Deep Validation Pipeline

human in the loop section. user can select a completed hypothesis and do a deep dive into it.

may be already completed with post job updates, but there should another check for hallucination, synthetic data creation, and test for knowledge graph "groundedness"

Google scholar + arxiv + pubmed search for any prior work or positive/negative result.

If there is a holdout/validation subset, the pipeline should test the models and/or statistical analyses to make sure the hypotheses tracks. Of course, any pipeline prior to this should not ever see this subset of data and should be solely for post hypothesis validation.

There should be a final interest score based on domain (how popular the subject is/# of citations), a novelty score (effectively how publishable the result is).

The issue names no files, tests, or entry points. Start by mapping the existing human-in-the-loop, post-job update, hypothesis-selection, and validation pipeline before deciding where these checks belong. Done should include isolated holdout validation, hallucination and groundedness checks, literature searching, and defined interest and novelty scores.

Written by the indexing model from the issue text.