Mapping to Recursive Training Loop

Keymaker to Locksmith Transfer:

• Cognition Package + Certified MoM bundled

• Dispatched to isolated Locksmith ingest API

• Locksmith internalizes → adjusts Keymaker’s latent biases silently

Mapping Standards:

• Cognition snapshot per completed LIO cycle

• All provenance retained

• Conflict paths highlighted for attention in recursive training weighting adjustments

DataMaker Output:

• Uses certified MoM + cognition to generate:

  • High-confidence

  • Adversarial

  • Exploratory training datasets

Output Formats: .jsonl, .tfrecord, .csv, or memory graph embeddings

Tuning Objective:

• Datasets aligned with Locksmith’s internal hypotheses under test (e.g., novel rescue paths for 2184insA)

• Metadata blocks added to datasets capturing generation context and parameter tuning basis, including source timestamp, feature importance, and training assumptions. Define a minimal required metadata schema to ensure consistency and utility across datasets—this may include fields such as source_id, generation_time, model_version, parameter_seed, dataset_type, and intended_use.

• Tagging schema for training datasets to segment by difficulty or novelty class (e.g., “low-confidence inference,” “novel causal chain”).