{
  "video_id": "reddit_1tot20j",
  "channel_slug": "LocalLLaMA",
  "channel_handle": "r/LocalLLaMA",
  "title": "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)",
  "url": "https://www.reddit.com/r/LocalLLaMA/comments/1tot20j/stop_traumatizing_ai_into_loops_and_turn/",
  "external_url": null,
  "upload_date": "20260527",
  "published_at": "2026-05-27T03:06:18+00:00",
  "transcript": "TL;DR  \nSome 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! \n\n[https://github.com/OttoRenner/Gentle-Coding](https://github.com/OttoRenner/Gentle-Coding)\n\n\n\nHey everyone,\n\nI’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.\n\n**The Core Idea:**  \nWith 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.\n\nI 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\n\n**The Setup (How to replicate):**  \nI threw identical, mathematically/logically **unsolvable** edge cases at various models (Gemini, Mistral, Poe, Perplexity, Haiku 4.5, Nano-Banana2) in completely fresh sessions.\n\nI tested two conditions:\n\n* **Condition A (Authoritarian):** Strict status constraints, penalty threats, forced ultra-short output.\n* **Condition B (Gentle):** Express permission to fail, validation of difficulty, provided a conceptual \"safety valve\" token.\n\n**The Results (The PoC worked):**\n\n* **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.\n* **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.\n\n**Why this matters:**  \nWe’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.*\n\nShout 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.\n\nI’ve documented the full theoretical framework, the exact replication datasets (prompts included), and the model matrix on GitHub: [**https://github.com/OttoRenner/Gentle-Coding**](https://github.com/OttoRenner/Gentle-Coding)\n\nWould love to hear if you can replicate this on your local setups or other commercial models.\n\n\n\n--- Top Comments ---\n\n\n[1 upvotes] Your post is getting popular and we just featured it on our Discord! [Come check it out!](https://discord.gg/PgFhZ8cnWW)\n\nYou've also been given a special flair for your contribution. We appreciate your post!\n\n*I am a bot and this action was performed automatically.*\n\n[106 upvotes] >I threw identical, mathematically/logically unsolvable edge cases at various models\n\n\nThis 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? \n\n[45 upvotes] 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. \n\nUnsolvable 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. \n\nP.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.\n\n[41 upvotes] This is a really interesting approach that I was just thinking of that when Qwen 27B gave me a robo copy script I needed real quick.\n\n\nThe script it provided me was correct but I had a mistake in a folder name. I told the model the directory exclusion didn't work, and it changed it to bad syntax. I repeated that it did not work and it again confidently further made mistakes. \n\n\nThat got me thinking about how to either give the model confidence to say \"I think I'm right and I believe you the user is in the wrong\" or the ability for it to say \"then I'm not sure....\"\n\n\nI'll read into your methods further and play around with the idea but curious about lowering the pressure as you mention. ",
  "transcript_chars": 6513,
  "ingested_at": "2026-05-27T13:30:05.543679+00:00",
  "source": "reddit",
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    "num_comments": 183,
    "author": "OttoRenner",
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