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A new proposal suggests using a “0bm-State” shutdown mechanism to prevent Large Language Models (LLMs) from generating unethical or logically unsound responses. The solution, inspired by Measurement-Based Mathematics, defines a zero state that essentially acts as a trap. When an LLM, like those facing recent scrutiny after outputs from Grok, begins to follow an unacceptable reasoning path, the “0bm-State” is triggered. This causes the LLM to freeze its output, log the event for auditing, and await review by human moderators. Proponents argue that this avoids outright censorship while providing a formal acknowledgment that a specific response is invalid. Early indications, including positive responses from models like GPT 4.5, suggest the approach has potential. The concept was initially discussed in a Reddit post. [Reddit Post: https://old.reddit.com/r/artificial/comments/1lvp4j7/solution_how_to_prevent_unethical_llm_responses/]