ResearchStrong architectural inferencev1.22.1

In plain English

This page preserves research summaries and source notes. Summaries distinguish direct findings from Cognivirus.com interpretation.

  • Why this matters: AI risk can come from the whole arrangement, not one obvious model.
  • What to look for: data, memory, routes, adapters, tools, evaluators, updates, and rollback paths.
  • Technical version below: the expert terminology remains available and is linked through the glossary.

Self-Replication Threat Assessment Synthesis

Evidence levelStrong architectural inferenceTechnical label: Architectural inference

The self-replication reports use strong threat language. A behavior pattern that can survive, move, or reappear across a changing AI system. Open glossary definition extracts a conservative engineering question: which transitions permit a system to create, promote, or preserve successors without adequate external authorization?

Safe synthesis

The reports distinguish direct copying, recombination, deprecation, and persistence through memory or data. They also stress lifecycle controls: versioning, rollback, A record of where a component or behavior came from. Open glossary definition, sandboxing, least privilege, continuous monitoring, and kill-switch design.

What public content omits

The site avoids operational exploit sequences, resource-hijacking procedures, and propagation instructions. It retains the defense-relevant structure: process separation, pre-action controls, signed evidence, rate limits, provenance, and Restoring not only a model artifact but the relevant router, prompts, memory state, tool permissions, evaluator version, deployment alias, and data dependencies. Open glossary definition.

Research questions

Open questions include how to measure Evidence that a behavior is no longer expressible across active artifacts, descendants, memory, routes, compositions, and retained training material. Deleting one model is not sufficient evidence. Open glossary definition, how to evaluate path-dependent skill chains, how to prevent evaluator drift under automation, and how to preserve human authority when systems become highly useful.