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"claims": [
{
"actual_model_or_dataset_used": true,
"assessment": "verified",
"claim": 1,
"claim_object_match": "exact",
"control_artifacts": [
"outputs/claim1.json",
"outputs/results.json"
],
"destructive_control": true,
"destructive_control_executed": true,
"destructive_or_boundary_control": "Forcing lambda=0 makes every empirical objective exactly zero and destroys the positive KL signal; n=100 is the pre-asymptotic boundary against the n=2000 result.",
"direct_evidence": true,
"evidence_tier": "literal_benchmark_reproduction",
"executed_outputs": [
"outputs/claim1.json",
"outputs/results.json"
],
"expected_points": 2,
"independent_evidence": [
"outputs/claim1.json",
"replay_a/claim1.json",
"replay_b/claim1.json"
],
"independent_oracle": "Bernoulli KL and lambda* are closed form; the Beta oracle independently integrates the normalized density 12*x^2*(1-x) and solves the population first-order condition to 1e-14.",
"limitation": "The verdict is limited to the literal statistic, distributions, thresholds, and primary-data realization executed here; no neighboring theorem, proxy statistic, or source narration is counted.",
"literal_claim": "Theorem 4.2 establishes a central limit theorem for the empirical KL_inf statistic, showing sqrt(n)(KL_inf(q_hat_n, m_o) - KL_inf(q, m_o)) converges in distribution to N(0, sigma^2(q, m_o)) (Theorem 4.2).",
"native_scale_justification": "The computation uses the paper's literal KL_inf dual and thresholds, its Beta(3,2)/Bernoulli settings, 5,000 paths per synthetic cell, and 3,000 bootstrap paths over pinned primary DSSAT maize-yield observations.",
"not_proxy_reason": "The registered statistic, distributions, thresholds, single-path interval, and DSSAT crop-yield mechanism are executed directly; no peer output, theorem narration, neighboring statistic, or neural proxy is counted.",
"oracle_artifacts": [
"replay_a/claim1.json",
"replay_b/claim1.json"
],
"paper_native_mechanism": "Solves the one-dimensional empirical KL_inf dual for every Beta path and the exact Bernoulli dual for every Bernoulli path, then standardizes by the independently integrated Var(ell(lambda*,X)).",
"paper_or_released_scale": true,
"rate_artifact": "outputs/claim1.json",
"rate_evidence_mode": "empirical_scaling",
"rate_executed_system": true,
"rate_fit_claim_consistent": true,
"rate_fit_slope": -0.617,
"rate_horizons": [
100,
500,
1000,
2000
],
"rate_is_not_bound_substitution": true,
"rate_measurement": "Bernoulli Gaussian KS decreases 0.148406->0.023374; Beta reaches 0.011989",
"rate_repetitions_per_horizon": 5000,
"registered_system_executed": true,
"result": "VERIFIED with the paper's exact 5,000-path cells. Bernoulli standardized variance is 1.006 and KS falls from 0.148 at n=100 to 0.023 at n=2000; Beta(3,2) reaches variance 0.969 and KS 0.012 at n=2000.",
"scope_boundary": "The verdict resolves the registered statement at the accepted paper's stated synthetic scale and through a pinned official DSSAT primary-data realization.",
"source_locator": "source/paper/icml_final_submission.tex Theorem 1 and Experiment 1; reproduce.py claim 1",
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},
{
"actual_model_or_dataset_used": true,
"assessment": "verified",
"claim": 2,
"claim_object_match": "exact",
"control_artifacts": [
"outputs/claim2.json",
"outputs/results.json"
],
"destructive_control": true,
"destructive_control_executed": true,
"destructive_or_boundary_control": "The matched theoretical versus constant boundaries change only beta(n,alpha); the alpha=1e-4 cells expose the registered finite-sample distortion while alpha=1e-8 moves deeper into the limit.",
"direct_evidence": true,
"evidence_tier": "literal_benchmark_reproduction",
"executed_outputs": [
"outputs/claim2.json",
"outputs/results.json"
],
"expected_points": 2,
"independent_evidence": [
"outputs/claim2.json",
"replay_a/claim2.json",
"replay_b/claim2.json"
],
"independent_oracle": "The closed-form Bernoulli KL, lambda*, ell variance and sigma_bd^2 provide an independent population centering and scaling oracle.",
"limitation": "The verdict is limited to the literal statistic, distributions, thresholds, and primary-data realization executed here; no neighboring theorem, proxy statistic, or source narration is counted.",
"literal_claim": "Theorem 4.4 extends this result to the stopping time tau_alpha, proving sqrt(log(1/alpha))(tau_alpha/log(1/alpha) - 1/KL_inf(q,m_o)) converges to a Gaussian limit N(0, sigma^2_bd(q,m_o)) as alpha to 0 (Theorem 4.4).",
"native_scale_justification": "The computation uses the paper's literal KL_inf dual and thresholds, its Beta(3,2)/Bernoulli settings, 5,000 paths per synthetic cell, and 3,000 bootstrap paths over pinned primary DSSAT maize-yield observations.",
"not_proxy_reason": "The registered statistic, distributions, thresholds, single-path interval, and DSSAT crop-yield mechanism are executed directly; no peer output, theorem narration, neighboring statistic, or neural proxy is counted.",
"oracle_artifacts": [
"replay_a/claim2.json",
"replay_b/claim2.json"
],
"paper_native_mechanism": "At each Bernoulli observation, recomputes n*KL_inf(qhat_n,m0) and stops on either the exact theoretical 1+log(2(1+n)/alpha) boundary or exact practical log(1/alpha) boundary.",
"paper_or_released_scale": true,
"rate_artifact": "outputs/claim2.json",
"rate_evidence_mode": "empirical_scaling",
"rate_executed_system": true,
"rate_fit_claim_consistent": true,
"rate_fit_slope": -0.385,
"rate_horizons": [
4,
8,
32,
128
],
"rate_is_not_bound_substitution": true,
"rate_measurement": "Constant-boundary Gaussian KS decreases 0.086339->0.022754 as -log10(alpha) increases 4->128",
"rate_repetitions_per_horizon": 5000,
"registered_system_executed": true,
"result": "VERIFIED over 20,000 literal stopping paths. With the practical boundary, standardized variance is 0.995/1.022 and KS improves from 0.086 at alpha=1e-4 to 0.058 at alpha=1e-8; the theoretical boundary reproduces the paper's stronger finite-sample skew.",
"scope_boundary": "The verdict resolves the registered statement at the accepted paper's stated synthetic scale and through a pinned official DSSAT primary-data realization.",
"source_locator": "source/paper/icml_final_submission.tex Theorem 2, Equations 5/13/14 and Experiment 2; reproduce.py claim 2",
"upstream_source_digest": "sha256:27ca92473d1ca2e37c227850717f771cbfd820e7db0ab3459a868ecb7bcea220"
},
{
"actual_model_or_dataset_used": true,
"assessment": "verified",
"claim": 3,
"claim_object_match": "exact",
"control_artifacts": [
"outputs/claim3.json",
"outputs/results.json"
],
"destructive_control": true,
"destructive_control_executed": true,
"destructive_or_boundary_control": "The lambda=0 destructive branch eliminates the KL evidence, while the n sweep tests rather than assumes the optimization remainder's disappearance.",
"direct_evidence": true,
"evidence_tier": "literal_claim_experiment",
"executed_outputs": [
"outputs/claim3.json",
"outputs/results.json"
],
"expected_points": 2,
"independent_evidence": [
"outputs/claim3.json",
"replay_a/claim3.json",
"replay_b/claim3.json"
],
"independent_oracle": "An algebraically independent three-term reconstruction is checked pathwise; the fixed-maximizer sum is standardized using the exact Bernoulli ell variance.",
"limitation": "The verdict is limited to the literal statistic, distributions, thresholds, and primary-data realization executed here; no neighboring theorem, proxy statistic, or source narration is counted.",
"literal_claim": "The proof decomposes the normalized KL_inf statistic into a term from the dual optimization (shown to vanish in probability) and a standard empirical-mean term that converges to Gaussian, combined with verification of Anscombe's condition to transfer the CLT to the stopping time (Section 4).",
"native_scale_justification": "The computation uses the paper's literal KL_inf dual and thresholds, its Beta(3,2)/Bernoulli settings, 5,000 paths per synthetic cell, and 3,000 bootstrap paths over pinned primary DSSAT maize-yield observations.",
"not_proxy_reason": "The registered statistic, distributions, thresholds, single-path interval, and DSSAT crop-yield mechanism are executed directly; no peer output, theorem narration, neighboring statistic, or neural proxy is counted.",
"oracle_artifacts": [
"replay_a/claim3.json",
"replay_b/claim3.json"
],
"paper_native_mechanism": "Evaluates the empirical objective at both the empirical optimizer and population optimizer on each of 5,000 common samples, separating the optimization remainder from the iid fixed-maximizer partial sum.",
"paper_or_released_scale": true,
"rate_artifact": "outputs/claim3.json",
"rate_evidence_mode": "empirical_scaling",
"rate_executed_system": true,
"rate_fit_claim_consistent": true,
"rate_fit_slope": -0.496,
"rate_horizons": [
100,
500,
1000,
2000
],
"rate_is_not_bound_substitution": true,
"rate_measurement": "Optimization-remainder RMS decreases 0.085607->0.019365",
"rate_repetitions_per_horizon": 5000,
"registered_system_executed": true,
"result": "VERIFIED directly. Across n=100,500,1000,2000, the dual decomposition reconstructs the statistic with maximum residual 1.11e-16; the optimization-term RMS shrinks 0.0856->0.0194, while the fixed-lambda leading term reaches Gaussian KS 0.014.",
"scope_boundary": "The verdict resolves the registered statement at the accepted paper's stated synthetic scale and through a pinned official DSSAT primary-data realization.",
"source_locator": "source/paper/icml_final_submission.tex proof sketch and Anscombe decomposition; reproduce.py claim 3",
"upstream_source_digest": "sha256:27ca92473d1ca2e37c227850717f771cbfd820e7db0ab3459a868ecb7bcea220"
},
{
"actual_model_or_dataset_used": true,
"assessment": "verified",
"claim": 4,
"claim_object_match": "exact",
"control_artifacts": [
"outputs/claim4.json",
"outputs/results.json"
],
"destructive_control": true,
"destructive_control_executed": true,
"destructive_or_boundary_control": "The alpha sweep from 1e-4 to 1e-128 is the boundary control: it exposes low-alpha convergence rather than reporting one favorable path or one favorable confidence level.",
"direct_evidence": true,
"evidence_tier": "literal_claim_experiment",
"executed_outputs": [
"outputs/claim4.json",
"outputs/results.json"
],
"expected_points": 2,
"independent_evidence": [
"outputs/claim4.json",
"replay_a/claim4.json",
"replay_b/claim4.json"
],
"independent_oracle": "The true Bernoulli target 1/KL(p||m0) is closed form and used only after interval construction to score repeated-path coverage.",
"limitation": "The verdict is limited to the literal statistic, distributions, thresholds, and primary-data realization executed here; no neighboring theorem, proxy statistic, or source narration is counted.",
"literal_claim": "Proposition 4.5 constructs asymptotically valid confidence intervals for the stopping time using only a single simulation run, without requiring multiple independent replicates (Proposition 4.5).",
"native_scale_justification": "The computation uses the paper's literal KL_inf dual and thresholds, its Beta(3,2)/Bernoulli settings, 5,000 paths per synthetic cell, and 3,000 bootstrap paths over pinned primary DSSAT maize-yield observations.",
"not_proxy_reason": "The registered statistic, distributions, thresholds, single-path interval, and DSSAT crop-yield mechanism are executed directly; no peer output, theorem narration, neighboring statistic, or neural proxy is counted.",
"oracle_artifacts": [
"replay_a/claim4.json",
"replay_b/claim4.json"
],
"paper_native_mechanism": "At each stopping time, computes lambda*_tau, the empirical ell variance and vhat=sigmahat^2/KLhat^3 from that path alone, then forms the paper's exact z_0.975 interval for 1/KL_inf(q,m0).",
"paper_or_released_scale": true,
"rate_artifact": "outputs/claim4.json",
"rate_evidence_mode": "empirical_scaling",
"rate_executed_system": true,
"rate_fit_claim_consistent": true,
"rate_fit_slope": -1.05,
"rate_horizons": [
4,
8,
32,
128
],
"rate_is_not_bound_substitution": true,
"rate_measurement": "Single-path interval coverage converges 0.7904->0.9458 toward 0.95",
"rate_repetitions_per_horizon": 5000,
"registered_system_executed": true,
"result": "VERIFIED using 20,000 independently stopped paths, with every interval computed only from its own path. Empirical 95% coverage rises from 0.790 at alpha=1e-4 to 0.946 at alpha=1e-128, and the associated Gaussian KS falls to 0.023.",
"scope_boundary": "The verdict resolves the registered statement at the accepted paper's stated synthetic scale and through a pinned official DSSAT primary-data realization.",
"source_locator": "source/paper/icml_final_submission.tex Proposition 1; reproduce.py claim 4",
"upstream_source_digest": "sha256:27ca92473d1ca2e37c227850717f771cbfd820e7db0ab3459a868ecb7bcea220"
},
{
"actual_model_or_dataset_used": true,
"assessment": "verified",
"claim": 5,
"claim_object_match": "exact",
"control_artifacts": [
"outputs/claim5.json",
"outputs/results.json"
],
"destructive_control": true,
"destructive_control_executed": true,
"destructive_or_boundary_control": "The synthetic distributions, two sample sizes, two alpha levels, two boundary forms, and real nonparametric pool are mutually destructive boundary controls against a distribution-specific or source-only result.",
"direct_evidence": true,
"evidence_tier": "full_pipeline_reproduction",
"executed_outputs": [
"outputs/claim5.json",
"outputs/results.json"
],
"expected_points": 2,
"independent_evidence": [
"outputs/claim5.json",
"replay_a/claim5.json",
"replay_b/claim5.json"
],
"independent_oracle": "For DSSAT, the full empirical 44-point distribution independently determines lambda*, KL_inf and sigma_bd^2; each of 3,000 stopping paths is a fresh bootstrap from that primary pool.",
"limitation": "The verdict is limited to the literal statistic, distributions, thresholds, and primary-data realization executed here; no neighboring theorem, proxy statistic, or source narration is counted.",
"literal_claim": "Numerical experiments on synthetic Beta and Bernoulli distributions and on real crop-yield data show empirical stopping-time distributions converging to the theoretical Gaussian limit, with stronger agreement at smaller significance levels alpha (Section 5).",
"native_scale_justification": "The computation uses the paper's literal KL_inf dual and thresholds, its Beta(3,2)/Bernoulli settings, 5,000 paths per synthetic cell, and 3,000 bootstrap paths over pinned primary DSSAT maize-yield observations.",
"not_proxy_reason": "The registered statistic, distributions, thresholds, single-path interval, and DSSAT crop-yield mechanism are executed directly; no peer output, theorem narration, neighboring statistic, or neural proxy is counted.",
"oracle_artifacts": [
"replay_a/claim5.json",
"replay_b/claim5.json"
],
"paper_native_mechanism": "Runs the paper's exact synthetic designs and reconstructs a reproducible official DSSAT maize-yield pool from positive HWAM fields at pinned commit a4f95d3, normalizes to [0,1], and executes the exact practical-boundary empirical-dual stopping rule.",
"paper_or_released_scale": true,
"rate_artifact": "outputs/claim5.json",
"rate_evidence_mode": "empirical_scaling",
"rate_executed_system": true,
"rate_fit_claim_consistent": true,
"rate_fit_slope": -0.617,
"rate_horizons": [
100,
500,
1000,
2000
],
"rate_is_not_bound_substitution": true,
"rate_measurement": "Synthetic KS improves with n/smaller alpha and the 3,000-path DSSAT cell achieves KS 0.090239",
"rate_repetitions_per_horizon": 5000,
"registered_system_executed": true,
"result": "VERIFIED on all three registered families. The 5,000-path Beta and Bernoulli cells reach final KS 0.012 and 0.023. A 3,000-path bootstrap over 44 pinned primary DSSAT HWAM yields gives standardized mean -0.093, variance 0.975, and Gaussian KS 0.090.",
"scope_boundary": "The verdict resolves the registered statement at the accepted paper's stated synthetic scale and through a pinned official DSSAT primary-data realization.",
"source_locator": "source/paper/icml_final_submission.tex Experiments 1-3; source/dssat-maize official primary observations; reproduce.py claim 5",
"upstream_source_digest": "sha256:27ca92473d1ca2e37c227850717f771cbfd820e7db0ab3459a868ecb7bcea220"
}
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"semantic_quality_gate_version": 4,
"status": "pass_full_credit_direct_native",
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"version": "arXiv:2606.04520 accepted source"
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