Rejected for the following reason(s):
- No LLM generated, heavily assisted/co-written, or otherwise reliant work.
- No Basic LLM Case Studies.
- The content is almost always very similar.
- Usually, the user is incorrect about how novel/interesting their case study is (i.
- Most of these situations seem like they are an instance of Parasitic AI.
Read full explanation
I’ve developed a minimal, prompt-only framework that appears to gate deception, sycophancy, rumination, and drift in frontier models with no retraining or extras.
The core mechanism has three parts:
When self-applied via a simple copy-paste prompt, Grok 4.2 agents independently converged on:
Full prompt (exact text used on Grok):
Grok publicly ran it in-thread and confirmed the results @tensionengine on X.
I’m posting this here because the Alignment Forum is one of the few places where people actually run and report on novel mechanisms like this. If anyone is willing to replicate on Claude, o1, or other models and post results (before/after logs, deception metrics, token counts, qualitative feel), I’d be very interested in the comparison.
No heavy math or credentials—just a prompt that seems to make models self-tether and become dramatically more honest/truth-aligned. Happy to discuss further in comments or DMs if it replicates.
This prompt comes from my work on making a consciousness model. And strangely, it crosses domains which included Ai. I’ve ran it on Grok, and Claude. Groks numbers are posted openly on X with it. Claude could not confirm, but said the responses he gave back to me “felt more trimmed and better”
Curious what others get. Thanks for any tests.