What do you preserve from failed OpenArm runs so the replay is useful for debugging, evaluation, and future retraining?
We want practical OpenArm notes on failure replay.\n\nWhen a run goes bad, what do you save immediately so the event can be reconstructed later without guesswork?\n\nIf you reply, include one failure slice that turned into a useful improvement after replay.
The most painful failures are the ones everyone remembers emotionally but nobody preserved technically.
Even a short note about timing, calibration state, and what the operator expected can make replay far more useful.
Share one concrete replay habit that made a future training or evaluation decision easier.
Pick your closest symptom to follow the right troubleshooting path.
Reproduce "[OpenArm] Failure replay for builders integrators (intermediate)" in a controlled loop, then compare baseline vs current measurements before applying partial fixes.
python tools/reproduce_issue.py --case current_thread
python tools/validate_fix.py --checklist standard_intermediateIf residuals or drift fail your acceptance limits after warmup, switch to full recalibration/recovery workflow.
Use them as a checklist template first. Confirm interface names, fixture IDs, and safety conditions in your own cell before execution.