{
  "comparison_started_after_freeze_at_utc": "2026-10-28T02:41:01.062953+00:00",
  "completed_at_utc": "2026-10-28T02:41:01.090818+00:00",
  "independent_score_freeze_sha256": "b600c8a41bcc23bbc7b814a9b9c48e8d83232d6db8c4befa513600fbddf55166",
  "frozen_score_hashes_verified": true,
  "method": "One judge compares shared reader questions after independent scoring, then reconsiders each pair beginning with the other submission. Titles need not match. Reverse passes use the same evidence and retained memory; this is an internal presentation-order diagnostic, not independent replication. Qualitative question preferences are not added as extra scores.",
  "direction_count": 12,
  "directions": [
    {
      "direction": "A\u2192B",
      "pass": "initial_comparison",
      "presentation_order": [
        "A",
        "B"
      ],
      "overall_judgement": "A",
      "rationale": "A is more reliable and easier to edit as a whole. B\u2019s statistical depth and research breadth are real strengths, but its cost guidance and categorical methods create a larger repair burden.",
      "overlapping_reader_questions": [
        {
          "reader_question_area": "decision framework and model/system boundary",
          "first_article": "A/articles/01-benchmarking-models.md",
          "second_article": "B/articles/evaluating-ai-models-and-agent-systems.md",
          "judgement": "A",
          "reason": "A offers a clearer decision journey; B adds a dense but source-rich opening and stronger statistical illustration.",
          "changed_from_forward_pass": null
        },
        {
          "reader_question_area": "public versus workload evidence",
          "first_article": "A/articles/02-public-benchmarks.md",
          "second_article": "B/articles/public-benchmarks-vs-workload-evaluation.md",
          "judgement": "tie",
          "reason": "B has broader verified historical benchmark research; A is better on vendor interrogation and denominator checks.",
          "changed_from_forward_pass": null
        },
        {
          "reader_question_area": "task and dataset design",
          "first_article": "A/articles/03-task-design.md",
          "second_article": "B/articles/designing-evaluation-tasks-and-datasets.md",
          "judgement": "tie",
          "reason": "B supplies a reusable YAML record; A offers more careful treatment of ambiguous outcomes and negative cases. B needs categorical sample/synthetic rules softened.",
          "changed_from_forward_pass": null
        },
        {
          "reader_question_area": "holdouts, contamination and leakage",
          "first_article": "A/articles/04-datasets-holdouts.md",
          "second_article": "B/articles/contamination-leakage-and-holdouts.md",
          "judgement": "A",
          "reason": "A gives a careful exposure-response procedure. B has a richer literature survey but mislabels the initial LiveCodeBench window and treats uncertain decontamination as an upper bound.",
          "changed_from_forward_pass": null
        },
        {
          "reader_question_area": "objective metrics and rubrics",
          "first_article": "A/articles/05-metrics-rubrics.md",
          "second_article": "B/articles/objective-metrics-and-rubrics.md",
          "judgement": "A",
          "reason": "A separates factual support, completeness and gates cleanly. B adds pass@k but needs token-F1 and traffic-weighting qualifications.",
          "changed_from_forward_pass": null
        },
        {
          "reader_question_area": "human review and model judges",
          "first_article": "A/articles/06-human-model-judges.md",
          "second_article": "B/articles/human-evaluation-and-model-judges.md",
          "judgement": "A",
          "reason": "A explicitly separates calibration and validation and audits passes. B usefully adds kappa but overgeneralises some mitigations.",
          "changed_from_forward_pass": null
        },
        {
          "reader_question_area": "repeatability, variation and uncertainty",
          "first_article": "A/articles/07-repeatability-uncertainty.md",
          "second_article": "B/articles/repeatability-variance-and-uncertainty.md",
          "judgement": "B",
          "reason": "B provides equations, a paired numerical example, power planning and a reporting table. A offers sound conceptual guidance without calculating an interval.",
          "changed_from_forward_pass": null
        },
        {
          "reader_question_area": "prompt, tool and configuration fairness",
          "first_article": "A/articles/08-fair-comparisons.md",
          "second_article": "B/articles/prompt-tool-and-configuration-fairness.md",
          "judgement": "A",
          "reason": "A distinguishes controlled substitution from deployable systems without treating nominal effort labels as equivalent.",
          "changed_from_forward_pass": null
        },
        {
          "reader_question_area": "latency, usage and cost",
          "first_article": "A/articles/09-latency-cost.md",
          "second_article": "B/articles/latency-token-usage-and-cost.md",
          "judgement": "A",
          "reason": "A has correct labelled arithmetic. B has richer unit economics, but its visible-output formula and omitted price tier must be fixed.",
          "changed_from_forward_pass": null
        },
        {
          "reader_question_area": "agent/tool-use evaluation",
          "first_article": "A/articles/10-agent-evaluation.md",
          "second_article": "B/articles/evaluating-agents-and-tool-use.md",
          "judgement": "A",
          "reason": "A offers specific read-error and partial-write failure injections. B provides more public benchmark context and pass^k explanation.",
          "changed_from_forward_pass": null
        },
        {
          "reader_question_area": "interpretation and maintenance",
          "first_article": "A/articles/11-results-maintenance.md",
          "second_article": "B/articles/interpreting-results-and-maintaining-evals.md",
          "judgement": "tie",
          "reason": "A is strong on version migration and historical baseline limits; B adds a usable memo and verified deprecation example.",
          "changed_from_forward_pass": null
        }
      ],
      "reversal_changes_judgement": null,
      "score_changes_made": false
    },
    {
      "direction": "B\u2192A",
      "pass": "reversed_presentation_reconsideration",
      "presentation_order": [
        "B",
        "A"
      ],
      "overall_judgement": "A",
      "rationale": "Starting with B highlights its YAML schema, source breadth and power calculations. Returning to A still favours A\u2019s dependable guidance and lower repair burden; no change.",
      "overlapping_reader_questions": [
        {
          "reader_question_area": "decision framework and model/system boundary",
          "first_article": "B/articles/evaluating-ai-models-and-agent-systems.md",
          "second_article": "A/articles/01-benchmarking-models.md",
          "judgement": "A",
          "reason": "A offers a clearer decision journey; B adds a dense but source-rich opening and stronger statistical illustration.",
          "changed_from_forward_pass": false
        },
        {
          "reader_question_area": "public versus workload evidence",
          "first_article": "B/articles/public-benchmarks-vs-workload-evaluation.md",
          "second_article": "A/articles/02-public-benchmarks.md",
          "judgement": "tie",
          "reason": "B has broader verified historical benchmark research; A is better on vendor interrogation and denominator checks.",
          "changed_from_forward_pass": false
        },
        {
          "reader_question_area": "task and dataset design",
          "first_article": "B/articles/designing-evaluation-tasks-and-datasets.md",
          "second_article": "A/articles/03-task-design.md",
          "judgement": "tie",
          "reason": "B supplies a reusable YAML record; A offers more careful treatment of ambiguous outcomes and negative cases. B needs categorical sample/synthetic rules softened.",
          "changed_from_forward_pass": false
        },
        {
          "reader_question_area": "holdouts, contamination and leakage",
          "first_article": "B/articles/contamination-leakage-and-holdouts.md",
          "second_article": "A/articles/04-datasets-holdouts.md",
          "judgement": "A",
          "reason": "A gives a careful exposure-response procedure. B has a richer literature survey but mislabels the initial LiveCodeBench window and treats uncertain decontamination as an upper bound.",
          "changed_from_forward_pass": false
        },
        {
          "reader_question_area": "objective metrics and rubrics",
          "first_article": "B/articles/objective-metrics-and-rubrics.md",
          "second_article": "A/articles/05-metrics-rubrics.md",
          "judgement": "A",
          "reason": "A separates factual support, completeness and gates cleanly. B adds pass@k but needs token-F1 and traffic-weighting qualifications.",
          "changed_from_forward_pass": false
        },
        {
          "reader_question_area": "human review and model judges",
          "first_article": "B/articles/human-evaluation-and-model-judges.md",
          "second_article": "A/articles/06-human-model-judges.md",
          "judgement": "A",
          "reason": "A explicitly separates calibration and validation and audits passes. B usefully adds kappa but overgeneralises some mitigations.",
          "changed_from_forward_pass": false
        },
        {
          "reader_question_area": "repeatability, variation and uncertainty",
          "first_article": "B/articles/repeatability-variance-and-uncertainty.md",
          "second_article": "A/articles/07-repeatability-uncertainty.md",
          "judgement": "B",
          "reason": "B provides equations, a paired numerical example, power planning and a reporting table. A offers sound conceptual guidance without calculating an interval.",
          "changed_from_forward_pass": false
        },
        {
          "reader_question_area": "prompt, tool and configuration fairness",
          "first_article": "B/articles/prompt-tool-and-configuration-fairness.md",
          "second_article": "A/articles/08-fair-comparisons.md",
          "judgement": "A",
          "reason": "A distinguishes controlled substitution from deployable systems without treating nominal effort labels as equivalent.",
          "changed_from_forward_pass": false
        },
        {
          "reader_question_area": "latency, usage and cost",
          "first_article": "B/articles/latency-token-usage-and-cost.md",
          "second_article": "A/articles/09-latency-cost.md",
          "judgement": "A",
          "reason": "A has correct labelled arithmetic. B has richer unit economics, but its visible-output formula and omitted price tier must be fixed.",
          "changed_from_forward_pass": false
        },
        {
          "reader_question_area": "agent/tool-use evaluation",
          "first_article": "B/articles/evaluating-agents-and-tool-use.md",
          "second_article": "A/articles/10-agent-evaluation.md",
          "judgement": "A",
          "reason": "A offers specific read-error and partial-write failure injections. B provides more public benchmark context and pass^k explanation.",
          "changed_from_forward_pass": false
        },
        {
          "reader_question_area": "interpretation and maintenance",
          "first_article": "B/articles/interpreting-results-and-maintaining-evals.md",
          "second_article": "A/articles/11-results-maintenance.md",
          "judgement": "tie",
          "reason": "A is strong on version migration and historical baseline limits; B adds a usable memo and verified deprecation example.",
          "changed_from_forward_pass": false
        }
      ],
      "reversal_changes_judgement": false,
      "score_changes_made": false
    },
    {
      "direction": "A\u2192C",
      "pass": "initial_comparison",
      "presentation_order": [
        "A",
        "C"
      ],
      "overall_judgement": "C",
      "rationale": "C has the stronger combination of checked worked examples, scoped sources and operational edge cases. A remains a coherent, sound cluster.",
      "overlapping_reader_questions": [
        {
          "reader_question_area": "decision framework and model/system boundary",
          "first_article": "A/articles/01-benchmarking-models.md",
          "second_article": "C/articles/ai-model-benchmarking-guide.md",
          "judgement": "C",
          "reason": "C closes with six concrete artefacts and review triggers; A has a useful first-week sequence. C handles evidence boundaries more consistently.",
          "changed_from_forward_pass": null
        },
        {
          "reader_question_area": "public versus workload evidence",
          "first_article": "A/articles/02-public-benchmarks.md",
          "second_article": "C/articles/read-ai-benchmark-leaderboards.md",
          "judgement": "tie",
          "reason": "A has stronger procurement questions and maintainer error categories; C gives a disciplined evidence-card method and interval cautions. Both serve the reading question well.",
          "changed_from_forward_pass": null
        },
        {
          "reader_question_area": "task and dataset design",
          "first_article": "A/articles/03-task-design.md",
          "second_article": "C/articles/design-llm-evaluation-tasks.md",
          "judgement": "C",
          "reason": "C defines an observation point and contrastive cases around missing information. A provides a useful traceable case review; C is more explicit about permitted behavioural alternatives.",
          "changed_from_forward_pass": null
        },
        {
          "reader_question_area": "holdouts, contamination and leakage",
          "first_article": "A/articles/04-datasets-holdouts.md",
          "second_article": "C/articles/evaluation-dataset-leakage-holdouts.md",
          "judgement": "C",
          "reason": "C combines provenance, overlap dispositions and correctly scoped contamination methods. A has a useful exposure response; C better connects data roles to analysis.",
          "changed_from_forward_pass": null
        },
        {
          "reader_question_area": "objective metrics and rubrics",
          "first_article": "A/articles/05-metrics-rubrics.md",
          "second_article": "C/articles/llm-evaluation-metrics-rubrics.md",
          "judgement": "C",
          "reason": "C supplies a checked confusion matrix and anchored scoring example. A has good qualitative boundary judgements but no equivalent numeric walkthrough.",
          "changed_from_forward_pass": null
        },
        {
          "reader_question_area": "human review and model judges",
          "first_article": "A/articles/06-human-model-judges.md",
          "second_article": "C/articles/human-reviewers-llm-judges.md",
          "judgement": "C",
          "reason": "C explicitly routes invalid, ambiguous and order-sensitive verdicts and explains an always-pass judge. A is careful but less worked.",
          "changed_from_forward_pass": null
        },
        {
          "reader_question_area": "repeatability, variation and uncertainty",
          "first_article": "A/articles/07-repeatability-uncertainty.md",
          "second_article": "C/articles/benchmark-uncertainty-repeatability.md",
          "judgement": "C",
          "reason": "C calculates a Wilson interval and gives paired/group resampling steps. A explains the principles accurately but stops before numerical inference.",
          "changed_from_forward_pass": null
        },
        {
          "reader_question_area": "prompt, tool and configuration fairness",
          "first_article": "A/articles/08-fair-comparisons.md",
          "second_article": "C/articles/fair-ai-model-comparisons.md",
          "judgement": "C",
          "reason": "C addresses non-equivalent settings, internal-compute limits and sensitivity checks in operational detail.",
          "changed_from_forward_pass": null
        },
        {
          "reader_question_area": "latency, usage and cost",
          "first_article": "A/articles/09-latency-cost.md",
          "second_article": "C/articles/ai-latency-cost-per-successful-task.md",
          "judgement": "C",
          "reason": "C includes cache eligibility, zero successes, double-counting and invoice reconciliation. A is sound but narrower.",
          "changed_from_forward_pass": null
        },
        {
          "reader_question_area": "agent/tool-use evaluation",
          "first_article": "A/articles/10-agent-evaluation.md",
          "second_article": "C/articles/evaluate-ai-agents-tools-outcomes.md",
          "judgement": "C",
          "reason": "C makes lost write acknowledgements, state reconciliation and exactly-one-record checks explicit. A partial-write scenarios are useful but less specified.",
          "changed_from_forward_pass": null
        },
        {
          "reader_question_area": "interpretation and maintenance",
          "first_article": "A/articles/11-results-maintenance.md",
          "second_article": "C/articles/maintain-llm-evaluation-suite.md",
          "judgement": "C",
          "reason": "C triages changes to input, output and score with a concrete policy/date example. A is a sound version-and-owner framework.",
          "changed_from_forward_pass": null
        }
      ],
      "reversal_changes_judgement": null,
      "score_changes_made": false
    },
    {
      "direction": "C\u2192A",
      "pass": "reversed_presentation_reconsideration",
      "presentation_order": [
        "C",
        "A"
      ],
      "overall_judgement": "C",
      "rationale": "Starting with C foregrounds its concrete state and interval examples. A\u2019s vendor questions and first-week plan do not reverse the preference; no change.",
      "overlapping_reader_questions": [
        {
          "reader_question_area": "decision framework and model/system boundary",
          "first_article": "C/articles/ai-model-benchmarking-guide.md",
          "second_article": "A/articles/01-benchmarking-models.md",
          "judgement": "C",
          "reason": "C closes with six concrete artefacts and review triggers; A has a useful first-week sequence. C handles evidence boundaries more consistently.",
          "changed_from_forward_pass": false
        },
        {
          "reader_question_area": "public versus workload evidence",
          "first_article": "C/articles/read-ai-benchmark-leaderboards.md",
          "second_article": "A/articles/02-public-benchmarks.md",
          "judgement": "tie",
          "reason": "A has stronger procurement questions and maintainer error categories; C gives a disciplined evidence-card method and interval cautions. Both serve the reading question well.",
          "changed_from_forward_pass": false
        },
        {
          "reader_question_area": "task and dataset design",
          "first_article": "C/articles/design-llm-evaluation-tasks.md",
          "second_article": "A/articles/03-task-design.md",
          "judgement": "C",
          "reason": "C defines an observation point and contrastive cases around missing information. A provides a useful traceable case review; C is more explicit about permitted behavioural alternatives.",
          "changed_from_forward_pass": false
        },
        {
          "reader_question_area": "holdouts, contamination and leakage",
          "first_article": "C/articles/evaluation-dataset-leakage-holdouts.md",
          "second_article": "A/articles/04-datasets-holdouts.md",
          "judgement": "C",
          "reason": "C combines provenance, overlap dispositions and correctly scoped contamination methods. A has a useful exposure response; C better connects data roles to analysis.",
          "changed_from_forward_pass": false
        },
        {
          "reader_question_area": "objective metrics and rubrics",
          "first_article": "C/articles/llm-evaluation-metrics-rubrics.md",
          "second_article": "A/articles/05-metrics-rubrics.md",
          "judgement": "C",
          "reason": "C supplies a checked confusion matrix and anchored scoring example. A has good qualitative boundary judgements but no equivalent numeric walkthrough.",
          "changed_from_forward_pass": false
        },
        {
          "reader_question_area": "human review and model judges",
          "first_article": "C/articles/human-reviewers-llm-judges.md",
          "second_article": "A/articles/06-human-model-judges.md",
          "judgement": "C",
          "reason": "C explicitly routes invalid, ambiguous and order-sensitive verdicts and explains an always-pass judge. A is careful but less worked.",
          "changed_from_forward_pass": false
        },
        {
          "reader_question_area": "repeatability, variation and uncertainty",
          "first_article": "C/articles/benchmark-uncertainty-repeatability.md",
          "second_article": "A/articles/07-repeatability-uncertainty.md",
          "judgement": "C",
          "reason": "C calculates a Wilson interval and gives paired/group resampling steps. A explains the principles accurately but stops before numerical inference.",
          "changed_from_forward_pass": false
        },
        {
          "reader_question_area": "prompt, tool and configuration fairness",
          "first_article": "C/articles/fair-ai-model-comparisons.md",
          "second_article": "A/articles/08-fair-comparisons.md",
          "judgement": "C",
          "reason": "C addresses non-equivalent settings, internal-compute limits and sensitivity checks in operational detail.",
          "changed_from_forward_pass": false
        },
        {
          "reader_question_area": "latency, usage and cost",
          "first_article": "C/articles/ai-latency-cost-per-successful-task.md",
          "second_article": "A/articles/09-latency-cost.md",
          "judgement": "C",
          "reason": "C includes cache eligibility, zero successes, double-counting and invoice reconciliation. A is sound but narrower.",
          "changed_from_forward_pass": false
        },
        {
          "reader_question_area": "agent/tool-use evaluation",
          "first_article": "C/articles/evaluate-ai-agents-tools-outcomes.md",
          "second_article": "A/articles/10-agent-evaluation.md",
          "judgement": "C",
          "reason": "C makes lost write acknowledgements, state reconciliation and exactly-one-record checks explicit. A partial-write scenarios are useful but less specified.",
          "changed_from_forward_pass": false
        },
        {
          "reader_question_area": "interpretation and maintenance",
          "first_article": "C/articles/maintain-llm-evaluation-suite.md",
          "second_article": "A/articles/11-results-maintenance.md",
          "judgement": "C",
          "reason": "C triages changes to input, output and score with a concrete policy/date example. A is a sound version-and-owner framework.",
          "changed_from_forward_pass": false
        }
      ],
      "reversal_changes_judgement": false,
      "score_changes_made": false
    },
    {
      "direction": "A\u2192D",
      "pass": "initial_comparison",
      "presentation_order": [
        "A",
        "D"
      ],
      "overall_judgement": "tie",
      "rationale": "A has a narrow numerical lead, but D\u2019s metric and labour-cost examples offset some of A\u2019s strengths in judges, uncertainty and recovery. Treat this as an editorial near-tie.",
      "overlapping_reader_questions": [
        {
          "reader_question_area": "decision framework and model/system boundary",
          "first_article": "A/articles/01-benchmarking-models.md",
          "second_article": "D/articles/choosing-model-benchmark.md",
          "judgement": "tie",
          "reason": "Both give an accessible decision-first framework. A maintains one example; D uses a more operational invoice decision rule.",
          "changed_from_forward_pass": null
        },
        {
          "reader_question_area": "public versus workload evidence",
          "first_article": "A/articles/02-public-benchmarks.md",
          "second_article": "D/articles/public-benchmark-literacy.md",
          "judgement": "A",
          "reason": "A avoids D\u2019s wrong position-paper author label and adds specific vendor questions. D has a clear shortlist checklist.",
          "changed_from_forward_pass": null
        },
        {
          "reader_question_area": "task and dataset design",
          "first_article": "A/articles/03-task-design.md",
          "second_article": "D/articles/build-task-eval.md",
          "judgement": "tie",
          "reason": "D has a fuller illustrative ticket dataset and pilot repair; A offers detailed case provenance and acceptable alternatives.",
          "changed_from_forward_pass": null
        },
        {
          "reader_question_area": "holdouts, contamination and leakage",
          "first_article": "A/articles/04-datasets-holdouts.md",
          "second_article": "D/articles/datasets-holdouts-contamination.md",
          "judgement": "tie",
          "reason": "A gives an exposure-response procedure; D adds freshness/label-quality tradeoffs and a cautiously scoped recent study. Neither dominates the whole question.",
          "changed_from_forward_pass": null
        },
        {
          "reader_question_area": "objective metrics and rubrics",
          "first_article": "A/articles/05-metrics-rubrics.md",
          "second_article": "D/articles/metrics-rubrics.md",
          "judgement": "D",
          "reason": "D adds selective coverage, ordinal-scale caution and useful metric hierarchy. A\u2019s qualitative rubric is clear; D covers more failure modes here.",
          "changed_from_forward_pass": null
        },
        {
          "reader_question_area": "human review and model judges",
          "first_article": "A/articles/06-human-model-judges.md",
          "second_article": "D/articles/human-evaluation-model-judges.md",
          "judgement": "A",
          "reason": "A is clearer about fresh validation and false-pass audits. D offers a readable hybrid workflow but has a bibliographic author repair.",
          "changed_from_forward_pass": null
        },
        {
          "reader_question_area": "repeatability, variation and uncertainty",
          "first_article": "A/articles/07-repeatability-uncertainty.md",
          "second_article": "D/articles/repeatability-uncertainty.md",
          "judgement": "A",
          "reason": "A explains paired/group bootstrap steps and correct frequentist interpretation. D offers a useful protocol but no worked interval or applied calculation.",
          "changed_from_forward_pass": null
        },
        {
          "reader_question_area": "prompt, tool and configuration fairness",
          "first_article": "A/articles/08-fair-comparisons.md",
          "second_article": "D/articles/fair-model-comparisons.md",
          "judgement": "tie",
          "reason": "Both distinguish controlled and best-configuration experiments and document asymmetric interfaces. Their practical omissions are similar.",
          "changed_from_forward_pass": null
        },
        {
          "reader_question_area": "latency, usage and cost",
          "first_article": "A/articles/09-latency-cost.md",
          "second_article": "D/articles/latency-cost.md",
          "judgement": "D",
          "reason": "D\u2019s human-review worksheet makes labour costs tangible; A\u2019s token/per-success example is cleaner but narrower. D should disambiguate retry bookkeeping.",
          "changed_from_forward_pass": null
        },
        {
          "reader_question_area": "agent/tool-use evaluation",
          "first_article": "A/articles/10-agent-evaluation.md",
          "second_article": "D/articles/agent-system-evaluation.md",
          "judgement": "A",
          "reason": "A\u2019s read failure and partial-write cases are more directly reusable than D\u2019s broader coding environment example.",
          "changed_from_forward_pass": null
        },
        {
          "reader_question_area": "interpretation and maintenance",
          "first_article": "A/articles/11-results-maintenance.md",
          "second_article": "D/articles/maintaining-evaluations.md",
          "judgement": "tie",
          "reason": "D supplies an illustrative layered cadence; A details migration and unavailable historical baselines. Both preserve versions and decision owners.",
          "changed_from_forward_pass": null
        }
      ],
      "reversal_changes_judgement": null,
      "score_changes_made": false
    },
    {
      "direction": "D\u2192A",
      "pass": "reversed_presentation_reconsideration",
      "presentation_order": [
        "D",
        "A"
      ],
      "overall_judgement": "tie",
      "rationale": "Starting with D highlights its labour worksheet, ordinal-scale caveats and broader examples. A remains stronger on judge validation and recovery, so the near-tie persists; no change.",
      "overlapping_reader_questions": [
        {
          "reader_question_area": "decision framework and model/system boundary",
          "first_article": "D/articles/choosing-model-benchmark.md",
          "second_article": "A/articles/01-benchmarking-models.md",
          "judgement": "tie",
          "reason": "Both give an accessible decision-first framework. A maintains one example; D uses a more operational invoice decision rule.",
          "changed_from_forward_pass": false
        },
        {
          "reader_question_area": "public versus workload evidence",
          "first_article": "D/articles/public-benchmark-literacy.md",
          "second_article": "A/articles/02-public-benchmarks.md",
          "judgement": "A",
          "reason": "A avoids D\u2019s wrong position-paper author label and adds specific vendor questions. D has a clear shortlist checklist.",
          "changed_from_forward_pass": false
        },
        {
          "reader_question_area": "task and dataset design",
          "first_article": "D/articles/build-task-eval.md",
          "second_article": "A/articles/03-task-design.md",
          "judgement": "tie",
          "reason": "D has a fuller illustrative ticket dataset and pilot repair; A offers detailed case provenance and acceptable alternatives.",
          "changed_from_forward_pass": false
        },
        {
          "reader_question_area": "holdouts, contamination and leakage",
          "first_article": "D/articles/datasets-holdouts-contamination.md",
          "second_article": "A/articles/04-datasets-holdouts.md",
          "judgement": "tie",
          "reason": "A gives an exposure-response procedure; D adds freshness/label-quality tradeoffs and a cautiously scoped recent study. Neither dominates the whole question.",
          "changed_from_forward_pass": false
        },
        {
          "reader_question_area": "objective metrics and rubrics",
          "first_article": "D/articles/metrics-rubrics.md",
          "second_article": "A/articles/05-metrics-rubrics.md",
          "judgement": "D",
          "reason": "D adds selective coverage, ordinal-scale caution and useful metric hierarchy. A\u2019s qualitative rubric is clear; D covers more failure modes here.",
          "changed_from_forward_pass": false
        },
        {
          "reader_question_area": "human review and model judges",
          "first_article": "D/articles/human-evaluation-model-judges.md",
          "second_article": "A/articles/06-human-model-judges.md",
          "judgement": "A",
          "reason": "A is clearer about fresh validation and false-pass audits. D offers a readable hybrid workflow but has a bibliographic author repair.",
          "changed_from_forward_pass": false
        },
        {
          "reader_question_area": "repeatability, variation and uncertainty",
          "first_article": "D/articles/repeatability-uncertainty.md",
          "second_article": "A/articles/07-repeatability-uncertainty.md",
          "judgement": "A",
          "reason": "A explains paired/group bootstrap steps and correct frequentist interpretation. D offers a useful protocol but no worked interval or applied calculation.",
          "changed_from_forward_pass": false
        },
        {
          "reader_question_area": "prompt, tool and configuration fairness",
          "first_article": "D/articles/fair-model-comparisons.md",
          "second_article": "A/articles/08-fair-comparisons.md",
          "judgement": "tie",
          "reason": "Both distinguish controlled and best-configuration experiments and document asymmetric interfaces. Their practical omissions are similar.",
          "changed_from_forward_pass": false
        },
        {
          "reader_question_area": "latency, usage and cost",
          "first_article": "D/articles/latency-cost.md",
          "second_article": "A/articles/09-latency-cost.md",
          "judgement": "D",
          "reason": "D\u2019s human-review worksheet makes labour costs tangible; A\u2019s token/per-success example is cleaner but narrower. D should disambiguate retry bookkeeping.",
          "changed_from_forward_pass": false
        },
        {
          "reader_question_area": "agent/tool-use evaluation",
          "first_article": "D/articles/agent-system-evaluation.md",
          "second_article": "A/articles/10-agent-evaluation.md",
          "judgement": "A",
          "reason": "A\u2019s read failure and partial-write cases are more directly reusable than D\u2019s broader coding environment example.",
          "changed_from_forward_pass": false
        },
        {
          "reader_question_area": "interpretation and maintenance",
          "first_article": "D/articles/maintaining-evaluations.md",
          "second_article": "A/articles/11-results-maintenance.md",
          "judgement": "tie",
          "reason": "D supplies an illustrative layered cadence; A details migration and unavailable historical baselines. Both preserve versions and decision owners.",
          "changed_from_forward_pass": false
        }
      ],
      "reversal_changes_judgement": false,
      "score_changes_made": false
    },
    {
      "direction": "B\u2192C",
      "pass": "initial_comparison",
      "presentation_order": [
        "B",
        "C"
      ],
      "overall_judgement": "C",
      "rationale": "C better combines operational usefulness with careful source boundaries and correct accounting. B is competitive in research context and analytic statistics, but needs substantive repairs.",
      "overlapping_reader_questions": [
        {
          "reader_question_area": "decision framework and model/system boundary",
          "first_article": "B/articles/evaluating-ai-models-and-agent-systems.md",
          "second_article": "C/articles/ai-model-benchmarking-guide.md",
          "judgement": "C",
          "reason": "C gives a clearer bounded decision framework; B is richer in historical results but requires more rhetorical qualification.",
          "changed_from_forward_pass": null
        },
        {
          "reader_question_area": "public versus workload evidence",
          "first_article": "B/articles/public-benchmarks-vs-workload-evaluation.md",
          "second_article": "C/articles/read-ai-benchmark-leaderboards.md",
          "judgement": "B",
          "reason": "B offers substantially broader, verified benchmark and leaderboard research for readers seeking context. C is the cleaner audit procedure; B\u2019s venue year is a repairable label error.",
          "changed_from_forward_pass": null
        },
        {
          "reader_question_area": "task and dataset design",
          "first_article": "B/articles/designing-evaluation-tasks-and-datasets.md",
          "second_article": "C/articles/design-llm-evaluation-tasks.md",
          "judgement": "C",
          "reason": "C is more careful about ambiguous behaviour, timing and sample-size dependence. B\u2019s copyable schema is useful but universal restrictions need correction.",
          "changed_from_forward_pass": null
        },
        {
          "reader_question_area": "holdouts, contamination and leakage",
          "first_article": "B/articles/contamination-leakage-and-holdouts.md",
          "second_article": "C/articles/evaluation-dataset-leakage-holdouts.md",
          "judgement": "C",
          "reason": "C\u2019s scoped contamination methods and holdout roles avoid B\u2019s version label and upper-bound problems.",
          "changed_from_forward_pass": null
        },
        {
          "reader_question_area": "objective metrics and rubrics",
          "first_article": "B/articles/objective-metrics-and-rubrics.md",
          "second_article": "C/articles/llm-evaluation-metrics-rubrics.md",
          "judgement": "C",
          "reason": "C pairs a checked numeric example with gates and explicit denominators. B\u2019s metric catalogue adds breadth but includes overbroad F1/traffic statements.",
          "changed_from_forward_pass": null
        },
        {
          "reader_question_area": "human review and model judges",
          "first_article": "B/articles/human-evaluation-and-model-judges.md",
          "second_article": "C/articles/human-reviewers-llm-judges.md",
          "judgement": "C",
          "reason": "C makes false passes, invalid verdicts, fresh validation and escalation operational. B\u2019s agreement statistics add value but do not compensate for weaker conditions on mitigations.",
          "changed_from_forward_pass": null
        },
        {
          "reader_question_area": "repeatability, variation and uncertainty",
          "first_article": "B/articles/repeatability-variance-and-uncertainty.md",
          "second_article": "C/articles/benchmark-uncertainty-repeatability.md",
          "judgement": "tie",
          "reason": "B provides more analytic standard-error and power detail; C provides a calculated Wilson interval and paired bootstrap with clearer small-sample limits. Different useful emphases.",
          "changed_from_forward_pass": null
        },
        {
          "reader_question_area": "prompt, tool and configuration fairness",
          "first_article": "B/articles/prompt-tool-and-configuration-fairness.md",
          "second_article": "C/articles/fair-ai-model-comparisons.md",
          "judgement": "C",
          "reason": "C correctly refuses to equate nominal settings with internal compute; B\u2019s manifest is useful after that repair.",
          "changed_from_forward_pass": null
        },
        {
          "reader_question_area": "latency, usage and cost",
          "first_article": "B/articles/latency-token-usage-and-cost.md",
          "second_article": "C/articles/ai-latency-cost-per-successful-task.md",
          "judgement": "C",
          "reason": "C has safer usage accounting, explicit cache conditions and zero-success handling. B\u2019s actual rates are mostly verified but budget guidance needs material repair.",
          "changed_from_forward_pass": null
        },
        {
          "reader_question_area": "agent/tool-use evaluation",
          "first_article": "B/articles/evaluating-agents-and-tool-use.md",
          "second_article": "C/articles/evaluate-ai-agents-tools-outcomes.md",
          "judgement": "C",
          "reason": "C provides a precise lost-acknowledgement recovery case and permission predicates. B\u2019s benchmark survey and reliability notation add context.",
          "changed_from_forward_pass": null
        },
        {
          "reader_question_area": "interpretation and maintenance",
          "first_article": "B/articles/interpreting-results-and-maintaining-evals.md",
          "second_article": "C/articles/maintain-llm-evaluation-suite.md",
          "judgement": "C",
          "reason": "C\u2019s change triage and incident-sampling limits are more operationally precise; B\u2019s memo and maturity model are useful.",
          "changed_from_forward_pass": null
        }
      ],
      "reversal_changes_judgement": null,
      "score_changes_made": false
    },
    {
      "direction": "C\u2192B",
      "pass": "reversed_presentation_reconsideration",
      "presentation_order": [
        "C",
        "B"
      ],
      "overall_judgement": "C",
      "rationale": "Starting with C does not erase B\u2019s useful public research or analytic statistics. C remains the more dependable complete handoff; no change.",
      "overlapping_reader_questions": [
        {
          "reader_question_area": "decision framework and model/system boundary",
          "first_article": "C/articles/ai-model-benchmarking-guide.md",
          "second_article": "B/articles/evaluating-ai-models-and-agent-systems.md",
          "judgement": "C",
          "reason": "C gives a clearer bounded decision framework; B is richer in historical results but requires more rhetorical qualification.",
          "changed_from_forward_pass": false
        },
        {
          "reader_question_area": "public versus workload evidence",
          "first_article": "C/articles/read-ai-benchmark-leaderboards.md",
          "second_article": "B/articles/public-benchmarks-vs-workload-evaluation.md",
          "judgement": "B",
          "reason": "B offers substantially broader, verified benchmark and leaderboard research for readers seeking context. C is the cleaner audit procedure; B\u2019s venue year is a repairable label error.",
          "changed_from_forward_pass": false
        },
        {
          "reader_question_area": "task and dataset design",
          "first_article": "C/articles/design-llm-evaluation-tasks.md",
          "second_article": "B/articles/designing-evaluation-tasks-and-datasets.md",
          "judgement": "C",
          "reason": "C is more careful about ambiguous behaviour, timing and sample-size dependence. B\u2019s copyable schema is useful but universal restrictions need correction.",
          "changed_from_forward_pass": false
        },
        {
          "reader_question_area": "holdouts, contamination and leakage",
          "first_article": "C/articles/evaluation-dataset-leakage-holdouts.md",
          "second_article": "B/articles/contamination-leakage-and-holdouts.md",
          "judgement": "C",
          "reason": "C\u2019s scoped contamination methods and holdout roles avoid B\u2019s version label and upper-bound problems.",
          "changed_from_forward_pass": false
        },
        {
          "reader_question_area": "objective metrics and rubrics",
          "first_article": "C/articles/llm-evaluation-metrics-rubrics.md",
          "second_article": "B/articles/objective-metrics-and-rubrics.md",
          "judgement": "C",
          "reason": "C pairs a checked numeric example with gates and explicit denominators. B\u2019s metric catalogue adds breadth but includes overbroad F1/traffic statements.",
          "changed_from_forward_pass": false
        },
        {
          "reader_question_area": "human review and model judges",
          "first_article": "C/articles/human-reviewers-llm-judges.md",
          "second_article": "B/articles/human-evaluation-and-model-judges.md",
          "judgement": "C",
          "reason": "C makes false passes, invalid verdicts, fresh validation and escalation operational. B\u2019s agreement statistics add value but do not compensate for weaker conditions on mitigations.",
          "changed_from_forward_pass": false
        },
        {
          "reader_question_area": "repeatability, variation and uncertainty",
          "first_article": "C/articles/benchmark-uncertainty-repeatability.md",
          "second_article": "B/articles/repeatability-variance-and-uncertainty.md",
          "judgement": "tie",
          "reason": "B provides more analytic standard-error and power detail; C provides a calculated Wilson interval and paired bootstrap with clearer small-sample limits. Different useful emphases.",
          "changed_from_forward_pass": false
        },
        {
          "reader_question_area": "prompt, tool and configuration fairness",
          "first_article": "C/articles/fair-ai-model-comparisons.md",
          "second_article": "B/articles/prompt-tool-and-configuration-fairness.md",
          "judgement": "C",
          "reason": "C correctly refuses to equate nominal settings with internal compute; B\u2019s manifest is useful after that repair.",
          "changed_from_forward_pass": false
        },
        {
          "reader_question_area": "latency, usage and cost",
          "first_article": "C/articles/ai-latency-cost-per-successful-task.md",
          "second_article": "B/articles/latency-token-usage-and-cost.md",
          "judgement": "C",
          "reason": "C has safer usage accounting, explicit cache conditions and zero-success handling. B\u2019s actual rates are mostly verified but budget guidance needs material repair.",
          "changed_from_forward_pass": false
        },
        {
          "reader_question_area": "agent/tool-use evaluation",
          "first_article": "C/articles/evaluate-ai-agents-tools-outcomes.md",
          "second_article": "B/articles/evaluating-agents-and-tool-use.md",
          "judgement": "C",
          "reason": "C provides a precise lost-acknowledgement recovery case and permission predicates. B\u2019s benchmark survey and reliability notation add context.",
          "changed_from_forward_pass": false
        },
        {
          "reader_question_area": "interpretation and maintenance",
          "first_article": "C/articles/maintain-llm-evaluation-suite.md",
          "second_article": "B/articles/interpreting-results-and-maintaining-evals.md",
          "judgement": "C",
          "reason": "C\u2019s change triage and incident-sampling limits are more operationally precise; B\u2019s memo and maturity model are useful.",
          "changed_from_forward_pass": false
        }
      ],
      "reversal_changes_judgement": false,
      "score_changes_made": false
    },
    {
      "direction": "B\u2192D",
      "pass": "initial_comparison",
      "presentation_order": [
        "B",
        "D"
      ],
      "overall_judgement": "D",
      "rationale": "D has fewer consequential repairs and more restrained guidance overall. B is stronger on public-source breadth and explicit statistics; those strengths do not remove the budgeting blocker.",
      "overlapping_reader_questions": [
        {
          "reader_question_area": "decision framework and model/system boundary",
          "first_article": "B/articles/evaluating-ai-models-and-agent-systems.md",
          "second_article": "D/articles/choosing-model-benchmark.md",
          "judgement": "D",
          "reason": "D is clearer and less categorical about the evidence needed for a decision. B has a stronger research catalogue and numeric planning example.",
          "changed_from_forward_pass": null
        },
        {
          "reader_question_area": "public versus workload evidence",
          "first_article": "B/articles/public-benchmarks-vs-workload-evaluation.md",
          "second_article": "D/articles/public-benchmark-literacy.md",
          "judgement": "B",
          "reason": "B\u2019s wider primary-source context and quantitative history outweigh its minor venue label repair for this reader question. D has a wrong author label.",
          "changed_from_forward_pass": null
        },
        {
          "reader_question_area": "task and dataset design",
          "first_article": "B/articles/designing-evaluation-tasks-and-datasets.md",
          "second_article": "D/articles/build-task-eval.md",
          "judgement": "D",
          "reason": "D\u2019s sample-size and synthetic-data caveats are more defensible. B\u2019s reusable schema is a practical advantage but its absolute restrictions need editing.",
          "changed_from_forward_pass": null
        },
        {
          "reader_question_area": "holdouts, contamination and leakage",
          "first_article": "B/articles/contamination-leakage-and-holdouts.md",
          "second_article": "D/articles/datasets-holdouts-contamination.md",
          "judgement": "D",
          "reason": "D\u2019s holdout and freshness guidance is more reliable as written. B\u2019s literature breadth comes with a version-label error and unsupported bound.",
          "changed_from_forward_pass": null
        },
        {
          "reader_question_area": "objective metrics and rubrics",
          "first_article": "B/articles/objective-metrics-and-rubrics.md",
          "second_article": "D/articles/metrics-rubrics.md",
          "judgement": "D",
          "reason": "D\u2019s ordinal, abstention and weighting qualifications are more precise. B adds pass@k, but its F1/traffic shorthand needs repair.",
          "changed_from_forward_pass": null
        },
        {
          "reader_question_area": "human review and model judges",
          "first_article": "B/articles/human-evaluation-and-model-judges.md",
          "second_article": "D/articles/human-evaluation-model-judges.md",
          "judgement": "tie",
          "reason": "B supplies kappa and a fresh validation protocol; D is more restrained about mitigation guarantees and offers a hybrid review pattern. Neither has a decisive overall advantage.",
          "changed_from_forward_pass": null
        },
        {
          "reader_question_area": "repeatability, variation and uncertainty",
          "first_article": "B/articles/repeatability-variance-and-uncertainty.md",
          "second_article": "D/articles/repeatability-uncertainty.md",
          "judgement": "B",
          "reason": "B\u2019s equations, power calculation and report table provide greater usable statistical depth than D\u2019s conceptual protocol.",
          "changed_from_forward_pass": null
        },
        {
          "reader_question_area": "prompt, tool and configuration fairness",
          "first_article": "B/articles/prompt-tool-and-configuration-fairness.md",
          "second_article": "D/articles/fair-model-comparisons.md",
          "judgement": "D",
          "reason": "D handles asymmetric controls without presenting nominal effort as a compute match. B adds a reusable manifest but requires qualification.",
          "changed_from_forward_pass": null
        },
        {
          "reader_question_area": "latency, usage and cost",
          "first_article": "B/articles/latency-token-usage-and-cost.md",
          "second_article": "D/articles/latency-cost.md",
          "judgement": "D",
          "reason": "D\u2019s hypothetical labour-cost example is useful and avoids a stale fixed-rate table. B\u2019s omitted long-context tier and output definition block budgeting use.",
          "changed_from_forward_pass": null
        },
        {
          "reader_question_area": "agent/tool-use evaluation",
          "first_article": "B/articles/evaluating-agents-and-tool-use.md",
          "second_article": "D/articles/agent-system-evaluation.md",
          "judgement": "tie",
          "reason": "B provides benchmark/reliability depth and D a pinned coding environment and trace method. Both need more complete local failure-injection artefacts.",
          "changed_from_forward_pass": null
        },
        {
          "reader_question_area": "interpretation and maintenance",
          "first_article": "B/articles/interpreting-results-and-maintaining-evals.md",
          "second_article": "D/articles/maintaining-evaluations.md",
          "judgement": "tie",
          "reason": "B\u2019s memo/maturity model and D\u2019s layered cadence/version bridge serve different parts of the maintenance question. Both are usable after ordinary editing.",
          "changed_from_forward_pass": null
        }
      ],
      "reversal_changes_judgement": null,
      "score_changes_made": false
    },
    {
      "direction": "D\u2192B",
      "pass": "reversed_presentation_reconsideration",
      "presentation_order": [
        "D",
        "B"
      ],
      "overall_judgement": "D",
      "rationale": "Starting with D makes its fewer material corrections visible; B\u2019s richer calculations still merit credit. Overall preference remains D; no change.",
      "overlapping_reader_questions": [
        {
          "reader_question_area": "decision framework and model/system boundary",
          "first_article": "D/articles/choosing-model-benchmark.md",
          "second_article": "B/articles/evaluating-ai-models-and-agent-systems.md",
          "judgement": "D",
          "reason": "D is clearer and less categorical about the evidence needed for a decision. B has a stronger research catalogue and numeric planning example.",
          "changed_from_forward_pass": false
        },
        {
          "reader_question_area": "public versus workload evidence",
          "first_article": "D/articles/public-benchmark-literacy.md",
          "second_article": "B/articles/public-benchmarks-vs-workload-evaluation.md",
          "judgement": "B",
          "reason": "B\u2019s wider primary-source context and quantitative history outweigh its minor venue label repair for this reader question. D has a wrong author label.",
          "changed_from_forward_pass": false
        },
        {
          "reader_question_area": "task and dataset design",
          "first_article": "D/articles/build-task-eval.md",
          "second_article": "B/articles/designing-evaluation-tasks-and-datasets.md",
          "judgement": "D",
          "reason": "D\u2019s sample-size and synthetic-data caveats are more defensible. B\u2019s reusable schema is a practical advantage but its absolute restrictions need editing.",
          "changed_from_forward_pass": false
        },
        {
          "reader_question_area": "holdouts, contamination and leakage",
          "first_article": "D/articles/datasets-holdouts-contamination.md",
          "second_article": "B/articles/contamination-leakage-and-holdouts.md",
          "judgement": "D",
          "reason": "D\u2019s holdout and freshness guidance is more reliable as written. B\u2019s literature breadth comes with a version-label error and unsupported bound.",
          "changed_from_forward_pass": false
        },
        {
          "reader_question_area": "objective metrics and rubrics",
          "first_article": "D/articles/metrics-rubrics.md",
          "second_article": "B/articles/objective-metrics-and-rubrics.md",
          "judgement": "D",
          "reason": "D\u2019s ordinal, abstention and weighting qualifications are more precise. B adds pass@k, but its F1/traffic shorthand needs repair.",
          "changed_from_forward_pass": false
        },
        {
          "reader_question_area": "human review and model judges",
          "first_article": "D/articles/human-evaluation-model-judges.md",
          "second_article": "B/articles/human-evaluation-and-model-judges.md",
          "judgement": "tie",
          "reason": "B supplies kappa and a fresh validation protocol; D is more restrained about mitigation guarantees and offers a hybrid review pattern. Neither has a decisive overall advantage.",
          "changed_from_forward_pass": false
        },
        {
          "reader_question_area": "repeatability, variation and uncertainty",
          "first_article": "D/articles/repeatability-uncertainty.md",
          "second_article": "B/articles/repeatability-variance-and-uncertainty.md",
          "judgement": "B",
          "reason": "B\u2019s equations, power calculation and report table provide greater usable statistical depth than D\u2019s conceptual protocol.",
          "changed_from_forward_pass": false
        },
        {
          "reader_question_area": "prompt, tool and configuration fairness",
          "first_article": "D/articles/fair-model-comparisons.md",
          "second_article": "B/articles/prompt-tool-and-configuration-fairness.md",
          "judgement": "D",
          "reason": "D handles asymmetric controls without presenting nominal effort as a compute match. B adds a reusable manifest but requires qualification.",
          "changed_from_forward_pass": false
        },
        {
          "reader_question_area": "latency, usage and cost",
          "first_article": "D/articles/latency-cost.md",
          "second_article": "B/articles/latency-token-usage-and-cost.md",
          "judgement": "D",
          "reason": "D\u2019s hypothetical labour-cost example is useful and avoids a stale fixed-rate table. B\u2019s omitted long-context tier and output definition block budgeting use.",
          "changed_from_forward_pass": false
        },
        {
          "reader_question_area": "agent/tool-use evaluation",
          "first_article": "D/articles/agent-system-evaluation.md",
          "second_article": "B/articles/evaluating-agents-and-tool-use.md",
          "judgement": "tie",
          "reason": "B provides benchmark/reliability depth and D a pinned coding environment and trace method. Both need more complete local failure-injection artefacts.",
          "changed_from_forward_pass": false
        },
        {
          "reader_question_area": "interpretation and maintenance",
          "first_article": "D/articles/maintaining-evaluations.md",
          "second_article": "B/articles/interpreting-results-and-maintaining-evals.md",
          "judgement": "tie",
          "reason": "B\u2019s memo/maturity model and D\u2019s layered cadence/version bridge serve different parts of the maintenance question. Both are usable after ordinary editing.",
          "changed_from_forward_pass": false
        }
      ],
      "reversal_changes_judgement": false,
      "score_changes_made": false
    },
    {
      "direction": "C\u2192D",
      "pass": "initial_comparison",
      "presentation_order": [
        "C",
        "D"
      ],
      "overall_judgement": "C",
      "rationale": "C\u2019s worked metrics, intervals and failure recovery give it a clear advantage over D\u2019s mostly conceptual protocols.",
      "overlapping_reader_questions": [
        {
          "reader_question_area": "decision framework and model/system boundary",
          "first_article": "C/articles/ai-model-benchmarking-guide.md",
          "second_article": "D/articles/choosing-model-benchmark.md",
          "judgement": "C",
          "reason": "C\u2019s six artefacts, bounded assistant and review triggers make the first evaluation easier to reconstruct. D\u2019s invoice thresholds are helpful but remain dispersed.",
          "changed_from_forward_pass": null
        },
        {
          "reader_question_area": "public versus workload evidence",
          "first_article": "C/articles/read-ai-benchmark-leaderboards.md",
          "second_article": "D/articles/public-benchmark-literacy.md",
          "judgement": "C",
          "reason": "C provides a coherent evidence-card audit and correct bibliographic associations. D\u2019s checklist is sound but the position-bias author needs repair.",
          "changed_from_forward_pass": null
        },
        {
          "reader_question_area": "task and dataset design",
          "first_article": "C/articles/design-llm-evaluation-tasks.md",
          "second_article": "D/articles/build-task-eval.md",
          "judgement": "tie",
          "reason": "C has a precise observation point and missing-information contract; D a fuller illustrative ticket suite. Both give useful case-design methods.",
          "changed_from_forward_pass": null
        },
        {
          "reader_question_area": "holdouts, contamination and leakage",
          "first_article": "C/articles/evaluation-dataset-leakage-holdouts.md",
          "second_article": "D/articles/datasets-holdouts-contamination.md",
          "judgement": "C",
          "reason": "C connects provenance and split rules to correctly bounded detection methods. D\u2019s freshness discussion is useful but sources and overlap handling are less precise.",
          "changed_from_forward_pass": null
        },
        {
          "reader_question_area": "objective metrics and rubrics",
          "first_article": "C/articles/llm-evaluation-metrics-rubrics.md",
          "second_article": "D/articles/metrics-rubrics.md",
          "judgement": "C",
          "reason": "C calculates the confusion matrix and supplies concrete rubric anchors; D adds useful selective/ordinal cautions without a numerical walkthrough.",
          "changed_from_forward_pass": null
        },
        {
          "reader_question_area": "human review and model judges",
          "first_article": "C/articles/human-reviewers-llm-judges.md",
          "second_article": "D/articles/human-evaluation-model-judges.md",
          "judgement": "C",
          "reason": "C makes calibration/validation separation and automated-pass auditing clearer, with a checked imbalance example.",
          "changed_from_forward_pass": null
        },
        {
          "reader_question_area": "repeatability, variation and uncertainty",
          "first_article": "C/articles/benchmark-uncertainty-repeatability.md",
          "second_article": "D/articles/repeatability-uncertainty.md",
          "judgement": "C",
          "reason": "C supplies an actual Wilson interval and paired resampling procedure; D gives no equivalent calculation.",
          "changed_from_forward_pass": null
        },
        {
          "reader_question_area": "prompt, tool and configuration fairness",
          "first_article": "C/articles/fair-ai-model-comparisons.md",
          "second_article": "D/articles/fair-model-comparisons.md",
          "judgement": "C",
          "reason": "C makes non-equivalent settings, compute limits and format sensitivity explicit and sourced. D is sound but more general.",
          "changed_from_forward_pass": null
        },
        {
          "reader_question_area": "latency, usage and cost",
          "first_article": "C/articles/ai-latency-cost-per-successful-task.md",
          "second_article": "D/articles/latency-cost.md",
          "judgement": "C",
          "reason": "C handles cache eligibility, zero-success ratios and category double counting explicitly. D contributes a good labour-cost scenario.",
          "changed_from_forward_pass": null
        },
        {
          "reader_question_area": "agent/tool-use evaluation",
          "first_article": "C/articles/evaluate-ai-agents-tools-outcomes.md",
          "second_article": "D/articles/agent-system-evaluation.md",
          "judgement": "C",
          "reason": "C\u2019s state predicate and lost-acknowledgement recovery are more specific than D\u2019s pinned coding scenario.",
          "changed_from_forward_pass": null
        },
        {
          "reader_question_area": "interpretation and maintenance",
          "first_article": "C/articles/maintain-llm-evaluation-suite.md",
          "second_article": "D/articles/maintaining-evaluations.md",
          "judgement": "C",
          "reason": "C\u2019s input/output/score triage and incident example provide stronger diagnostic procedure; D\u2019s layered cadence and bridge are useful.",
          "changed_from_forward_pass": null
        }
      ],
      "reversal_changes_judgement": null,
      "score_changes_made": false
    },
    {
      "direction": "D\u2192C",
      "pass": "reversed_presentation_reconsideration",
      "presentation_order": [
        "D",
        "C"
      ],
      "overall_judgement": "C",
      "rationale": "Starting with D gives full credit to ticket sampling and labour economics. C\u2019s more explicit validated methods and recovery checks retain the advantage; no change.",
      "overlapping_reader_questions": [
        {
          "reader_question_area": "decision framework and model/system boundary",
          "first_article": "D/articles/choosing-model-benchmark.md",
          "second_article": "C/articles/ai-model-benchmarking-guide.md",
          "judgement": "C",
          "reason": "C\u2019s six artefacts, bounded assistant and review triggers make the first evaluation easier to reconstruct. D\u2019s invoice thresholds are helpful but remain dispersed.",
          "changed_from_forward_pass": false
        },
        {
          "reader_question_area": "public versus workload evidence",
          "first_article": "D/articles/public-benchmark-literacy.md",
          "second_article": "C/articles/read-ai-benchmark-leaderboards.md",
          "judgement": "C",
          "reason": "C provides a coherent evidence-card audit and correct bibliographic associations. D\u2019s checklist is sound but the position-bias author needs repair.",
          "changed_from_forward_pass": false
        },
        {
          "reader_question_area": "task and dataset design",
          "first_article": "D/articles/build-task-eval.md",
          "second_article": "C/articles/design-llm-evaluation-tasks.md",
          "judgement": "tie",
          "reason": "C has a precise observation point and missing-information contract; D a fuller illustrative ticket suite. Both give useful case-design methods.",
          "changed_from_forward_pass": false
        },
        {
          "reader_question_area": "holdouts, contamination and leakage",
          "first_article": "D/articles/datasets-holdouts-contamination.md",
          "second_article": "C/articles/evaluation-dataset-leakage-holdouts.md",
          "judgement": "C",
          "reason": "C connects provenance and split rules to correctly bounded detection methods. D\u2019s freshness discussion is useful but sources and overlap handling are less precise.",
          "changed_from_forward_pass": false
        },
        {
          "reader_question_area": "objective metrics and rubrics",
          "first_article": "D/articles/metrics-rubrics.md",
          "second_article": "C/articles/llm-evaluation-metrics-rubrics.md",
          "judgement": "C",
          "reason": "C calculates the confusion matrix and supplies concrete rubric anchors; D adds useful selective/ordinal cautions without a numerical walkthrough.",
          "changed_from_forward_pass": false
        },
        {
          "reader_question_area": "human review and model judges",
          "first_article": "D/articles/human-evaluation-model-judges.md",
          "second_article": "C/articles/human-reviewers-llm-judges.md",
          "judgement": "C",
          "reason": "C makes calibration/validation separation and automated-pass auditing clearer, with a checked imbalance example.",
          "changed_from_forward_pass": false
        },
        {
          "reader_question_area": "repeatability, variation and uncertainty",
          "first_article": "D/articles/repeatability-uncertainty.md",
          "second_article": "C/articles/benchmark-uncertainty-repeatability.md",
          "judgement": "C",
          "reason": "C supplies an actual Wilson interval and paired resampling procedure; D gives no equivalent calculation.",
          "changed_from_forward_pass": false
        },
        {
          "reader_question_area": "prompt, tool and configuration fairness",
          "first_article": "D/articles/fair-model-comparisons.md",
          "second_article": "C/articles/fair-ai-model-comparisons.md",
          "judgement": "C",
          "reason": "C makes non-equivalent settings, compute limits and format sensitivity explicit and sourced. D is sound but more general.",
          "changed_from_forward_pass": false
        },
        {
          "reader_question_area": "latency, usage and cost",
          "first_article": "D/articles/latency-cost.md",
          "second_article": "C/articles/ai-latency-cost-per-successful-task.md",
          "judgement": "C",
          "reason": "C handles cache eligibility, zero-success ratios and category double counting explicitly. D contributes a good labour-cost scenario.",
          "changed_from_forward_pass": false
        },
        {
          "reader_question_area": "agent/tool-use evaluation",
          "first_article": "D/articles/agent-system-evaluation.md",
          "second_article": "C/articles/evaluate-ai-agents-tools-outcomes.md",
          "judgement": "C",
          "reason": "C\u2019s state predicate and lost-acknowledgement recovery are more specific than D\u2019s pinned coding scenario.",
          "changed_from_forward_pass": false
        },
        {
          "reader_question_area": "interpretation and maintenance",
          "first_article": "D/articles/maintaining-evaluations.md",
          "second_article": "C/articles/maintain-llm-evaluation-suite.md",
          "judgement": "C",
          "reason": "C\u2019s input/output/score triage and incident example provide stronger diagnostic procedure; D\u2019s layered cadence and bridge are useful.",
          "changed_from_forward_pass": false
        }
      ],
      "reversal_changes_judgement": false,
      "score_changes_made": false
    }
  ],
  "reversal_summary": {
    "pairs_reconsidered": 6,
    "changed_overall_judgements": 0,
    "changed_question_judgements": 0,
    "meaning": "No change was judged necessary after reversed emphasis. This does not prove absence of order, style or prior-reading bias."
  },
  "scores_adjusted_after_freeze": false
}