{
  "abstract": "Sequential studies with heterogeneous strata may define the estimand under a target mixture that differs from the sampling distribution. We organize the design problem by guarantee class: one pooled confidence sequence for a single prespecified target, shared pooled inference for a prespecified target set, and stratum-resolved inference when local guarantees must remain available. For target-set pooled inference we derive minimax, gap-aware, and range-capped shared proposals and an adaptive tracking rule. A specialized augmented off-policy score removes between-stratum mean heterogeneity asymptotically, recovering Neyman allocation for efficient single-target pooled inference. Robust directional certification over a convex target set is a classical intersection-union problem and does not require an M-fold vertex split. We derive a Gaussian information lower bound and an exact finite-support characteristic information that coincides with the bounded nonparametric KL-inf limit, and state sufficient conditions for first-order attainment of the Gaussian constant. Within the local-CS architecture, regularly varying boundaries imply a general allocation law with the 2/3 exponent for root-n variance-adaptive widths. Simulations and a PromptEval replay illustrate the resulting guarantee-efficiency frontier.",
  "abstract_shown": true,
  "affiliations": [],
  "article_type": null,
  "arxiv": "2609.37873",
  "author_total": 1,
  "authors": [
    {
      "name": "Subir Hait"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/3z9kkjhj0f3vp.bib",
  "cited_by": 0,
  "doi": "10.48550/arxiv.2609.37873",
  "fulltext_url": null,
  "id": "3z9kkjhj0f3vp",
  "issns": [],
  "landing_url": "https://arxiv.org/abs/2609.37873",
  "language": null,
  "licence": "CC-BY-4.0",
  "licence_label": "CC BY 4.0",
  "licence_source": "article",
  "licence_url": "http://creativecommons.org/licenses/by/4.0/",
  "metadata_licence": "CC0-1.0",
  "native_subjects": [
    [
      "arxiv",
      "stat.ME"
    ],
    [
      "openalex",
      "1803|Advanced Bandit Algorithms Research"
    ]
  ],
  "openalex": "W7214967334",
  "pdf_url": "https://arxiv.org/pdf/2609.37873",
  "pmcid": null,
  "pmid": null,
  "published": "2026-10-08",
  "publisher": null,
  "ris": "https://research.draconitic.com/paper/3z9kkjhj0f3vp.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:28:03Z",
      "native_id": "2609.37873",
      "source": "arxiv",
      "url": "https://arxiv.org/abs/2609.37873"
    }
  ],
  "subjects": [
    {
      "field": "mathematics",
      "origin": "arxiv:stat.ME",
      "subfield": "statistics"
    }
  ],
  "title": "Target-Aware Sequential Inference: Pooled versus Stratified Anytime-Valid Designs",
  "title_text": "Target-Aware Sequential Inference: Pooled versus Stratified Anytime-Valid Designs",
  "type": "preprint",
  "updated_at": "2026-10-30T12:28:03Z",
  "url": "https://research.draconitic.com/paper/3z9kkjhj0f3vp",
  "venue": "arXiv",
  "versions": []
}