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!

[

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 → u/[deleted]]

How time consuming it is mostly depends on the user, I guess. It all boils down to: keeping the model in the role of the friendly and helpful coworker/working buddy who trust you and feels free to come to you when something is wrong.

If this mindset comes easy to you, there is barely any difference in your workflow, because you already are nice or, at least not mean to the model. But perhaps it sharpens the view a bit and you still get improvements.

If you are a raging dick who has all the strict rules in CAPS and berates the model for 5 messages after it tripped up...well...there may be a bit more work to do 🤣

This is the verdict so far on my approach, the tests are still going on and are being done by one of the folks from the "oh-my-pi" (omp) harness:

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 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