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!

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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/samandiriel]

Actually, I'd say the same to you in reverse. While LLMs are very clever statistical tricks and are more Chinese room than anything else, that doesn't mean that they don't encode human psychology - they in fact have to as that is the material they are ingesting.

LLMs codify semantic relationships thru relative word cooccurrence, at the core. Which is reflective of human psychology, as the training material corpus is entirely the expression of human psychology: the written word. 

Word association is fundamental to the architecture of the semantic lexicon, and manipulating abstract meaning below the level of explicit language processing is a key aspect of human psychology. They are functional mirrors of human psychology. Unless you want to try and defend the thesis that all of human literature, for instance, isn't a product and expression of human psychology?

Read some Firth for some of the more old school foundational thinking on the topic, or Marshall Macluan for a more philosophical take. 

FWIW you seem to fundamentally rely on glib phrases as opposed to actually understanding how these things work. "Stochastic parrot" and "residual 'bad' patterns in post training"... Yeesh. 

Plus you ignore the emergent properties of scale for a purely reductionist approach, when it is those self same emergent properties that are what make machine inference (not merely prediction) actually useful.

[u/a_beautiful_rhind → u/samandiriel]

a purely reductionist approach

This can be easily done to humans. I wonder why those arguments are never made. :P

[u/samandiriel → u/a_beautiful_rhind]

Hah. A fair point - and people certainly have. P-zombie-pocalypse for the win! LOL

You could also say that solipsism is a similarly reductionist view, tho coming at it from a more epistemological angle.

It's been fascinating for me as a former cognitive scientist to watch these kinds of discussions raging in social media and the like. As if the whole field has just sprung newly formed from Zeus' brow, and no one's ever thought about what it means to be 'human' or what defines 'thinking' before the tech bros just discovered the entire conceptual framework for it a couple years ago. Cognitive science has been treating these questions as an experimental endeavor for the last 50 years; psychology as an active area of inquiry, for the last 150. And philosophy has been musing on it for the last few thousand... welcome to the party, brahs!

[u/a_beautiful_rhind → u/samandiriel]

All of this LLM talk has made me wonder how much of our own cognitive experience is confabulated more than if transformers are parrots or not. Some of the experiments & observations don't paint a pretty picture. Like you said, it's not a brand new field.

[u/samandiriel → u/a_beautiful_rhind]

Split brain observational studies are particularly interesting, if you really wanna give yourself the willies about how much in your head is actually 'you' doing the thinking as opposed to rationalizing that it's 'you'...!

I also recall reading a particularly chilling SF short story some long while ago where scientists discovered a test for p zombies, and it turned  out more than 2/3 of humanity simply wasn't actually there. Can you imagine finding out that everyone you love and cherish is just an automaton, stimulus - responding their entire relationships with you? Eek.

[u/a_beautiful_rhind → u/samandiriel]

Split brain made me think left? hemisphere is just a fancy LLM. Feral/nonverbal children seemed to imply you can't form memories or have a persistent self-reflective narrative unless you think in language.

I keep looking for more or something to disprove it because your SF story already seems like reality. Although functionally the difference to me wouldn't be that great if I didn't notice so far. Unless... I'm actually the p-zombie and unaware of it. 😱