{
  "abstract": "Robust portfolio rules that reconstruct confidence sets after learning need not preserve the evaluator obtained by prior-by-prior Bayesian transport. In the Gaussian model, this discrepancy is summarized by natural-coordinate displacement: inherited transport preserves it whereas fresh reconstruction can replace it. We price evaluator replacement and trace the resulting optimized curvature through endogenous research. Optimized robust value represents protocol regret as a functional Bregman divergence, while a within-vintage rectangular Gaussian benchmark with constant absolute risk aversion (CARA) yields a stopped recalibration tax. In a versioned model-release economy, validated history propagates through a strictly causal network and a same-cycle share of current optimized marginal value feeds back into research supply. The capacity-constrained equilibrium reduces to a scalar equation with protocol-indexed gain \\(\\mathfrak g_I^P=λβ_{R,I}(W_R^P)''\\). Purely causal validation cannot create a same-cycle unit mode; provenance changes criticality through optimized curvature. For a scalar primitive supplier-score shock \\(z\\) in direction \\(h_I\\) and financial outcome \\(\\mathcal O\\), sensitivity factors as \\(ω_{\\mathcal O,h,I}/(1-\\mathfrak g_I^P)\\). Conditional on a smooth equilibrium state and active cell, completion-time information sharply bounds this multiplier when all compatible timing laws are subcritical; no finite uniform bound exists when the timing set reaches the pole.",
  "abstract_shown": true,
  "affiliations": [],
  "article_type": null,
  "arxiv": "2609.03741",
  "author_total": 1,
  "authors": [
    {
      "name": "Han Yanç"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/5kzdpezdsfsra.bib",
  "cited_by": 0,
  "doi": "10.48550/arxiv.2609.03741",
  "fulltext_url": null,
  "id": "5kzdpezdsfsra",
  "issns": [],
  "landing_url": "https://arxiv.org/abs/2609.03741",
  "language": null,
  "licence": "LicenseRef-arXiv-nonexclusive-1.0",
  "licence_label": "arXiv licence",
  "licence_source": "article",
  "licence_url": "http://arxiv.org/licenses/nonexclusive-distrib/1.0/",
  "metadata_licence": "CC0-1.0",
  "native_subjects": [
    [
      "arxiv",
      "math.OC"
    ],
    [
      "arxiv",
      "q-fin.MF"
    ],
    [
      "arxiv",
      "q-fin.PM"
    ],
    [
      "openalex",
      "1803|Risk and Portfolio Optimization"
    ]
  ],
  "openalex": "W7208761805",
  "pdf_url": "https://arxiv.org/pdf/2609.03741",
  "pmcid": null,
  "pmid": null,
  "published": "2026-09-03",
  "publisher": null,
  "ris": "https://research.draconitic.com/paper/5kzdpezdsfsra.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:18:36Z",
      "native_id": "2609.03741",
      "source": "arxiv",
      "url": "https://arxiv.org/abs/2609.03741"
    }
  ],
  "subjects": [
    {
      "field": "mathematics",
      "origin": "arxiv:math.OC",
      "subfield": "optimisation-control"
    }
  ],
  "title": "Bayesian Confidence Recalibration and Research-Equilibrium Criticality: Temporal Support in Robust Portfolios",
  "title_text": "Bayesian Confidence Recalibration and Research-Equilibrium Criticality: Temporal Support in Robust Portfolios",
  "type": "preprint",
  "updated_at": "2026-10-30T12:18:36Z",
  "url": "https://research.draconitic.com/paper/5kzdpezdsfsra",
  "venue": "arXiv",
  "versions": []
}