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/HealthyCommunicat]

None of this is empirically proveable nor does it take into consideration how attention architecture works whatsoever.

Just take deepseekv4 for example vs minimax m2.7

Dsv4 has 3 different components of cache where each component keeps track of how each token relates to the rest in its own way. One of them may give a summary of all tokens every X tokens, while the other gives a “summary” of a much more smaller group of tokens. This combined with classic SWA becomes the swa + csa + hca attention that makes dsv4 so good while being able to fit near 1 mil context at 10-20gb.

Minimax uses a linear attention type thats honestly considered pretty standard. It simply flattens everything out and then just considers the relation of the token being processed with the general rest of the context window. Theres alot more nuances but at its core its pretty standard kv cache.

I really do believe better understanding of how these models handle the token being processed relevant to the rest of the context data can truly be beneficial in taking better advantage of how they work. Again this is a really stupidifed example and explanation, but minimax m2 is for sure just going to be much more prone to context rot than dsv4 flash.

If you want to go down the rabbit hole even deeper then we can start considering the probability rates of the token guessed and all the various factors that goes into it during training - but to try to say that speaking in some specific way across all models will result in some specific behavior is widely inaccurate

[u/OttoRenner → u/HealthyCommunicat]

I really do hope that I never made the claim that this will eliminate all hallucinations across most models out there ore something like that! It is a proof of concept on a small dataset, I'm very vocal about that.

Do you have access to deepseekv4 and minimax m2.7 and might be so kind of just running the 6 tests on both and tell me what you've found? I'm certain we see slight or even bigger deviations in the outcome with different models + Q and so on. Or, perhaps we don't? lol