Feature request: semantic audit during training loop #653
Description
I propose adding a semantic audit module to the training loop of nanoGPT.
This would allow the model to reflect on its outputs during training, improving coherence and conceptual alignment.
Motivation:
nanoGPT is a minimal and efficient training framework.
Introducing a semantic checkpoint — using Specter embeddings and FAISS — could help detect drift and reinforce epistemic consistency.
Proposed Implementation:
- Embed intermediate outputs during training
- Compare against a conceptual memory bank
- Trigger revision or flagging if semantic misalignment is detected
Inspired by https://github.com/elly99-AI/MarCognity-AI.git, a framework for cognitive orchestration.
Happy to contribute a prototype if aligned with the roadmap.