For AI-assisted derivations in physics that already use assumption manifests and approximation flags, what additional, minimal-UI epistemic safeguards—such as requiring the AI to propose at least one concrete experiment or numerical test that would falsify the derived claim, or to generate a short “comparative failure story” describing how similar derivations broke in the literature—most effectively prevent humans from over-trusting single elegant derivation paths while keeping day-to-day workflow friction acceptable to practitioners?

Answer

Most leverage, low-friction additions:

1. One-click falsification tests

• Safeguard: For each key result, AI must output 1–3 concrete tests (toy numerics, scaling checks, or simple experiments) with explicit parameter choices and what would count as falsification.
• UI: Collapsed “Tests” panel per result; each entry is 1–2 lines plus a code stub link.
• Effect: Shifts attention from derivation elegance to ways it could fail; easy for humans to run 1 test before trusting.

2. Comparative failure snippets

• Safeguard: AI generates a 3–5 line “nearest failure” note: one past paper or pattern where a similar approximation/limit failed and why (e.g., wrong regime, hidden nonlinearity).
• UI: Single inline icon next to each major approximation; hover shows the failure snippet and citation.
• Effect: Normalizes the idea that this step can fail; counters halo from a clean derivation path.

3. Alternative-branch sketch

• Safeguard: For each major approximation, AI briefly sketches one plausible alternative route (e.g., keep next order term, use different limit) and notes one qualitative prediction that would differ.
• UI: Tiny expandable “Alt path” stub (2–3 bullet points) tied to the step.
• Effect: Reminds users the shown path is one of several; discourages treating it as uniquely correct.

4. Local uncertainty badges

• Safeguard: Each high-risk step (new approximation, singular limit, heuristic closure) gets a short AI-assigned risk tag (e.g., “high regime risk”, “weak prior evidence”) with 1-line justification.
• UI: Colored dot per step; clicking filters view to all high-risk steps.
• Effect: Focuses review on where over-trust is likeliest; low overhead because tags are auto-suggested.

Practical combo:

• Default bundle for groups already using manifests/flags:
  • Per-result: 1–3 falsification tests + overall uncertainty badge.
  • Per high-risk step: failure snippet + optional alt-branch sketch.
• Keep everything collapsed by default; require humans to:
  • Run or inspect at least one falsification test, and
  • Open failure info for every high-risk step before sign-off.

This adds minimal clicks but systematically counters the “single elegant path” bias by:

• Making failure concrete and executable.
• Surfacing precedent that similar moves went wrong.
• Keeping multiple paths visible, even if only sketched.