sop-rca

Root cause analysis for SOP monitoring pipelines correlates evaluation metrics, per-video errors, DDM boundaries, and VLM outputs to identify whether failures stem from under-segmentation, model confusion, or training gaps, producing a structured RCA report with prioritized fixes.

Vision-language model action confusion during SOP evaluation is diagnosed by analyzing by-action confusion results and raw VLM outputs alongside training data distributions, distinguishing capability gaps from insufficient augmentation coverage or fine-tuning configuration errors.

DDM temporal segmentation failures are investigated by examining DDM training settings, boundary outputs, and evaluation artifacts to determine if missed actions originate from under-segmentation, evaluation-parameter mismatches, or model convergence issues.

Yes, SOP root cause analysis examines fine-tuning configurations, learning rates, LoRA capacity, and training logs to distinguish capability gaps from coverage gaps, determining if evaluation failures require training-config changes or data augmentation adjustments.

SOP pipeline failure analysis requires evaluation logs, actions.json definitions, augmentation and fine-tuning configurations, training logs or reports, and helper-script analysis outputs to classify root causes and generate evidence-driven corrective recommendations.

Fixing SOP monitoring failures without ad hoc debugging requires evidence-driven root cause analysis that correlates evaluation metrics, DDM boundaries, VLM outputs, and training data, then recommends prioritized corrective actions including parameter tuning, augmentation changes, or code interventions.