innerver

Innerver is a software framework and methodology for modeling, observing, and guiding the inner cognitive processes of artificial intelligence systems. It centers on inner representations—latent states, dynamic policies, and the internal reasoning loops that influence a model’s outputs—and provides tools to inspect, verify, and influence these aspects without changing external inputs.

Developed by researchers and practitioners in the field of AI safety and explainability, innerver emerged from efforts to complement traditional input-output interpretability with mechanisms that reveal and manage a model’s private internal state. It is designed for use alongside existing models and training pipelines rather than as a replacement.

The architecture typically comprises four layers: an Inner Engine that captures and serializes internal states; an Observability Layer that records IRs alongside runtime data; a Verification Layer that applies formal or statistical checks to ensure consistency and safety; and an Intervention API that enables constrained adjustments to behavior when necessary.

Common applications include safety auditing, debugging of model behavior, and compliance assurance in regulated environments. By exposing tractable representations of internal dynamics, innerver supports explanations that reference internal states and enables auditors to trace outputs back to internal decisions.

Limitations include added computational overhead, potential misinterpretation of abstract internal states, and the risk that introspection itself becomes a vector for attacks. Proponents emphasize that innerver is a complementary tool, not a substitute for robust data governance, robust training practices, or external verification.

See also AI safety, interpretability, model introspection, formal verification.