# 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.