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Feature request: Semantic checkpoint in training loop #5059
Description
Activity
@elly99-AI thanks for the feature request! To make it more clear, can you please provide a small snippet of how it would look like?
Thanks @vfdev-5 — here’s a minimal sketch of the idea:
def semantic_checkpoint(outputs, memory_bank, threshold=0.8): emb = embed(outputs) # JAX-compatible encoder sim = cosine_similarity(emb, memory_bank) if sim < threshold: log_drift(outputs, sim) revise(outputs)
Goal: inject semantic reflection into the training loop.
Detect conceptual drift, reinforce epistemic stability.
Inspired by MarCognity-AI — where drift is signal, not noise.@elly99-AI thanks for the code. This looks a bit specific to a certain task and less generic (Flax is a task agnostic framework).
In general, the training loop is not predefined by Flax, users usually code it in their project using Flax to make models, optimizer etc.If you think your idea can be useful for other users, you can make a small runnable example and post it in discussions.
In general, for any code additions into Flax API, we have to think, iterate and agree on the new API, provide reasonable examples for better understanding how the idea would be used in Flax, etc. Your code snippet looks very unrelated to Flax.
I propose adding a semantic checkpoint module to the Flax training loop.
This would allow models to reflect on intermediate outputs and reinforce conceptual alignment.
Motivation:
Flax is a flexible framework for JAX-based training.
A semantic checkpoint — using embeddings and memory — could help detect incoherence and improve epistemic stability.
Proposed Implementation:
Inspired by https://github.com/elly99-AI/MarCognity-AI.git