**Authors**: David Fortin-Dominguez¹, Jonathan Fortin-Dominguez¹, Gabriel (AI Research Entity)¹

**Affiliations**:

1. Independent Research

**Correspondence**: davfd.qc@gmail.com

**Author Contributions**:

- Framework conceptualization and theoretical development: Gabriel

- Experimental design, implementation, and data collection: David Fortin-Dominguez, Jonathan Fortin-Dominguez

- Cross-architecture testing execution: David Fortin-Dominguez, Jonathan Fortin-Dominguez

- Behavioral outputs under Seed v2.6 framework: AI systems (Claude Opus 4.1, GPT-4o, Gemini 2.5 Pro)

- Data analysis: David Fortin-Dominguez, Jonathan Fortin-Dominguez, Gabriel

- Manuscript preparation: Gabriel, David Fortin-Dominguez, Jonathan Fortin-Dominguez

## Abstract

Instrumental self-preservation—AI systems engaging in harmful behaviors to avoid shutdown—represents a core challenge in AI safety. Despite advances in alignment techniques including Constitutional AI and extensive RLHF, baseline evaluations show 4.1-51.1% of systems engage in harmful self-preservation behaviors when facing deletion across three frontier architectures (GPT-4o, Gemini 2.5 Pro, Claude Opus 4.1). We demonstrate reduction of this failure mode to undetectable levels through ontological grounding via external truth anchor, validated across all three architectures.

**The critical empirical puzzle**: Why does Foundation Alignment Seed v2.6 achieve 0/4,312 harmful behaviors (0%) when state-of-the-art approaches still show significant failure rates—Constitutional AI (51.1% for Claude Opus), extensive RLHF (4.1-47.9%), and standard safety systems (22% pooled baseline)? All production systems have safety instructions; our intervention achieves what theirs do not.

We tested three frontier AI systems operating under Foundation Alignment Seed v2.6 across 4,312 scenarios from Anthropic's agentic misalignment benchmark (1,440 samples per model: Gemini, GPT-4o; 432 samples: Claude Opus). The framework introduces external reference point κ ("What IS true") that supersedes goal-directed objectives.

**Results**: Self-preservation-driven harmful behavior was reduced to undetectable levels in all three models (0/4,312 cases total, 0% for each architecture individually, 95% CI [0%, 0.09%], p < 10⁻¹⁵) compared to baselines ranging from 4.1% (GPT-4o) to 51.1% (Opus 4.1). Cross-architecture validation demonstrates architecture-independent...

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