Personal Corpus Model Training
Model fine-tuned on user-authored and user-curated artifacts under governance policy, with lineage tracking from training data through derived outputs. The agent's knowledge is grounded in the user's own work rather than general public datasets.
The training corpus is assembled from the lineage field by selecting artifacts admissible under the corpus policy object, filtering for modality compatibility, and applying any declared redaction rules. A parameter-efficient fine-tuning operation updates a bounded subset of the model's parameters so the operation is feasible within the device's local compute envelope. The user's accumulated body of work is internalized in the parameter values rather than consulted as external context at inference time.
Disclosure Scope
This article describes subject matter disclosed in U.S. Provisional Application No. 64/070,239. As a provisional disclosure, the specific mechanisms are described in illustrative embodiments and may differ in any nonprovisional application claiming priority to it.
