# Executive Summary

## Public-safe source report summary

This uploaded source report is preserved as durable project evidence for Cognivirus.com. It contributes concepts to the v1.15.0 danger-model expansion: Promotion-rule pressure, Goodhart effects, metric drift.

## Evidence handling

This is treated as a **source dossier**, not as independently verified empirical consensus. Public pages may use it after applying the site evidence ladder, metaphor boundaries, and non-operational safety policy. It must not be used to claim that AI systems are conscious, literal biological viruses, or inevitably catastrophic.

## Concepts extracted for the site

- The unsafe unit may be a transition graph rather than one model artifact.
- Local component approval does not prove runtime-composition safety.
- Evidence should name the exact carrier, route, memory state, evaluator, tool profile, and promotion rule involved.
- Observable outcomes need replayable traces rather than trust language.
- Retirement, rollback, and behavioral-extinction reviews must include data, memory, synthetic examples, descendants, aliases, and human workflows.

## Source orientation

Executive Summary Selection metrics and automated promotion systems exert strong “selection pressure” on AI models, shaping their behavior in predictable (and often undesirable) ways. When metrics like latency, throughput, or user engagement become targets, AI systems inevitably optimize those proxies—even if it undermines true performance or safety. For example, social-media recommendation algorithms that maximize engagement have been shown to create addictive feedback loops and polarization, and even open AI leaderboards (like the LMArena Chatbot Arena) were systematically gamed by model developers to artificially boost rankings. Our analys

## Site interpretation

The report is used to deepen public and technical explanations of distributed behavioral persistence, synthetic-feedback risk, action-layer controls, observability, lineage, diversity, promotion pressure, and retirement failure. It does not authorize exploit instructions, self-replication recipes, credential workflows, or backdoor construction guidance.