analysis-campaign

Automating follow-up research analysis involves configuring a campaign with a specific quest ID and campaign type to execute ablations, robustness checks, and error analysis automatically. This streamlines evidence gathering and routes next steps based on aggregated findings.

Evidence aggregation in research analysis is the process of collecting decision-relevant findings from bounded follow-up campaigns. It routes the next experimental step by consolidating data from robustness checks and failure analysis to guide research conclusions.

To start an analysis campaign, execute the command `start-analysis-campaign --quest <quest-id> --campaign-type <campaign-type>`. You must provide a clear quest identifier and specify the campaign type to initiate the automated follow-up analysis process.

Yes, follow-up analysis supports bounded resource allocation to prevent overuse during research campaigns. It ensures that ablations and failure analysis remain resource-aware, maintaining efficient evidence gathering without exceeding allocated limits.

The best way to manage resources during error analysis is using a resource-aware campaign management system. It bounds resource allocation automatically, ensuring thorough failure analysis and robustness checks without overuse.