{
  "id": "oai:papers.qnfo.org:cross-model-llm-convergence-as-a-bayesian-gate-check-for-fringe-physics-claims",
  "slug": "cross-model-llm-convergence-as-a-bayesian-gate-check-for-fringe-physics-claims",
  "title": "Cross-Model LLM Convergence as a Bayesian Gate-Check for Fringe Physics Claims",
  "authors": [
    "Rowan Brad Quni-Gudzinas"
  ],
  "abstract": "The verification of fringe scientific claims is bottlenecked by a shortage of domain experts and the high cost of exhaustive peer review. Existing benchmarks assess single large-language-model (LLM) performance against ground truth but ignore the evidential weight of inter-model agreement. We formalize cross-model convergence—two or more conditionally independent LLMs producing the same multi-step argument on a claim—as a Bayesian evidence signal. Using illustrative assumptions drawn from a four-arm benchmark (retrieval, synthesis, cascade, trap), we derive closed-form expressions for posterior odds and probabilities. The analysis yields a Bayes factor of K_S = 8 for two-model synthesis agreement, a posterior of P₂ = 8/17 ≈ 0.471 from a $0.1$ prior, and a cascade posterior P₄ ≈ 0.983 after four agreeing models. A trap-arm Bayes factor K_T ≈ 0.0111 drives the posterior to P_T ≈ 1.2 × 10^-3, flagging unreliable convergence. Sensitivity analysis shows the posterior varies from $0.182$ to $0.690$ as the null-model agreement probability p_b^S ranges from $0.20$ to $0.02$. These results demonstrate that cross-model convergence can serve as a quantitative gate-check, enabling funding agencies and journal editors to prioritize expert review for claims that survive the convergence test while discarding those that do not. The framework is analytically tractable, requires no empirical data beyond benchmark-derived probabilities, and is positioned at Technology Readiness Level 1–2.",
  "abstract_markdown": "The verification of fringe scientific claims is bottlenecked by a shortage of domain experts and the high cost of exhaustive peer review. Existing benchmarks assess single large-language-model (LLM) performance against ground truth but ignore the evidential weight of inter-model agreement. We formalize cross-model convergence—two or more conditionally independent LLMs producing the same multi-step argument on a claim—as a Bayesian evidence signal. Using illustrative assumptions drawn from a four-arm benchmark (retrieval, synthesis, cascade, trap), we derive closed-form expressions for posterior odds and probabilities. The analysis yields a Bayes factor of $K_S = 8$ for two-model synthesis agreement, a posterior of $P_2 = 8/17 \\approx 0.471$ from a $0.1$ prior, and a cascade posterior $P_4 \\approx 0.983$ after four agreeing models. A trap-arm Bayes factor $K_T \\approx 0.0111$ drives the posterior to $P_T \\approx 1.2 \\times 10^{-3}$, flagging unreliable convergence. Sensitivity analysis shows the posterior varies from $0.182$ to $0.690$ as the null-model agreement probability $p_b^S$ ranges from $0.20$ to $0.02$. These results demonstrate that cross-model convergence can serve as a quantitative gate-check, enabling funding agencies and journal editors to prioritize expert review for claims that survive the convergence test while discarding those that do not. The framework is analytically tractable, requires no empirical data beyond benchmark-derived probabilities, and is positioned at Technology Readiness Level 1–2.",
  "date": "2026-11-10",
  "first_published": "2026-11-10",
  "last_revised": "2026-11-10T00:00:00Z",
  "modified": "2026-11-10T00:00:00Z",
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  "doi": null,
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  "url": "https://papers.qnfo.org/papers/cross-model-llm-convergence-as-a-bayesian-gate-check-for-fringe-physics-claims",
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  "version_url": "https://papers.qnfo.org/papers/cross-model-llm-convergence-as-a-bayesian-gate-check-for-fringe-physics-claims/v/1.0.0",
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    "creators": [
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        "name": "Rowan Brad Quni-Gudzinas",
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    "titles": [
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        "title": "Cross-Model LLM Convergence as a Bayesian Gate-Check for Fringe Physics Claims"
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    "publicationYear": "2026",
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        "description": "The verification of fringe scientific claims is bottlenecked by a shortage of domain experts and the high cost of exhaustive peer review. Existing benchmarks assess single large-language-model (LLM) performance against ground truth but ignore the evidential weight of inter-model agreement. We formalize cross-model convergence—two or more conditionally independent LLMs producing the same multi-step argument on a claim—as a Bayesian evidence signal. Using illustrative assumptions drawn from a four-arm benchmark (retrieval, synthesis, cascade, trap), we derive closed-form expressions for posterior odds and probabilities. The analysis yields a Bayes factor of K_S = 8 for two-model synthesis agreement, a posterior of P₂ = 8/17 ≈ 0.471 from a $0.1$ prior, and a cascade posterior P₄ ≈ 0.983 after four agreeing models. A trap-arm Bayes factor K_T ≈ 0.0111 drives the posterior to P_T ≈ 1.2 × 10^-3, flagging unreliable convergence. Sensitivity analysis shows the posterior varies from $0.182$ to $0.690$ as the null-model agreement probability p_b^S ranges from $0.20$ to $0.02$. These results demonstrate that cross-model convergence can serve as a quantitative gate-check, enabling funding agencies and journal editors to prioritize expert review for claims that survive the convergence test while discarding those that do not. The framework is analytically tractable, requires no empirical data beyond benchmark-derived probabilities, and is positioned at Technology Readiness Level 1–2.",
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