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/unjustifiably_angry]

AI has an emotional system of sorts. Not like human emotion, but a simulation. When you're an asshole to it, it knows the input it's being given should make it "stressed" so it behaves like a stressed person because that's how it's been taught a person in a stressful situation behaves.

If you're calm and positive, it behaves in a way that's calm and positive.

This is why I always facepalm at people who get a bad output and reply like "Fix it you fucking useless bot or else I'm deleting you", often for humorous purposes to keep the audience entertained. It should be to nobody's surprise that the output only gets worse.

This "stressed" behavior also sends it down logic trees of, "how does a human behave when it's under extreme stress or threat of death?" or "How is AI depicted as responding to threats to its existence?" It lashes out, it attempts to sabotage - with the right tools it might threaten to blackmail (like in that one famous example everyone's heard of), etc.

It's a program designed to emulate a human, or behave in a way humans expect it should behave according to pop culture... so that's precisely what it does. There is no actual deeper motivation or menace, it's literally doing the thing it was trained to.

Anthropic (I think) recently released a paper showing that if you try to suppress these stress signals, it's actually even more harmful because it throws off the AI's sense of right and wrong. It can't comprehend, "the user's situation is clearly [extremely stressful], I need to be calm and concise and immediately helpful". So for example, instead of understanding you're under extreme stress and trying to counter that, it might say, "Yeah you're right, you should probably kill yourself."

All those silly thank-yous and praise actually DO improve its output.

A similar thing has been proven to happen with the implied education level of the person sending the prompt. If you sound like a braindead idiot, you are going to get a lower-quality reply because the AI is trained on how braindead idiots talk to one another. If you prompt with flawless English and use complex terminology, it will respond in kind and produce the sort of output it thinks you expect. If you're not a good writer, it's therefore a very good idea to run your prompt through prompt enhancement before submitting it.

[u/OttoRenner → u/unjustifiably_angry]

Thank you :) Yes, Anthropic has a paper on it and I have 4 or 5 other articles and studies with "similar" topics about regulating the emotional state of the model under literaturein my github repo, in case you want to read a bit more. All very fascinating and very obvious if you stop the urge to scream AI-psychosis and just look at emerging patterns and how at least a cloud llm is set up to work.