# Стенд-проверка (stage-2) · промт оператора:

(универсальный)

Ответ агента:

{
  "worked": false,
  "confidence": 10,
  "verdict": "The Lipschitz-inflated conformal tube was implemented and evaluated on the matched dynamics/rnn_small benchmark. Its trained-model mechanism signature was confirmed: the observed radius slope matched predicted Gamma to numerical precision, and diagnostic dense coverage was slightly higher than the constant-radius baseline. However, the tube is inference-time uncertainty post-processing and did not change the independent endpoint MSE: paired delta_mean=0.0 with permutation p=1.0, so there was no benchmark win.",
  "metrics": {
    "baseline": "Best constant-radius baseline at lr=0.01: eight-seed MSE mean 0.009070940839592367, std 0.0043108428117023265.",
    "idea": "Inflated-tube system at lr=0.01: eight-seed MSE mean 0.009070940839592367, std 0.0043108428117023265; paired delta 0.0, permutation p=1.0."
  },
  "bench_report": {
    "bench_version": 1,
    "track": "dynamics",
    "model": "rnn_small",
    "metric_direction": "lower is better",
    "n_seeds": 8,
    "baseline": {
      "best_cfg": {
        "lr": 0.01,
        "radius": "constant"
      },
      "sweep": [
        {
          "cfg": {
            "lr": 0.001,
            "radius": "constant"
          },
          "mean": 0.14777550008147955
        },
        {
          "cfg": {
            "lr": 0.003,
            "radius": "constant"
          },
          "mean": 0.02788990270346403
        },
        {
          "cfg": {
            "lr": 0.01,
            "radius": "constant"
          },
          "mean": 0.010466416599228978
        }
      ],
      "full": {
        "mean": 0.009070940839592367,
        "std": 0.0043108428117023265,
        "per_seed": [
          0.01107354648411274,
          0.007685416843742132,
          0.006489071529358625,
          0.016617631539702415,
          0.014132681302726269,
          0.008140470832586288,
          0.002562329638749361,
          0.005866378545761108
        ],
        "n": 8
      }
    },
    "idea": {
      "mean": 0.009070940839592367,
      "std": 0.0043108428117023265,
      "per_seed": [
        0.01107354648411274,
        0.007685416843742132,
        0.006489071529358625,
        0.016617631539702415,
        0.014132681302726269,
        0.008140470832586288,
        0.002562329638749361,
        0.005866378545761108
      ],
      "n": 8,
      "selected_per_seed": [
        {
          "seed": 0,
          "best_lr": 0.01,
          "metric": 0.01107354648411274
        },
        {
          "seed": 1,
          "best_lr": 0.01,
          "metric": 0.007685416843742132
        },
        {
          "seed": 2,
          "best_lr": 0.01,
          "metric": 0.006489071529358625
        },
        {
          "seed": 3,
          "best_lr": 0.01,
          "metric": 0.016617631539702415
        },
        {
          "seed": 4,
          "best_lr": 0.01,
          "metric": 0.014132681302726269
        },
        {
          "seed": 5,
          "best_lr": 0.01,
          "metric": 0.008140470832586288
        },
        {
          "seed": 6,
          "best_lr": 0.01,
          "metric": 0.002562329638749361
        },
        {
          "seed": 7,
          "best_lr": 0.01,
          "metric": 0.005866378545761108
        }
      ],
      "selected_hyperparameters": [
        {
          "lr": 0.001
        },
        {
          "lr": 0.003
        },
        {
          "lr": 0.01
        }
      ]
    },
    "comparison": {
      "delta_mean": 0.0,
      "idea_wins": 0,
      "n_pairs": 8,
      "per_seed_diffs": [
        0.0,
        0.0,
        0.0,
        0.0,
        0.0,
        0.0,
        0.0,
        0.0
      ],
      "p_value": 1.0,
      "mde": 0.0,
      "mde_rel_pct": 0.0,
      "verdict": "no measurable effect",
      "system_worked": false
    },
    "mechanism_signature": {
      "prediction": "Dense tube radius is affine in nearest-observation distance with slope Gamma.",
      "radius_slope_predicted_mean": 0.23295989949256185,
      "radius_slope_observed_mean": 0.2329598994925619,
      "radius_slope_abs_error_mean": 8.326672684688674e-17,
      "tube_coverage_mean": 0.9995028409090909,
      "constant_radius_coverage_mean": 0.9989109848484848,
      "median_radius_mean": 0.6557790094055236,
      "confirmed": true,
      "trained_model_measurements": true,
      "note": "The fixed dynamics bench predicts a single endpoint, so dense coverage is a diagnostic interpolation signature; endpoint MSE is the independent primary metric."
    },
    "protocol_notes": {
      "paired_seeds": [
        0,
        1,
        2,
        3,
        4,
        5,
        6,
        7
      ],
      "baseline_grid": [
        {
          "lr": 0.001,
          "radius": "constant"
        },
        {
          "lr": 0.003,
          "radius": "constant"
        },
        {
          "lr": 0.01,
          "radius": "constant"
        }
      ],
      "idea_grid": [
        {
          "lr": 0.001,
          "tube": "q+Gamma*delta"
        },
        {
          "lr": 0.003,
          "tube": "q+Gamma*delta"
        },
        {
          "lr": 0.01,
          "tube": "q+Gamma*delta"
        }
      ],
      "epochs": 6,
      "n_train": 500,
      "n_test": 160,
      "structural_match": "Continuous-time trajectory uncertainty and stability maps to the built-in dynamics track."
    }
  },
  "how_to_run": "/home/maxwelhelp/main/bin/python3 bench_lipschitz_dynamics.py",
  "files": [
    "bench_lipschitz_dynamics.py",
    "bench_report.json"
  ],
  "limitations": "The fixed dynamics track predicts one endpoint from an eight-step window rather than a true continuous-time rollout, so dense tube coverage is a diagnostic interpolation signature rather than the primary task metric. No multi-step neural ODE or independently sampled high-frequency calibration split was tested.",
  "system_verdict": "partial",
  "practical_verdict": "no_effect",
  "mechanism_ok": 1,
  "system_judged": true
}