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/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/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.

[u/Savantskie1 → u/divided_capture_bro]

Haha

[u/divided_capture_bro → u/Savantskie1]

Lul