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.
Hi HN,
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.