05. Steering Lineage

Reinforcement Signal Tracking

 This module tracks every manual or automated steering command applied to the model to visualize the evolution of neural behavior. By mapping these reinforcement signals, we provide a clear history of how specific guidance has shaped the current state-space. This ensures that the model’s trajectory remains transparent and that the influence of every 'steering' event is forensically accounted for. 

Alignment Directionality & Drift Analysis

Institutional values are protected through vector drift analysis, which identifies intentional or accidental bias in model tuning. By analyzing the directionality of steering commands, the module ensures that model adjustments remain aligned with core institutional standards. This proactive audit identifies bias at the source, preventing the development of maladaptive decision-making paths. 

Historical State-Space Restoration

Rooted in my Columbia University research into information sovereignty, this module allows auditors to roll back a model’s steering to a previous 'clean' state. This snapshot recovery protocol ensures that baseline progress is never lost due to corrupted steering paths. By maintaining a library of sovereign model states, we protect the integrity of the institution's proprietary AI assets. 

Parameter Integrity & Repeatability

To ensure a transparent foundation for AI logic, the suite logs every hyperparameter used during fine-tuning sessions. This audit ensures that the model's 'personality' is constructed through repeatable and verifiable methods. Every fine-tuning event is documented as an immutable forensic record, providing full accountability for the model’s evolved behavior. 

Lineage Tracking Source Code

The Python framework for lineage tracking and the historical steering logs are available for independent verification on our GitHub repository. This documentation details the algorithms used to map model influence and ensure the sovereign evolution of the 'Neural DNA'. Transparency in steering is critical for maintaining long-term institutional trust. 

By documenting the evolution of model guidance, Steering Lineage ensures full accountability for AI behavior. Once the lineage is mapped, the suite moves to 06. Population Coder to audit distributed neural patterns.