Round 2 verification audit for C060

Model: gpt-5

Claim: For Rabi-frequency errors (unwanted X_e X_e), the simulated 80-cycle error increase is solely due to slower convergence; the steady-state error is completely unaffected.

Source alignment: source/supp_content.tex:111-111 (Supp. Mat. S3)

Prior official verdict: verified with failure_reason None.

Executable evidence: run.py. Sandbox rerun logs: run.log.

Independent audit: I scanned the copied script for imports/shared helper dependencies and reran it through the sandbox. The code is self-contained in this attempt directory and targets the claim strategy: Simulate the steady-state singlet error versus fractional Rabi error and confirm the fixed-point error is unchanged while convergence slows.. I checked the relevant family model rather than relying only on exit status; the rerun is treated as one reproducibility input.

Decision: Round 2 verdict is verified with failure_reason None and limitations []. Notes: Rabi error rescales Omega -> area Phi=(1+eps)^2*pi/4. In the self-contained model the steady-state singlet fidelity is exactly 1.0 for all eps in {-0.1,0,0.02,0.05,0.1,0.2}: |Psi-> stays the unique fixed point, so the steady-state error is completely unaffected (as claimed). N0 grows with |eps| (7.62->17.4 at eps=0.2) and the finite-80-cycle fidelity drops (0.99998->0.99088), so the 80-cycle error increase is purely a convergence slowdown. This is a clean fixed-point fact, not a reproduced measurement -> verified.