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      "literal_claim": "Theorem 3.1 (i.i.d. noise) and Theorem 4.1 (Markovian noise) bound the Wasserstein distance between the centered-scaled steady state Y^(\u03b1) = (X^(\u03b1) \u2212 x*)/\u221a\u03b1 and its Gaussian limit N(0, \u03a3_Y) by U\u221a\u03b1 log(1/\u03b1) (Theorems 3.1 and 4.1)."
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            "w1_gibbs": 0.0002049821363439059,
            "w1_grid_abs_difference": 5.592173815101916e-07
          },
          {
            "E_absX": 0.17283678787603213,
            "E_absX_over_correct_scaling": 0.5813513413956883,
            "E_absX_over_sqrt_alpha": 1.9554250359302907,
            "alpha": 0.0078125,
            "evaluations": [
              {
                "E_absY": 0.5813505619309431,
                "fixed_point_iterations": 10000,
                "fixed_point_residual": 6.579456434132637e-14,
                "grid": 2048,
                "kurtosis": 2.1892310809783737,
                "variance": 0.47802035786817065,
                "w1_best_variance_gaussian": 0.059409109274097954,
                "w1_gibbs": 7.241863798520748e-05
              },
              {
                "E_absY": 0.5813513413956883,
                "fixed_point_iterations": 8400,
                "fixed_point_residual": 5.833032950219673e-14,
                "grid": 4096,
                "kurtosis": 2.189231926559346,
                "variance": 0.47802021296561475,
                "w1_best_variance_gaussian": 0.059409205298102244,
                "w1_gibbs": 7.252403897106758e-05
              }
            ],
            "inverse_alpha": 128,
            "kurtosis": 2.189231926559346,
            "scaling_alpha_1_over_h": 0.29730177875068026,
            "w1_best_variance_gaussian": 0.059409205298102244,
            "w1_gibbs": 7.252403897106758e-05,
            "w1_grid_abs_difference": 1.0540098586010304e-07
          },
          {
            "E_absX": 0.1453406043093006,
            "E_absX_over_correct_scaling": 0.5813624172372024,
            "E_absX_over_sqrt_alpha": 2.3254496689488096,
            "alpha": 0.00390625,
            "evaluations": [
              {
                "E_absY": 0.5813601504287552,
                "fixed_point_iterations": 18400,
                "fixed_point_residual": 8.265245180870104e-14,
                "grid": 2048,
                "kurtosis": 2.1887124869422143,
                "variance": 0.4779977411677699,
                "w1_best_variance_gaussian": 0.059458613839746564,
                "w1_gibbs": 2.5410003114949005e-05
              },
              {
                "E_absY": 0.5813624172372024,
                "fixed_point_iterations": 19200,
                "fixed_point_residual": 8.774803788212039e-14,
                "grid": 4096,
                "kurtosis": 2.188718987637267,
                "variance": 0.47800033050266666,
                "w1_best_variance_gaussian": 0.05945846262993744,
                "w1_gibbs": 2.5500254963784185e-05
              }
            ],
            "inverse_alpha": 256,
            "kurtosis": 2.188718987637267,
            "scaling_alpha_1_over_h": 0.25,
            "w1_best_variance_gaussian": 0.05945846262993744,
            "w1_gibbs": 2.5500254963784185e-05,
            "w1_grid_abs_difference": 9.025184883518003e-08
          }
        ],
        "sqrt_scaling_spread": 2.0012785293943227,
        "target": {
          "E_abs": 0.5813683170191186,
          "b": 0.5,
          "density": "proportional to exp(-y^4/2)",
          "kurtosis": 2.1884396152264776,
          "variance": 0.4779887974861249
        },
        "w1_gibbs_fit": {
          "intercept": -2.2321106246001516,
          "r2": 0.9999979366326253,
          "slope": 1.504864832767487
        }
      },
      "headline": "For paper-native P_2(x)=x^4/4, E|X| slope 0.249787 (theory 0.25, R2=0.999999785); at the smallest alpha Gibbs W1 is 2.5500255e-05 versus 0.05945846 for the variance-matched Gaussian.",
      "literal_claim": "Proposition 5.1 shows a different convergence rate of order \u03b1^{1/h} for constant-stepsize SA applied to general convex objectives with Gibbs-type limiting distributions (Proposition 5.1, Section 5)."
    }
  ],
  "methods": {
    "determinism": "exact arithmetic/FFT/transfer operator plus fixed two-seed Markov nonlinear check",
    "runtime_dependency_versions": {
      "numpy": "2.2.4"
    },
    "stepsizes": [
      0.125,
      0.0625,
      0.03125,
      0.015625,
      0.0078125,
      0.00390625,
      0.001953125
    ]
  },
  "paper_id": "m4TAzup6Yc",
  "summary": {
    "all_claims_directly_executed": true,
    "expected_points": 12,
    "falsified": 0,
    "registered_claims": 6,
    "verified": 6
  }
}
