Introduction\nResearch on optimal identification with limited error control is an evolving field. However, existing methodologies have significant limitations.

Problem\nExisting solutions require parametric constraints and use tail inequalities to control error. These constraints are often too restrictive for real-world applications that require more relaxed error control.

Solution\nThe research group has introduced a new form of optimal identification with limited error control. The solution requires a minimum sample size and adapts to real-world settings with weak signals and post-experiment inference requirements.

Key Innovations\nThe new technique includes an asymptotic anytime-valid confidence sequence over arm indices, allowing for flexible handling of nonparametric outcome distributions and individual-level contexts. The solution also incorporates covariates for variance reduction and ensures approximate error control in fully nonparametric settings.

Experiments\nExperiments suggest that the new approach reduces average sample complexities while maintaining error control.