In physics groups that already use structured hypothesis cards and handoff protocols for AI-assisted hypothesis generation, which additional, low-friction epistemic safeguards—such as mandatory “disagreement fields” where humans must log any unease about an AI proposal, or automatic surfacing of the 3 most similar past hypotheses and their eventual empirical fates—most effectively prevent gradual reclassification of speculative AI ideas into “standard assumptions” over the lifespan of a project or collaboration?

Answer

Most effective low-friction safeguards are ones that (1) keep visible the speculative status over time and (2) continuously tie each hypothesis to conflicts and outcomes. Four patterns:

1. Mandatory human “disagreement/unease” field

• Each AI hypothesis card has one short, required field: “What feels off / what could fail?”.
• Status of this field (empty not allowed; “none” discouraged) is kept visible in all downstream views.
• Effect: forces at least one explicit doubt; makes it harder to later treat the hypothesis as a default assumption.

2. Auto-link to past-similar hypotheses + fate badges

• System surfaces 3 most similar past hypotheses with simple fates: {supported, mixed, refuted, inconclusive}.
• Cards show a compact badge summary (e.g., “similar: 1 refuted, 2 mixed”).
• Effect: keeps a local “base rate” of failure in view; resists quiet promotion to standard assumption.

3. Time-stamped status + promotion gates

• Each card has a status: {speculative, working, provisionally adopted, standard} with date and reason.
• Promotion requires a brief justification tied to concrete checks (e.g., key simulations, benchmarks) and is logged.
• Effect: any reclassification leaves an audit trail; later readers can see if promotion was evidence-based or drift.

4. Periodic “assumption review” views

• Every few months, auto-generate a list of hypotheses whose status is ≥“working” but lacking: (a) explicit tests run, or (b) updated unease notes.
• Group quickly re-tags: keep speculative, downgrade, or justify promotion.
• Effect: prevents old AI ideas from ossifying just by age and reuse.

Low-friction implementation rule of thumb: one short human text field + one or two auto-computed fields (similar-hypothesis fate badges, status+date) per card, plus very lightweight periodic review. These add little overhead but make it much harder for AI-generated ideas to silently become “standard.”