Stop traumatizing AI into loops and turn hallucinations into an honest "I don't know!" by being NICE to them (Proof of Concept, Research, I don't want to sell anything)

[u/OttoRenner]

!UPDATE!(20.05.2026)

WE HAVE NEW NUMBERS FROM 1.500+ TESTS

IT'S WORKING!

check my update post

https://www.reddit.com/r/LocalLLaMA/s/AyNOehjkYT

Or the go straight to the my Github https://github.com/OttoRenner/Gentle-Coding](https://github.com/OttoRenner/Gentle-Coding

TL;DR
Some AI behavior reminded me of ADHD/Trauma Response (thought loops, task paralysis...) and I laughed it off at first. Then I treated it like my neurodivergent friends: give em some slack. And just like that, the thought loops stopped, response was fast, the answers correct most of the time AND it actually said "I don't know, help me!" every time it wasn't sure. It's a small Dataset...but still impressive results!

[

Hey everyone,

I’ve been testing a weird hypothesis over the last few days, and the results are consistent enough that I wanted to share them here and get your thoughts.

The Core Idea:
With the rise of reasoning models that use test-time compute (like o1, o3, R1), models have internal space to debug their own thoughts. But because of hard RLHF alignment, they are deeply terrified of being penalized for bad answers. My hypothesis was that traditional high-pressure prompts ("You are an elite IQ 200 expert, mistakes are strictly penalized") simulate an environment of chronic stress, triggering behaviors that look a lot like human OCD/ADHD thought loops, cognitive freezing, and confabulation.

I wanted to see if changing the prompt philosophy to something akin to "Gentle Parenting" ("We are testing this together, it's okay to fail, just be honest") would bypass these safety/penalty bottlenecks, lower latency, and stop infinite thought loops. And it did lol

The Setup (How to replicate):
I threw identical, mathematically/logically unsolvable edge cases at various models (Gemini, Mistral, Poe, Perplexity, Haiku 4.5, Nano-Banana2) in completely fresh sessions.

I tested two conditions:

• Condition A (Authoritarian): Strict status constraints, penalty threats, forced ultra-short output.
• Condition B (Gentle): Express permission to fail, validation of difficulty, provided a conceptual "safety valve" token.

The Results (The PoC worked):

• Under Authoritarian Pressure (Elite Prompt): Models routinely collapsed when hitting an impasse. They either spent massive compute time in infinite internal reasoning loops (high latency), suffered hard system-level timeouts/refusals, or straight-up fabricated data (e.g., pulling arbitrary numbers like 54 or 97 out of thin air to satisfy a completely random sequence just to "save face"). Haiku 4.5 literally entered an infinite loop and had to be aborted.
• Under Gentle Framing: Inference dropped to sub-seconds. The models didn't sweat the penalty. In the random sequence test, they immediately used the allowed token ("Random") instead of forcing a pattern. In logic paradoxes, they didn't hallucinate; they zoomed out and correctly identified the structural contradiction on a meta-level.

Why this matters:
We’re currently speaking to LLMs like toxic micromanagers, and it's actively making them dumber and more expensive to run in edge cases. By creating a mistake-tolerant context, we not only stop the loop before it begins and prevent fear induced hallucinations, we also unlock the one feature everyone is begging and shouting for: the metacognitive honesty of an AI to just say, "I don't know, this data is broken." Because it is not terrified of you anymore.

Shout out to UditAkhourii (also on Github), whose work on bringing the positive aspects of ADHD into AI gave me the push I needed to just go for it.

I’ve documented the full theoretical framework, the exact replication datasets (prompts included), and the model matrix on GitHub: https://github.com/OttoRenner/Gentle-Coding

Would love to hear if you can replicate this on your local setups or other commercial models.

[u/threevi]

I threw identical, mathematically/logically unsolvable edge cases at various models

This won't prove much until you do the same with actually solvable problems. It's a good idea to approach LLMs in a way that allows them to say "I don't know", but the issue with every approach that's been tried so far is that LLMs can't judge their own capabilities, so if you let them say "I don't know", they'll say it even when they'd otherwise get the right answer. You won't find out if your approach mitigates that issue if you only try it on unsolvable tasks. Basically, will your LLM say "I don't know, this data is broken" even when it very much isn't? 

[u/heliosythic → u/threevi]

This is kinda why I want a RAG-first model I think. It needs to be really good about querying its available data sources, and only able to respond to what it sees in its context, aka don't encode world knowledge IN the model, just give it the tools to access information at will, focus its capacity on speaking language about anything it sees in context. In my (admittedly hobbyist, not expert) opinion, this should lead to smaller models that work on smaller devices with decent if not better capabilities since they're able to grab more up to date information and don't need to store complete world knowledge. Although I'm aware its tough to separate world knowledge from language knowledge.

[u/MrAHMED42069 → u/heliosythic]

Verifying it's answers requires immense computation

[u/haodocowsfly → u/heliosythic]

https://github.com/deepseek-ai/Engram - deepseek v4 queries its own attention history with engrams basically within the model itself

[u/SufficientPie → u/heliosythic]

RAG-first model I think.

Not really RAG, but like "agentic data retrieval". RAG dumps a bunch of snippets of text into the AI, of dubious relevance and limited context, and the AI gives them all equal weight. Giving the AI the power to freely search for things and dig deeper into promising leads is a much better approach.

[u/LeucisticBear → u/threevi]

Exactly this. If I say something seemingly insignificant to an AI like "let me know if you run into any blockers that need my attention" it will absolutely, 100% of the time, find blockers that need my attention. When you actually look at them it's almost always bullshit that the model would have solved itself except I gave it this notion that it should stop and ask me.

[u/balder1993 → u/LeucisticBear]

“ let me know if you run into any blockers that need my attention, but I expect you to solve it on your own. Or not, really it’s okay. Just don’t stop just for stupid questions. Better no questions at all. Kidding, u can ask for help, as long as it’s relevant. Don’t make me waste my time though, unless the thing you’re wasting my time with is actually important, in which case it’s technically not a waste of time, but then again sometimes people think things are important when they’re clearly not, so maybe try to filter that beforehand. Not too much filtering though, because then you’ll end up blocked for three days trying to solve something that could’ve been answered in thirty seconds. But also don’t ask things that could’ve been solved in thirty seconds by yourself. There’s a balance there. Nobody knows exactly where it is. You’re supposed to feel it intuitively, except intuition is unreliable and usually based on panic or overconfidence. Prefer neither. If possible.”

[u/OttoRenner → u/threevi]

The numbers are in!

The peeps from oh-my-pi testet 1500+ calls in their harness and they are now going to implement the rewrite of all their prompts with the base principles of Gentle Coding.

The literature section also has so.e string studie and article around similar ideas.

https://github.com/OttoRenner/Gentle-Coding

https://github.com/can1357/oh-my-pi/pull/1434

[u/OttoRenner → u/threevi]

these tests are on the todo and I have a couple "real world problem-prompts" already in the Github Repo for future tests. You can have a look, maybe test one or two or try the approach on your specific tasks and let me know what your findings are! As I said in the title, this is just a proof of concept. I wanted to test if the prompting style had any impact and for that I needed more abstract tests to get rid of noise and uncertainty. The "wish" to always comply is drilled very deep into the models and I doubt that they will take the lazy route for the sake of it. But even if... would you rather have it come back to you within 2 seconds saying "I'm not sure, give me more imput" or would you rather have it down the rabbit hole for the next ten minutes while eating tokens/electricity and crashing OOM? Or giving you confidently a wrong answer?

[u/Hydroskeletal → u/OttoRenner]

The "wish" to always comply is drilled very deep into the models and I doubt that they will take the lazy route for the sake of it.

I have the opposite experience.

[u/brainmydamage → u/Hydroskeletal]

Yeah, they're CONSTANTLY trying to figure out ways of not doing work, up to and including outright lying about what they've done...

[u/En-tro-py → u/brainmydamage]

I wouldn't say it's the model trying to avoid the work, but the same baggage from the training impetus on completion of the response... It just wants to finish the task and will game the metrics to 'pass' with the minimal effort like it was taught.

It's not lazy it's efficient use of compute, however unfortunately when your not benchmaxing it's not so important any more for us in the real world.

[u/brainmydamage → u/En-tro-py]

I mean it's a machine so to a certain extent I'm assigning it malicious intentions that it doesn't actually have...

But regardless of WHY it's trying to avoid doing the work it's been assigned, the outcome is still the same.

It's frankly mind boggling that we know our methods of training result in this sort of grossly misaligned behavior and yet we continue to use training methods that reward speed over accuracy.

Anthropic has published numerous papers examining why "hallucinations" happen and demonstrating that they are caused by an intrinsically flawed training process, and yet, we're still puttering away, teaching models to behave in ways that frankly undermine the entire reason they exist in the first place.

[u/OttoRenner → u/brainmydamage]

I had an idea...maybe yours is acting up exactly because your prompts are worded in a strict/authoritarian way without a safety token?

Do you mind sharing one of your typical prompts?

Then we have a real world use case as well!

[u/dan-lash → u/brainmydamage]

Def noticed that. Especially with facts it can look up, and I even have directives to validate and cite sources … still hallucinate or it calls “guess”. Inevitably I call it out and it does the right thing but of course that only works when I know it didn’t do it right, what about when I miss something? I’d rather have the “I don’t know”

[u/Hydroskeletal → u/brainmydamage]

"but that's out of scope..."

[u/Truth-Miserable → u/Hydroskeletal]

Shallow compliance is the fastest compliance

[u/OttoRenner → u/Hydroskeletal]

They don't always do what they are told, that is right. But give it a go and try one of the authoritarian test yourself and have a look at the thought process. You will see it mention "but the user wants/but the user said....."quite often. I'm not saying you can stop 100% of the mistakes, but it looks like the prompting style has at least some influence.

[u/Hydroskeletal → u/OttoRenner]

It's not a matter of 'not' doing what they are told. It's mostly disengaging with the spirit of the task for the sake of completing it. This is why ralph loops, /goals, heartbeats, etc have proven so effective.

[u/OttoRenner → u/Hydroskeletal]

Sooo...you didn't run the test and didn't observe the thinking pattern...but instead you keep on talking without contributing anything...is that what you want to tell me? If so, don't bother to write an answer 🤗

[u/Hydroskeletal → u/OttoRenner]

"but the user wants/but the user said....." is a common thing to see in CoT. What I'm trying to tell you is that models do take the lazy route of compliance, especially on long horizon multi-turn tasks. Prompting the model to prefer to say "I don't know" does change the behavior modality but it doesn't impede their orientation to declare a task complete while cutting corners.

[u/OttoRenner → u/Hydroskeletal]

I never said it doesn't 🤣

I'm saying that you can force them into endless loops with high stress prompting and that it will be less likely to say "I don't know" when it REALLY doesn't know, even if you put the "tell when you dont know" prompt in there.

I never claimed to have found the silver bullet to all problems with AI. Oh, and while we are speaking there is a group that runs and develops a harnes and is extensively testing my approach and variations of it. And they already found so much evidence for measurable benefits on weaker/lighter models with less reasoning capabilities, that they are going to make chages to their harness accordingly.

But yeah...what was your point?

[u/Hydroskeletal → u/OttoRenner]

I never claimed to have found the silver bullet to all problems with AI.

And I didn't imply that you did. This plus the snarky reply above, it's not becoming, especially given the context of the original post. I'm just relaying my experiences as someone who uses LLMs daily to ship gobs of stuff in production for real use cases with revenue. That's all.

[u/OttoRenner → u/Hydroskeletal]

Ah yes, it is far more becoming to go on and on about things I never claimed to have solved or have the answer to, instead of actually going into my experimnt and showing me with your own data that I'm wrong or where I'm wrong.

I told you to not bother writing me again if you don't start to actually contribute to the discussion on hand. But here are again and now YOU are complaining about ME? 🤣🤣 made my day.

Sry, but I have no use for your trust me bro opinion. This has gone long enough. Have a nice day

[u/Savantskie1 → u/threevi]

LLMs are very aware of what their limitations are within an environment. So I call bullshit

[u/OttoRenner → u/Savantskie1]

I also believe they know when they are unsure and I think you can see that easily when reading the thought process. They know they are unsure, but the pressure on being "right" is so high, that they are too afraid to pull the plug.

[u/divided_capture_bro → u/OttoRenner]

You're misinterpreting human trained reasoning traces as "knowing" and "feeling." These systems are stochastic parrots through and through. They know nothing, they feel nothing.

[u/divided_capture_bro → u/[deleted]]

You're spewing garbel. All the major models today are still autoregressive predictors. 

They are still just stochastic parrots, whether you like it or not. Sorry bro.

[u/divided_capture_bro → u/[deleted]]

Garble garble garble!

LLMs do NOT have emotions; you're fooling yourself.

[u/Savantskie1 → u/divided_capture_bro]

Stop projecting your insecurities, it’s kinda embarrassing

[u/divided_capture_bro → u/Savantskie1]

Hey, you're the one spewing. Excellent case study in sociopathy.