{
  "abstract": "SaaS capability assessments can become stale between questionnaires, while delivery telemetry offers incomplete and correlated evidence. Continuous assessment and Bayesian delivery analysis already exist, leaving uncertainty about how biased surveys and missing observations should influence capability estimates.We implement SAAS-MATURITY-TWIN, an anchored Gaussian state model that jointly filters capability proxies and persistent survey offsets. Source covariance prevents correlated observations from being counted independently. Conditional uncertainty supports forecasting and budgeted intervention screening, with action effects estimated from randomized synthetic cohorts. Ten seeds generate forty paired worlds spanning reference observations, extensive missingness, informative missingness, and increased noise. Reference latent root mean squared error is 0.1261, compared with 0.1452 for a tuned moving average; nominal ninety percent intervals cover 90.3 percent. Improved state recovery produces only small incident forecasting differences. Adaptive conformal inference and an adapted conformal PID controller are evaluated separately for observed telemetry, where noise shifts expose calibration limitations. A licensed TravisTorrent pilot evaluates build prediction on 13 074 held out builds from 224 projects. Logistic regression achieves a Brier score of 0.1289, close to random forests. These results support reproducible methodological testing, but do not establish real organizational maturity, intervention effectiveness, or production readiness. Independent calibration and longitudinal validation remain necessary.",
  "abstract_shown": true,
  "affiliations": [],
  "article_type": null,
  "arxiv": "2611.14541",
  "author_total": 1,
  "authors": [
    {
      "name": "Taimoor Ahmad"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/1yvqxg2zn2z5z.bib",
  "cited_by": 0,
  "doi": "10.48550/arxiv.2611.14541",
  "fulltext_url": null,
  "id": "1yvqxg2zn2z5z",
  "issns": [],
  "landing_url": "https://arxiv.org/abs/2611.14541",
  "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.AP"
    ]
  ],
  "openalex": null,
  "pdf_url": "https://arxiv.org/pdf/2611.14541",
  "pmcid": null,
  "pmid": null,
  "published": "2026-11-01",
  "publisher": null,
  "ris": "https://research.draconitic.com/paper/1yvqxg2zn2z5z.ris",
  "sources": [
    {
      "first_seen": "2026-11-13T06:21:06Z",
      "native_id": "2611.14541",
      "source": "arxiv",
      "url": "https://arxiv.org/abs/2611.14541"
    }
  ],
  "subjects": [
    {
      "field": "mathematics",
      "origin": "arxiv:stat.AP",
      "subfield": "statistics"
    }
  ],
  "title": "Bayesian Evidence Fusion for Capability Tracking-A Simulation Study and Historical CI Pilot",
  "title_text": "Bayesian Evidence Fusion for Capability Tracking-A Simulation Study and Historical CI Pilot",
  "type": "preprint",
  "updated_at": "2026-11-13T06:21:06Z",
  "url": "https://research.draconitic.com/paper/1yvqxg2zn2z5z",
  "venue": "arXiv",
  "versions": []
}