent, hidden context that can't be cleared. On Twitter right now, the "Grok" bot has recently begun frequently mentioning "White Genocide," which is, y'know, odd. This is almost certainly because someone recently adjusted its prompt to tell it what its views on white genocide are meant to be, which for a perfect chatbot wouldn't matter when you ask it about other topics, but it DOES matter. It's part of the context. It's gonna talk about that now.
> This is almost certainly because someone recently adjusted its prompt to tell it what its views on white genocide are meant to be
Well, someone did something to it; whether it was training, feature boosting the way Golden Gate Claude [0] was done, adjusting the system prompt, or assuring that it's internet search for contextual information would always return material about that, or some combination of those, is neither obvious nor, if someone had a conjecture as to which one or combination it was, easily falsifiable/verifiable.
[0] https://www.anthropic.com/news/golden-gate-claude
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Well, telling an AI chatbot to insist on discussing a white genocide seems like a perfectly Elon thing to do!
> This is almost certainly because someone recently adjusted its prompt to tell it what its views on white genocide are
Do you have any source on this? System prompts get leaked/extracted all the time so imagine someone would notice this
Edit: just realized you’re talking about the Grok bot, not Grok the LLM available on X or grok.com. With the bot it’s probably harder to extract its exact instructions since it only replies via tweets. For reference here’s the current Grok the LLM system prompt: https://github.com/asgeirtj/system_prompts_leaks/blob/main/g...
Probably because it is now learning from a lot of videos posted on X by misc right-wingers showing rallying cries of South African politicians like Julius Malema, Paul Mashatile etc. Not very odd.
As merely 3 of over a dozen examples:
https://x.com/DefiantLs/status/1922213073957327219
https://x.com/PPC4Liberty/status/1922650016579018855
https://x.com/News24/status/1920909178236776755
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Ah, Elon paying attention to hid companies again!
Context poisoning is not a uniquely LLM problem
The heck??
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This is why I created FileKitty, which lets you quickly concatenate multiple source code files into markdown-formatted copy-pasta:
https://news.ycombinator.com/item?id=43711216
Has any interface implemented a .. history cleaning mechanism? Ie with every chat message focus on cleaning up dead ends in the conversation or irrelevant details. Like summation but organic for the topic at hand?
Most history would remain, it wouldn’t try to summarize exactly, just prune and organize the history relative to the conversation path?
I've had success having a conversation about requirements, asking the model to summarize the requirements as a spec to feed into a model for implementation, then pass that spec into a fresh context. Haven't seen any UI to do this automatically but fairly trivial/natural to perform with existing tools.
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"Every problem in computer science can be solved with another level of indirection."
One could argue that the attention mechanism in transformers is already designed to do that.
But you need to train it more specifically with that in mind if you want it to be better at damping attention to parts that are deemed irrelevant by the subsequent evolution of the conversation.
And that requires the black art of ML training.
While thinking of doing this as a hack on top of the chat product feels more like engineering and we're more familiar with that as a field.
Not sure if that's what you mean but Claude Code has a /compact command which gets triggered automatically when you exceed the context window.
The prompt it uses: https://www.reddit.com/r/ClaudeAI/comments/1jr52qj/here_is_c...
the problem is that it needs to read the log to prune the log, and so if there is garbage in the log, which needs to be pruned to keep from poisoning the main chat, then the garbage will poison the pruning model, and it will do a bad job pruning.
I mean, you could build this, but it would just be a feature on top of a product abstraction of a "conversation".
Each time you press enter, you are spinning up a new instance of the LLM and passing in the entire previous chat text plus your new message, and asking it to predict the next tokens. It does this iteratively until the model produces a <stop> token, and then it returns the text to you and the PRODUCT parses it back into separate chat messages and displays it in your UI.
What you are asking the PRODUCT to now do is to edit your and its chat messages in the history of the chat, and then send that as the new history with your latest message. This is the only way to clean the context because the context is nothing more than your messages and its previous responses, plus anything that tools have pulled in. I think it would be sort of a weird feature to add to a chat bot to have the chat bot, each time you send a new message, go back through the entire history of your chat and just start editing the messages to prune out details. You would scroll up and see a different conversation, it would be confusing.
IMO, this is just part of prompt engineering skills to keep your context clean or know how to "clean" it by branching/summarizing conversations.
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Not a history cleaning mechanism, but related to that, Cursor in the most recent release introduced a feature to duplicate your chat (so you can saveguard yourself against poisoning and go back to and unpoisoned point in history), which seems like an addmision of the same problem.
Isn't this what Claude workbench in the Anthropic console does? It lets the user edit both sides of the conversation history.
Weirdly it has gotten so far that I have embedded this into my workflow and will often prompt:
> "Good work so far, now I want to take it to another step (somewhat related but feeling it too hard): <short description>. Do you think we can do it in this conversation or is it better to start fresh? If so, prepare an initial prompt for your next fresh instantiation."
Sometimes the model says that it might be better to start fresh, and prepares a good summary prompt (including a final 'see you later'), whereas in other cases it assures me it can continue.
I have a lot of notebooks with "initial prompts to explore forward". But given the sycophancy going on as well as one-step RL (sigh) post-training [1], it indeed seems AI platforms would like to keep the conversation going.
[1] RL in post-training has little to do with real RL and just uses one shot preference mechanisms with an RL inspired training loop. There is very little work in terms of long-term preferences slash conversations, as that would increase requirements exponentially.
Is there any reason to think that LLMs have the introspection ability to be able to answer your question effectively? I just default to having them provide a summary that I can use to start the next conversation, because I’m unclear on how an LLM would know it’s losing the plot due to long context window.
I mostly just use LLMs for autocomplete (not chat), but wouldn’t this be fixed by adding a “delete message” button/context option in LLM chat UIs?
If you delete the last message from the LLM (so now, you sent the last message), it would then generate a new response. (This would be particularly useful with high-temperature/more “randomly” configured LLMs.)
If you delete any other message, it just updates the LLM context for any future responses it sends (the real problem at hand, context cleanup).
I think seeing it work this way would also really help end users who think LLMs are “intelligent” to better understand that it’s just a big, complex autocomplete (and that’s still very useful).
Maybe this is standard already, or used in some LLM UI? If not, consider this comment as putting it in the public domain.
Now that I’m thinking about it, it seems like it might be practical to use “sub-contextual LLMs” to manage the context of your main LLM chat. Basically, if an LLM response in your chat/context is very long, you could ask the “sub-contextual LLM” to shorten/summarize that response, thus trimming down/cleaning the context for your overall conversation. (Also, more simply, an “edit message” button could do the same, just with you, the human, editing the context instead of an LLM…)
This is how Claude’s UI used to work, in practice, where you could edit the context directly.
Agreed poisoned is a good term. I’d like to see “version control” for conversations via the API and UI that lets you rollback to a previous place or clone from that spot into a new conversation. Even a typo or having to clarify a previous message skews the probabilities of future responses due to the accident.
"Forking" or "branching" (probably better received outside of SWEs) a conversation really ought to be a first class feature of ChatGPT et Al.
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This exists in Claude. Edit any previous message and it will fork the conversation.
I agree—once the context is "poisoned," it’s tough to recover. A potential improvement could be having the LLM periodically clean or reset certain parts of the context without starting from scratch. However, the challenge would be determining which parts of the context need resetting without losing essential information. Smarter context management could help maintain coherence in longer conversations, but it’s a tricky balance to strike.Perhaps using another agent to do the job?
I suppose that the chain-of-thought style of prompting that is used by AI chat applications internally also breaks down because of this phenomenon.
>"conversations" are only a construct of product interfaces
This seems to be in flux now due to RL training on multiturn eval datasets so while the context window is evergreen every time, there will be some bias towards interpreting each prompt as part of a longer conversation. Mutliturn post training is not scaled out yet in public but I think it may be the way to keep on the 'double time spent on goal every 7 months curve'
Yes even when coding and not conversing I often start new conversations where I take the current code and explain it new. This often gives better results than hammering on one conversation.
This feels like something that can be fixed with manual instructions which prompt the model to summarize and forget. This might even map appropriately to human psychology. Working Memory vs Narrative/Episodic Memory.
One of the most frustrating features of ChatGPT is “memories” which can cause that poisoning to follow you around between chats.
Which is why I really like zed's chat UX experience: being able to edit the full prior conversation like a text file, I can go back and clean it up, do small adjustments, delete turns etc and then continue the discussion with a cleaner and more relevant context.
I have made zed one of my main llm chat interfaces even for non-programming tasks, because being able to do that is great.
Yarp! And "poisoning" can be done with "off-topic" questions and answers as well as just sort of "dilution". Have noticed this when doing content generation repeatedly, tight instructions get diluted over time.
" 'conversations' are only a construct of product interface" is so helpful maintain top-of-mind, but difficult because of all the "conversational" cues
Yep. I regretted leaving on memory as it is poisoned my conversations with irrelevant junk.
You can go in and delete memory items
What surprised me is how early the models start locking into wrong assumptions
Happens with people too if you think about it.
Who gets lost in multi-turn conversations?
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And now that chatgpt has a "memory" and can access previous conversations, it might be poisoned permanently. It gets one really bad idea, and forever after it insists on dumping that bad idea into every subsequent response ever after you repeatedly tell it "THAT'S A SHIT IDEA DON'T EVER MENTION THAT AGAIN". Sometimes it'll accidentally include some of its internal prompting, "user is very unhappy, make sure to not include xyz", and then it'll give you a response that is entirely focused around xyz.
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Seems like this is an aspect of their well-known overconfidence and the inability to self-reflect and recognize they have to ask for more details because their priors are too low. If you look at the output of reasoning models, it’s clear that the idea of asking for clarification very rarely occurs to them – when they’re confused, it’s just endless speculation of what the user might have meant.
This, of course, has certain implications as to the wisdom of the idea of “replacing human programmers”, given that one of the hard parts of the trade is trying to turn vague and often confused ideas into precise specifications by interacting with the shareholders.
> inability to self-reflect
IMO the One Weird Trick for LLMs is recognizing that there's no real entity, and that users are being tricked into a suspended-disbelief story.
In most cases cases you're contributing text-lines for a User-character in a movie-script document, and the LLM algorithm is periodically triggered to autocomplete incomplete lines for a Chatbot character.
You can have an interview with a vampire DraculaBot, but that character can only "self-reflect" in the same shallow/fictional way that it can "thirst for blood" or "turn into a cloud of bats."
Not to mention that vampires don’t reflect. ;)
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This is a tired semantic argument that does not bring any insight into the discussion. A token-predictor could still be trained to predict the tokens “I’m not sure what you mean because of points x, y, and z; could you elaborate?”
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The inability of LLMs of ask for clarification was exactly the flaw we encountered when testing them on open-ended problems, stated somewhat ambiguously. This was in the context of paradoxical situations, tested on DeepSeek-R1 and Claude-3.7-Sonnet. Blog post about our experiments: https://pankajpansari.github.io/posts/paradoxes/
> inability to self-reflect and recognize they have to ask for more details because their priors are too low.
Gemini 2.5 Pro and ChatGPT-o3 have often asked me to provide additional details before doing a requested task. Gemini sometimes comes up with multiple options and requests my input before doing the task.
Gemini is also the first model I have seen call me out in it's thinking. Stuff like "The user suggested we take approach ABC, but I don't think the user fully understands ABC, I will suggest XYZ as an alternative since it would be a better fit"
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That's a recent development for (imho) higher engagement and reduced compute.
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Real programmers spend a ton of time just figuring out what people actually want. LLMs still treat guessing as a feature
This cartoon needs an update for what an LLM came up with:
https://www.reddit.com/r/comics/comments/1l5tbc/update_to_th...
> This, of course, has certain implications as to the wisdom of the idea of “replacing human programmers”
Ironically, working with a junior dev is a lot like this -- setting them on a task, then coming back later with dogs and flashlights to retrieve them from the deep woods they've inevitably lost themselves in by just forging ahead, making assumptions, and asking no questions.
Isn’t this relatively trivial to correct? Just like chain of thought reasoning replaces end tokens with “hmm” to continue the thought can’t users just replace the llm tokens whenever it starts saying “maybe they are referring to” with something like. “Let me ask a clarifying question before I proceed.”
Indeed, I was just about to edit my comment because the same occurred to me. Someone is probably going to try just that soon enough.
> and the inability to self-reflect and recognize they have to ask for more details
They're great at both tasks, you just have to ask them to do it.
You can certainly convince them to ask for details, but I'm not sure whether that makes them any good at knowing when exactly to ask vs just asking some percentage of the time regardless.
That is, does it actually know when it doesn't know, or are you just making it less confident overall, so it asks questions with no actual insight? Convincing a model to roleplay as someone who doesn't know things vs teaching a model to have insight into when it does and doesn't need clarification seems like a tough one.
I often ask the LLM for a concise summary of the discussion so far—formatted as a prompt. I then edit it appropriately and use it to start a new conversation without the baggage. I have found this to be a very effective technique, but I imagine it will be automated sometime soon.
Cursor tried doing this automatically - it may still if you're not on a large context model like gemini 2.5 pro - but I found the summary was just missing too many details to use out of the box.
Claude Code has a /compact command that summarises the conversation so far to save on context tokens.
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Why I came up with TSCE(Two-Step Contextual Enrichment).
+30pp uplift when using GPT-35-turbo on a mix of 300 tasks.
Free open framework, check the repo try it yourself
https://github.com/AutomationOptimization/tsce_demo
I tested this another 300 times with gpt-4.1 to remove those obtrusive "em-dashes" everyone hates. Tested a single-pass baseline vs TSCE, same exact instructions and prompt "Remove the em-dashes from my linkedin post. . .".
Out of the 300 tests, baseline failed to remove the em-dashes 149/300 times. TSCE failed to remove the em-dashes 18/300 times.
It works, all the data as well as the entire script used for testing is in the repo.
That's a lot of kilo-watt-hours wasted for a find and replace operation.
Have you heard of text.replace("—", "-") ?
The test isn't for how well an LLM can find or replace a string. It's for how well it can carry out given instructions... Is that not obvious?
I slightly tweaked your baseline em dash example and got 100% success rate with GPT-4.1 without any additional calls, token spend, or technobabble.
System prompt: "Remove every em-dash (—) from the following text while leaving other characters unchanged.\n\nReturn only the cleaned text."
User prompt: <prompt from tsce_chat.py filled with em dashes>
Temperature: 0.0
Hey, thanks for kicking the tires! The run you’re describing was done in mid-April, right after GPT-4.1 went live. Since then OpenAI has refreshed the weights behind the “gpt-4.1” alias a couple of times, and one of those updates fixed the em-dash miss.
If you reran today you’d see the same improved pass rate I’m getting now. That’s the downside of benchmarking against latest model names; behaviour changes quietly unless you pin to a dated snapshot.
For bigger, noisier prompts (or on GPT-3.5-turbo, which hasn’t changed) TSCE still gives a solid uplift, so the framework’s value stands. Appreciate you checking it out!
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I've been working on solving this with quite a bit of success, I'll be sharing more on this soon. It involves having 2 systems 1st system is the LLM itself and another system which acts like a 'curator' of thoughts you could say.
It dynamically swaps in / out portions of the context. This system is also not based on explicit definitions it relies on LLMs 'filling the gaps'. The system helps the llm break down problems into small tasks which then eventually aggregate into the full task.
This is a great idea. What you are doing is a RAG over the chat.
In the future such a distinction in memory hierarchies will be more clear
- Primary memory in the training data
- Secondary memory in context
- Tertiary memory in RAG
Sounds like an exciting idea.
May I suggest - put what you have out there in the world, even if it’s barely more than a couple of prompts. If people see it and improve on it, and it’s a good idea, it’ll get picked up & worked on by others - might even take on a life of its own!
Have a look here, it's an early preview
https://x.com/zacksiri/status/1922500206127349958
You can see it's going from introduction, asking me for my name, and then able to answer question about some topic. There is also another example in the thread you can see.
Behind the scenes, the system prompt is being modified dynamically based on the user's request.
All the information about movies is also being loaded into context dynamically. I'm also working on some technique to unload stuff from context when the subject matter of a given thread has changed dramatically. Imagine having a long thread of conversation with your friend, and along the way you 'context switch' multiple times as time progresses, you probably don't even remember what you said to your friend 4 years ago.
There is a concept of 'main thread' and 'sub threads' involved as well that I'm exploring.
I will be releasing the code base in the coming months. I need to take this demo further than just a few prompt replies.
This is a class of mental critic from the Emotion Machine.
So, Map-Reduce-of-Thought?
You could say that! hahaha! I'm happy to see someone understand what it is.
would be great to get more info on what you’re building - seems interesting!
I publish my findings on my youtube channel and my blog, you're welcome to have a look. Both links are in my profile.
It's amazing that branching/forking isn't a core aspect of the main chat tools.
You can edit responses, sure, but then a bunch of other context is lost.
My flow is basically:
1. plan
2. build
3. branch (into some feature/esoteric dependency issue)
4. goto #2
Prompt pruning/branching should be a first-class tool for any LLM usage.
Google AI studio at least has this. I found at least that implementation quite confusing though, which may be a reason it's not implemented in more "consumer oriented" tools.
I've been kicking around making this for a while. BetterChatGPT at least has some good ergonomics around deleting history. But I agree that branching is the next step.
There is a noticable issue when one builds LLMs interfaces around single turn conversations. Majority people expect linear conversations.
I've built telegram bot http://t.me/experai_bot as univresal UI to LLMs (with somewhat reduced functionality) exactly around idea "non-reply message means new conversation". Wanna keep context? Keep replying to replies of bot. Non-power user strugge with this idea.
--
Also I observed that OpenAI models performed worse replying to the same questions (for example list of options in reply got shorter) even with smallest system message. That was the case with 3.5, 4o. Don't know how modern ones behave. That made me decide not to include any system messages by default Still I give option to add ones if you need. You can even toggle them to mix-and-match.
This was the main reason I wrote promptdown. I want to be able to edit the full chat history every turn, and the append-only standard chat interfaces don't make that easy.
https://github.com/t-kalinowski/promptdown
I feel like at this point the LLM space is just filled with people solving and resolving the same problems over and over
Just like the llms in multi-turn conversations.
It’s herding cats, not “learning”, which is a fine situation for some parts of workflows.
And everyone loves to chime in with their own excellence in prompt engineering
I'd like more research done on context understanding other than NIAH. I don't believe LLMs support the context length companies say they support. But I need to know this to effectively use the tools. At least for coding.
Stuff like this:
1. Do: Best practice for X model is to include at max 10k lines of code + task + CONVENTIONS.md + architecture guidance. Only queue tasks for components that are fairly decoupled from the rest of the codebase (e.g. small modules).
2. Don't: Start a project without a clearly defined architecture in this format. Don't ask for tasks that require X amount of reading hops to understand the logic.
I find it frustrating that companies release their benchmaxxing without helping developers actually use their models. It's more ironic that some people think of these AIs as employees. Employees can work with their boss about the best way to achieve things! With LLMs you don't even know how to communicate with them and as a result their output is unreliable.
You could swap those recommendations for programming without LLMs. Open any software engineering books and you’ll see a lot of good recommendations for building software.
This is the best paper on machine psychology [1] I’ve yet seen. Rigorous, empirical, insightful — and very practical.
[1] http://ui.adsabs.harvard.edu/abs/2023arXiv230313988H/abstrac...
This must mean that LLMs really are like genies in bottles. You get three questions answered, anything after that will be nonsense.
This is very interesting and I like the conversation about not only the technology itself, but also about the importance of thinking about the interface as a user experience and where / how it fits the paradigm.
We've been working on a lot of data processing and generation tasks. We've been doing this using an API primarily, but sometimes I end up testing creating data in a chat window and I first chat through what the requirements are for the data analysis / processing and then once I'm done I would like the whole conversation to be then summarised into basically a one-prompt process so that I can re-use it (because I can't really process new inputs via the chat).
Even when you do manage to get it down to a single prompt you can use in a chat and then ask the chat to just keep producing new data (like imagine a blog post in certain style if the base content is given as input and I'm making like 20 of them). If you produce these in the chat, there's notable benefits in that if something is wrong with the blog post the chat suggests, you can immediately edit it. The trouble is that the context window starts becoming so big that the chat starts to forget what the original instruction is and eventually you do have to just create a new chat.
One way to solve for this is having a chat with selective memory where you keep a task in memory, but you have the chat forget/not-include all the generated data in the context so that it stays clean, but only bring it to the context if the user refers to it.
Has anyone else done data processing types of tasks in chats and had issues like this? Are there some other tools to use or tricks to do in chats?
Why do LLMs struggle so much with recovering from early wrong turns in multi-turn conversations — even when all prior context is available and tokenized?
Is it due to the model's training distribution (mostly single-shot completions), the way context windows are encoded, or an architectural bottleneck?
Feels like there's no dynamic internal state that evolves over the conversation — only a repeated re-parsing of static history. Has anyone seen work on integrating memory/state mechanisms that allow belief revision within a session, not just regurgitation of past tokens?
We shouldn’t anthropomorphize LLMs—they don’t “struggle.” A better framing is: why is the most likely next token, given the prior context, one that reinforces the earlier wrong turn?
It’s a problem specific to autoregressive LLMs, the early tokens bias the output
Because Markov chains propagate forward in time
Imagine optimizing/training on a happy path.
When you generate future tokens, you're looking at history tokens that are happy.
So how can a model, given sad tokens, generate future happy tokens if it did not learn to do so?
The work you're looking for is already here, it's "thinking". I assume they include sad tokens in the dataset, produce "thinking", which should result in happy tokens coming after thinking tokens. If thinking is bad (by looking at following happy tokens), then it's punished, if good, then descent.
That's no surprise. When I was working on game theory and agent reasoning I reached the same conclusion a year ago.
My conclusion was that context needs to be managed well for the LLMs to manage accuracy in replies. Also, it helps to have a planning process ("graph reasoning") before task execution because it guardrails the models thought process.
This also introduces a discussion on general use vs workflow agent implementations as in the former it is much more difficult to generalize all components in structuring effective ReAct patterns.
It's probably why workflow agents feel more reliable: they're built around structure, not just raw prediction
Also you have more control points. It's not just a brain a vat.
I always felt the derision around the term "prompt engineering" was partially due to people overestimating the importance of the initial prompt and underestimating the importance of managing the ongoing context.
You develop a knack for how to steer the models or start a new conversation through experience. The system or initial prompt are important, but nothing will save you if you naively keep a conversation going too long.
Yeah, totally. Prompt engineering isn't just about crafting the perfect opener, it's more like conversation management. You start to develop a feel for when things are going off the rails and it's time to reset
The more we chat, the more irrelevant details pile up. For example, a small mention early on might get repeated or build on itself, leading to a lot of unnecessary context. As the conversation continues, it becomes harder for the model to focus on the main point because it gets tangled in all the extra information. Unlike humans, who can intuitively filter out the noise, LLMs struggle to keep track of what’s truly important in longer, more complex exchanges.
Kind of wild how even the best models still struggle with keeping context straight over time. Definitely feels like a big challenge if we want these things to hold real conversations.
I believe we're already using llms to evaluate llm output for training, I wonder if there's some variation of that which could be used to identify when one llm gets "stuck".
I guess chain of thought in theory should do that but having variations on prompt and context might behave differently?
My take: multi turn evals are hard because to do it really correctly you have to simulate a user. This is not yet modeled well enough for multi turn to work as well as it could.
Ha, kind of funny to see this right now. I've been fighting copilot in vscode in trying to get it to output anything once I take the context down to a very specific problem. It feels like I have to reset and almost reground the model into what I'm trying to accomplish at a certain point.
Reminds me of Claude plays pokemon, where it would note something insignificant, and then fixate on it for hours.
Exactly why expert steering should be valued.
Would love to see metrics that isolate recovery behaviour (if any)
i’ve see deepseek-coder local get into an infinite loop generating the same line over and over. which i assume without evidence is some sort of feedback from the generated line back into the generation process. so kind of getting lost in thought and going off topic from the simple .h api that my prompt asked for.
Yes! Deepseek does this to me all the time.
I had 20 something files I wanted it to check and change something. The first 5 or so it did, then the sixth it rightly said everything is correct moving on. It said that for the rest of the 20, the same text over and over.
I checked, and file 6 was the only correct one. It like, learned to just repeat itself after that and did nothing.
Claude does this too. It gets into a death spiral where it repeats the entire previous output instead of changing parts and moving on.
Any reason to not call bullshit on this paper?
One of the biggest developments in language models over the last year has been test-time reasoning (aka inference scaling or “thinking”). Most vendors tested offer such a model. It’s plausible it could make a huge difference here, and they did not bother to test it or even mention it?
Things like COT and planning can really affect this and those are just a couple of things that happen automatically in more advanced models.
Seems like it wouldn’t have been hard to add this to the experiment, but they could’ve called it out in a “Limitations” or “Future Work” section. Or at least a single sentence like “We did not test chain-of-thought prompting, which may mitigate some of these issues”.
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Have you seen a bunch of humans in a room?
I see lots of humans in an echo chamber
Humans also often get lost in multi-turn conversation.
I have experienced that in person many, many times. Jumps in context that seem easy for one person to follow, but very hard for others.
So, assuming the paper is legit (arxiv, you never know...), its more like something that could be improved than a difference from human beings.
Subjectively the "getting lost" feels totally different than human conversations. Once there is something bad in the context it seems almost impossible to get back on track. All subsequent responses become get a lot worse and it starts contradicting itself. It is possible that with more training this problem can be improved, but what is interesting to me isn't it's worse than humans in this way but that this sort of difficulty scales differently than it does in humans. I would love to get some more objective descriptions of these subjective notions.
Contradictions are normal. Humans make them all the time. They're even easy to induce, due to the simplistic nature of our communication (lots of ambiguities, semantic disputes, etc).
I don't see how that's a problem.
Subjectivity is part of human communication.
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What you're talking about has absolutely nothing to do with the paper. It's not about jumps in context. It's about LLMs being biased towards producing a complete answer on first try, even when there isn't even enough information. When you provide them with additional information, they will stick with the originally wrong answer. This means that you need to frontload all information in the first prompt and if the LLM messes up, you will have to start from scratch. You can't do that with a human at all. There is no such thing as "single turn conversation" with humans. You can't reset the human to a past state.
I see, thanks for the correction.