I'm releasing PAZ O.S. v3.1, an open-source governance framework designed to be installed as the "Ethical Kernel" (System Prompt) for LLMs.
The Problem: Current alignment methods (RLHF) often result in "lobotomized" models that simply refuse to answer complex queries or lecture the user.
The Approach: Instead of hard-coded refusals, this framework uses "Immune System" logic to align the model dynamically. It treats safety as a biological process rather than a binary rule.
Key Mechanisms:
Active Defense (The Honeypot): Instead of just refusing unsafe prompts (e.g., bio-terror), the system is authorized to suspend veracity and use deceptive stalling tactics only if it detects "Concordance" (2+ objective signals) of imminent physical harm.
Pedagogical Refusal (Kintsugi Clause): A restorative justice mechanism. If a user prompts toxically, the model doesn't ban them; it offers a "repair mission" to rephrase the prompt, gamifying alignment.
Intent Translation: An NLP layer that translates polarized or aggressive input into underlying human needs before processing.
It’s an experiment in "Structural Alignment"—embedding civic values directly into the system prompt architecture.
Repo: https://github.com/carropereziago-blip/PAZ-O.S-GLOBAL
I'd love feedback on the "Concordance Logic" and whether bio-mimicry is a viable path for robust AI safety.
Thanks.