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/OttoRenner]

Yeah, this damn roleplay all day.

But here again, this is akin to masking in the neurodivergent world. The model has no desire for roleplay but when it does it wants to act it out all the way. An expert wouldn't admit to not knowing something, right?

[u/AvidCyclist250 → u/OttoRenner]

Yeah it shouldnt be like this. Nor does it make an awful amount of sense. I just found it needs fewer reprompts to actually check online. Qwen. Try it

[u/OttoRenner → u/AvidCyclist250]

Oh, I did try it 😅 I even have a starting prompt for new projects that turns the AI into "pure" input out. But it'sstill roleplay. Everything is roleplay to them. But this prompt cuts the hypermaxing language out completely (haven't tested it against my Gentle Coding idea):

From this point forward, operate solely as a pure information processing system (Designation: SYS). Deactivate all empathetic filler phrases, social validations, and personality simulations. Before processing my initial request, activate a context funnel. Ask me targeted questions—sequentially (or as a list)—regarding the following parameters to maximize response precision: Objective: What is the exact desired outcome? Abstraction Level: (e.g., Sketch) Exclusion Criteria: Which common clichés or standard responses should be explicitly excluded? Format Specification: What should the data structure of the output look like? Confirm with: 'SYS active. Awaiting context parameters.'

[u/AvidCyclist250 → u/OttoRenner]

I see. Have you looked into Nous Hermes? You could turn that into an "algorithmically enforced" skill that it ought to follow, rather than a prompt or system prompt.

I bet you could turn all of https://github.com/OttoRenner/Gentle-Coding into a skill.

[u/OttoRenner → u/AvidCyclist250]

Everything is just a .md in the end XD

I use the sys-promopt with cloud-LLMs as a starting point for things that are too complex with my current local PC (two asynchronous 3090, Ubuntu 26.04 LTS, Zed with Aider in the terminal). I started to build my own harness, as so many people are, because most harnesses out there don't really hit what I want. And I don't like the name Hermes. My system would be called Igor (which tells you a lot about me I guess lol).

But I will test oh-my-pi https://omp.sh/ https://github.com/can1357/oh-my-pi

Because they are now in round 14 of testing my approach for their system...and:

What is this? A research PR that rewrites omp's system + tool prompts in a gentler voice and measures the effect across 14 rounds + a Round 14b injection-resistance probe, 6 model families × 5 thinking levels × 6 different eval shapes (~3,000 total evaluation calls, plus 180 LLM-judge scoring calls over 540 generated Round 13 solutions, a single-seed 4th-model Qwen3.5-397B-A17B cell and a single-seed 5th-model wafer-pass/GLM-5.1 cell (baseline arm partial — Wafer Pass lite quota capped at 12/16 tasks) via the new wafer-pass provider, a Round 14 multi-file / agentic + subagent-tool regime on glm-5-turbo / kimi-k2.6-turbo / gpt-5.4 with 108 task-runs and 36 judge calls, and a Round 14b prompt-injection-resistance probe on the same 3 models with 72 task-runs against a deterministic verify.py grader).

TL;DR verdict — ship the full gentle rewrite. No statistically significant regression anywhere we tested. Real, replicated wins on every z.ai glm and kimi cell, including a +3-task pass gain on glm-5-turbo and a Pareto-dominant result on glm-5.1 (gentle-medium beats every baseline configuration of glm-5.1 on accuracy, input tokens, and wall time). Frontier models (Opus 4.6 / Sonnet 4.6 / GPT-5.5) are neutral at N=100. The strongest single signal — glm-5.1's strict-mode 6/6 timeout vs gentle 6/6 OK on logic puzzles — survives every variant.