{
  "id": "oai:papers.qnfo.org:quniverse-fleet-lessons",
  "slug": "quniverse-fleet-lessons",
  "title": "Quantitative Integrity and Reliability Modeling of the Quniverse Autonomous Research Fleet",
  "authors": [
    "Rowan Brad Quni-Gudzinas"
  ],
  "abstract": "Autonomous research fleets must keep serverless worker scripts, databases, and storage buckets operational while publishing cost and performance metrics that external stakeholders can trust. Existing studies treat fleet sizing, failure propagation, and claim validation separately, leaving operators without a unified decision framework. This work fills the gap by presenting an integrated analytical model that jointly captures reliability, monetary impact, and claim‑integrity auditing for the Quniverse fleet. The model combines a Bernoulli failure‑propagation formulation with a mitigation factor derived from the fleet’s own self‑heal and handoff counts, aggregates resource consumption, computes a weighted composite autonomy score, and recomputes every quantitative claim in the fleet’s retraction notice with explicit arithmetic and unit‑dimension checks. Using the count‑derived mitigation factor α = 1,889/(1,889+1,264) ≈ 0.5991, the model yields an expected E[F]=1.732 unresolved worker‑script failures per month (standard deviation ≈1.284), a corpus productivity of $9.65$ papers per active project, a high‑severity gap proportion of 11.9\\%, and a baseline composite autonomy score of $4.26$ that rises to $4.66$ when the per‑worker failure probability is halved. The billing‑unit error inflates the perceived monthly spend to \\$11,000, representing a $73.3$‑fold violation of the \\$150 cap, a $122.2$‑fold violation of the \\$90 per‑30‑day gate, and a 22.2\\% exceedance of the gate after correcting the spend to \\$110. These analytically derived levers enable platform operators, funders, and auditors to size monitoring, budget accurately, and enforce claim integrity at TRL 2.",
  "abstract_markdown": "Autonomous research fleets must keep serverless worker scripts, databases, and storage buckets operational while publishing cost and performance metrics that external stakeholders can trust. Existing studies treat fleet sizing, failure propagation, and claim validation separately, leaving operators without a unified decision framework. This work fills the gap by presenting an integrated analytical model that jointly captures reliability, monetary impact, and claim‑integrity auditing for the Quniverse fleet. The model combines a Bernoulli failure‑propagation formulation with a mitigation factor derived from the fleet’s own self‑heal and handoff counts, aggregates resource consumption, computes a weighted composite autonomy score, and recomputes every quantitative claim in the fleet’s retraction notice with explicit arithmetic and unit‑dimension checks. Using the count‑derived mitigation factor $\\alpha = 1{,}889/(1{,}889+1{,}264) \\approx 0.5991$, the model yields an expected $E[F]=1.732$ unresolved worker‑script failures per month (standard deviation $\\approx1.284$), a corpus productivity of $9.65$ papers per active project, a high‑severity gap proportion of $11.9\\%$, and a baseline composite autonomy score of $4.26$ that rises to $4.66$ when the per‑worker failure probability is halved. The billing‑unit error inflates the perceived monthly spend to $\\$11{,}000$, representing a $73.3$‑fold violation of the $\\$150$ cap, a $122.2$‑fold violation of the $\\$90$ per‑30‑day gate, and a $22.2\\%$ exceedance of the gate after correcting the spend to $\\$110$. These analytically derived levers enable platform operators, funders, and auditors to size monitoring, budget accurately, and enforce claim integrity at **TRL 2**.",
  "date": "2026-09-27",
  "first_published": "2026-09-27",
  "last_revised": "2026-10-21T05:21:23Z",
  "modified": "2026-10-21T05:21:23Z",
  "version": "1.9.0",
  "version_count": 9,
  "language": "en",
  "keywords": [
    "autonomous systems",
    "cloud infrastructure",
    "self-healing",
    "research infrastructure",
    "operational lessons"
  ],
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    "High-quality scholarly research",
    "open-access scholarly work",
    "versioned research publication"
  ],
  "quality_label": "High-quality scholarly research",
  "quality_basis": "QNFO publisher classification for open-access, versioned research with a public revision history and reader correction channel; this is not a claim of independent peer review.",
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  "peer_review_status": "Not independently peer-reviewed",
  "resource_type": "ScholarlyArticle",
  "doi": null,
  "doi_url": null,
  "url": "https://papers.qnfo.org/papers/quniverse-fleet-lessons",
  "license_url": "https://legal.qnfo.org/",
  "license_spdx": null,
  "sha256": "c9096c6e01d9fd6d7b07b118c86038bcbcce2da5caa1007c040a9913f670f744",
  "version_url": "https://papers.qnfo.org/papers/quniverse-fleet-lessons/v/1.9.0",
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    "identifiers": [
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        "identifier": "https://papers.qnfo.org/papers/quniverse-fleet-lessons",
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    ],
    "creators": [
      {
        "name": "Rowan Brad Quni-Gudzinas",
        "nameType": "Personal",
        "nameIdentifiers": [
          {
            "nameIdentifier": "https://orcid.org/0009-0002-4317-5604",
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    ],
    "titles": [
      {
        "title": "Quantitative Integrity and Reliability Modeling of the Quniverse Autonomous Research Fleet"
      }
    ],
    "publisher": "QNFO",
    "publicationYear": "2026",
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    "subjects": [
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        "subject": "autonomous systems"
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        "subject": "cloud infrastructure"
      },
      {
        "subject": "self-healing"
      },
      {
        "subject": "research infrastructure"
      },
      {
        "subject": "operational lessons"
      },
      {
        "subject": "High-quality scholarly research",
        "subjectScheme": "QNFO publisher classification",
        "schemeUri": "https://qnfo.org/about"
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        "subject": "open-access scholarly work",
        "subjectScheme": "QNFO publisher classification",
        "schemeUri": "https://qnfo.org/about"
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        "subject": "versioned research publication",
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    "dates": [
      {
        "date": "2026-09-27",
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        "date": "2026-10-21",
        "dateType": "Updated"
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        "description": "Autonomous research fleets must keep serverless worker scripts, databases, and storage buckets operational while publishing cost and performance metrics that external stakeholders can trust. Existing studies treat fleet sizing, failure propagation, and claim validation separately, leaving operators without a unified decision framework. This work fills the gap by presenting an integrated analytical model that jointly captures reliability, monetary impact, and claim‑integrity auditing for the Quniverse fleet. The model combines a Bernoulli failure‑propagation formulation with a mitigation factor derived from the fleet’s own self‑heal and handoff counts, aggregates resource consumption, computes a weighted composite autonomy score, and recomputes every quantitative claim in the fleet’s retraction notice with explicit arithmetic and unit‑dimension checks. Using the count‑derived mitigation factor α = 1,889/(1,889+1,264) ≈ 0.5991, the model yields an expected E[F]=1.732 unresolved worker‑script failures per month (standard deviation ≈1.284), a corpus productivity of $9.65$ papers per active project, a high‑severity gap proportion of 11.9\\%, and a baseline composite autonomy score of $4.26$ that rises to $4.66$ when the per‑worker failure probability is halved. The billing‑unit error inflates the perceived monthly spend to \\$11,000, representing a $73.3$‑fold violation of the \\$150 cap, a $122.2$‑fold violation of the \\$90 per‑30‑day gate, and a 22.2\\% exceedance of the gate after correcting the spend to \\$110. These analytically derived levers enable platform operators, funders, and auditors to size monitoring, budget accurately, and enforce claim integrity at TRL 2.",
        "descriptionType": "Abstract"
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        "description": "Publisher quality classification: High-quality scholarly research. QNFO publisher classification for open-access, versioned research with a public revision history and reader correction channel; this is not a claim of independent peer review.",
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