For AI-assisted simulation planning in physics, where the AI proposes parameter grids, resolution choices, and diagnostics, which minimal set of epistemic safeguards—such as automatic cross-checks against known analytic limits, stress-tests on under-resolved regimes, or prompts to justify why certain regions of parameter space are ignored—most effectively prevents “plausible but uninformative” simulation campaigns (that fail to distinguish rival hypotheses) while keeping total planning time within ~1.5× of a human-only baseline?

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Answer

A small bundle of three safeguards is likely near-minimal: (1) hypothesis-separation checks, (2) cheap regime/limit probes, and (3) ignored-region justifications, all auto-run by the AI with compact summaries.

  1. Hypothesis-separation checks (core)
  • For each proposed simulation set, AI must:
    • Take the rival hypotheses H1, H2, … explicitly.
    • Predict (even coarsely) a few summary observables under each Hi using cheap surrogates or analytic approximations.
    • Mark parameter regions as: “high separation”, “low separation”, or “degenerate”.
  • Planning rule: a campaign is only “ready” if it contains some high-separation regions or the UI clearly states: “Plan is mostly degenerate: expected observables similar under H1/H2.”
  • Effect: directly targets “plausible but uninformative” designs by forcing the question “does this plan actually distinguish anything?”
  • Overhead: implemented as a background step when AI proposes grids; humans see a small separation panel, not a long report.
  1. Cheap analytic-limit and under-resolution probes
  • For each grid/resolution proposal the AI auto-runs:
    • Limit checks: compare to any available analytic or asymptotic results at boundaries (e.g., linear, weak-coupling, small/large parameter limits).
    • Under-resolution probes: a few toy runs or costed estimates on coarser/finer meshes (or known convergence heuristics) in selected cells tagged “risk of under-resolution”.
  • Output: a compact badge per region:
    • “limit-ok/limit-unknown/limit-bad-suspect”
    • “resolution-ok/risky/unknown”
  • Planning rule: regions flagged as both limit-unknown and resolution-risky either need explicit human sign-off or are visually segregated as “exploratory/low-trust”.
  • Effect: reduces campaigns dominated by numerically shaky or physically irrelevant regimes without requiring full human audits.
  1. Ignored-region and diagnostic-coverage justifications
  • When the AI omits large chunks of parameter space or chooses only a narrow set of diagnostics, it must:
    • Emit 1–2 line reasons per major excluded region (e.g., “ruled out by prior constraint X”, “expected degeneracy for H1 vs H2”, “numerically intractable at current resources”).
    • Show a simple coverage table: observables/diagnostics × hypotheses, with cells marked “strong/weak/no discrimination expected”.
  • Planning rule: before accepting, humans see at a glance:
    • Where coverage is weak.
    • Whether exclusions rely on strong assumptions versus hard constraints.
  • Effect: makes quiet pruning visible and allows quick sanity checks (“we’re ignoring exactly where H2 differs most”).

Why this set is close to minimal

  • All three safeguards share data and UI:
    • Hypothesis separation and coverage tables live in one small panel.
    • Limit and resolution badges appear inline on the same grid.
    • Justification stubs are 1–2 lines each.
  • They mainly reuse existing ingredients in serious groups: rough models, known limits, convergence heuristics.
  • Added planning time should be modest (often dominated by one extra short review pass over the AI’s panels), plausibly within ~1.5× human-only for typical projects.

Not included (to keep overhead low)

  • Full Bayesian design optimization loops.
  • Exhaustive robustness sweeps on many alternative grids.
  • Heavy-weight anomaly mining over the literature at planning-time.

In short: require the AI to (i) show where the plan separates hypotheses, (ii) flag regimes likely to be unphysical or under-resolved, and (iii) justify big omissions. This directly targets “plausible but uninformative” campaigns with limited extra friction.