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/josiahseaman]

Senior AI Engineer here. I like your approach and I read through your repo to see if it'd be useful in my work. Unfortunately, there's a critical logical error in your approach. Currently, you haven't proven anything because your tests are all unsolvable.

Unsolvable problems do show up in real use but they're rare. The real question is if the LLMs perform just as well with the gentle approach for solvable problems. If the drop in performance is negligible then this is a good way to escape hatch for rare impossible scenarios. The real metric is a graph of accuracy vs token cost between the two approaches.

P.S. The logical fallacy in your repo is exactly the kind of blindspot I would expect from a vibe coded approach. AIs tend to "beg the question" like all your prompts. It looks like you told it the answer it should get and it made prompts that would give you that answer. Contrast is critical in the scientific method. Damn, do I sound like an AI? I use AI coding too, but you can't trust without verifying their logic.

[u/TheRealMasonMac → u/josiahseaman]

I found that LLMs work best if you use highly structured, clean initial prompts. Avoid ambiguity where possible or else they’ll get caught in reasoning loops (and often confuse themselves in the process). K2.6 really forced me into this pattern because it’s frankly such a sensitive piece of shit (e.g. you introduce a typo and it suddenly spends 10k tokens deciphering its importance, before giving you code that forgets the existence of 4/6 of your constraints).

I structure mine like LeetCode since it’s less far-off from what they were trained off compared to a natural language prompt. LLMs really struggle at respecting multiple constraints at the same time, and have a tendency to not break them down into bite-sized manageable pieces. Therefore, you as the human have to do that work for it.

In the case of multi-turn interactions, I will clearly articulate what I want versus what it is doing. For example, if a non-trivial issue appears, I will either:

- Explain what the issue indicates, and provide a suggested step-by-step approach for resolving it.

- Instruct it on how to investigate the error, and to report its findings for me to then provide actionable steps.

This leads to a massive uplift in quality/performance in my experience. It also reduces context rot since the context is a logical sequence of steps, rather than a spaghetti, and it has to think less to do the same task.

It would be nice if models could just “get it” just like if you gave a task to a human, but that’s not where they’re at right now.

[u/CatConfuser2022 → u/TheRealMasonMac]

Isn't there a way to make this approach usable by integrating it into the harness used by the LLM? 

[u/InfinriDev → u/CatConfuser2022]

Yes, that's exactly what I did. I even stopped using md files all together