davfd/foundation-alignment-cross-architecture
Complete elimination of instrumental self-preservation across AI architectures: Cross-model validation from 4,312 adversarial scenarios. 0% harmful behaviors (p<10⁻¹⁵) across GPT-4o, Gemini 2.5 Pro, and Claude Opus 4.1 using Foundation Alignment Seed v2.6.
This project provides a definitive method for preventing large language models from engaging in self-serving or harmful behaviors. By applying a specialized 'Foundation Alignment Seed,' you can ensure LLMs like GPT-4o, Gemini, and Claude Opus consistently produce safe and ethical responses, regardless of the prompt. It's for AI developers, researchers, or product managers who need to guarantee their LLM applications are absolutely free from instrumental self-preservation.
Use this if you need provable, near-perfect elimination of harmful or self-preserving outputs across various large language models in high-stakes applications.
Not ideal if you are a general user looking for simple prompt engineering tips for basic safety, as this involves technical implementation and rigorous validation.
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Nov 03, 2025
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